research paper on political campaigns

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The Oxford Handbook of Political Communication

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56 New Media and Political Campaigns

Diana Owen (Ph.D., University of Wisconsin-Madison) is Associate Professor of Political Science and Director of American Studies at Georgetown University, and teaches in the Communication, Culture, and Technology graduate program. She is the author of American Government and Politics in the Information Age with David Paletz and Timothy E. Cook (Flatworld, 2011), New Media and American Politics with Richard Davis (Oxford, 1998) and Media Messages in American Presidential Elections (Greenwood, 1991), and editor of The Internet and Politics: Citizens, Voters, and Activists, with Sarah Oates and Rachel Gibson (Routledge, 2006), and editor of Making a Difference: The Internet and Elections in Comparative Perspective, with Richard Davis, Stephen Ward, and David Taras (Lexington, 2009).

Published: 01 May 2014
This version: January 2018

Updated in this version:

Additional citations and brief discussion.

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New media have been playing an increasingly central role in American elections since they first appeared in 1992. While television remains the main source of election information for a majority of voters, digital communication platforms have become prominent. New media have triggered changes in the campaign strategies of political parties, candidates, and political organizations; reshaped election media coverage; and influenced voter engagement. This chapter examines the stages in the development of new media in elections from the use of rudimentary websites to the rise sophisticated social media. It discusses the ways in which new media differ from traditional media in terms of their form, function, and content; identifies the audiences for new election media; and examines the effects on voter interest, knowledge, engagement, and turnout. Going forward, scholars need to employ creative research methodologies to catalogue and analyze new campaign media as they emerge and develop.

The 1992 presidential election ushered in a new era of campaign media. Candidates turned to entertainment venues to circumvent the mainstream press’s stranglehold on the campaign agenda. This development was marked by the signature moments of businessman Ross Perot launching his third party presidential bid on Larry King Live and Democratic nominee Bill Clinton donning dark shades and playing the saxophone on the Arsenio Hall Show . At the same time, voters became more visibly engaged with campaign media, especially through call-in radio and television programs. Communication researchers speculated about the dawn of a new era of campaign media, alternately praising its populist tendencies and lamenting its degradation of political discourse. These forms of new media primarily made use of traditional print, radio, and television media platforms.

In the years since, new technologies have transformed the campaign media system and in the process altered the ways in which campaigns are waged by candidates, reported on by journalists, and experienced by voters. New campaign media have proliferated and become increasingly prominent with each passing election. Social media platforms that facilitate interaction and collaboration in the production, dissemination, and exchange of content have become campaign mainstays. Candidates employ complex media strategies incorporating an ever-changing menu of innovations in conjunction with traditional media management techniques. Campaign reporting is no longer the exclusive province of professional journalists, as bloggers and average citizens cover events and provide commentary that is widely available. Voters look to new media as primary sources of information and participate actively in campaigns through digital platforms.

The New Media Campaign Environment

A multilayered communication environment exists for election campaigns. The media system is transitioning from a broadcast model associated with traditional media where general-interest news items are disseminated to the mass public through a narrowcasting model where carefully crafted messages target discrete audience segments. On the one hand, the mainstream press maintains an identifiable presence. Much original and investigative campaign reporting is conducted by professional journalists, even as financial pressures have forced the industry to reduce their numbers drastically. Mainstream media still validate information disseminated via new media platforms, such as blogs and Twitter feeds. At the same time, the proliferation of new media has increased the diversification and fragmentation of the communication environment. Media are more politically polarized, as niche sources associated with extreme ideological positions appeal to growing sections of the audience. The abundance of new sources makes it possible for voters to tailor their media consumption to conform to their personal tastes ( Sunstein, 2000 ; Jamieson and Cappella, 2008 ; Stroud, 2011 ; Levendusky, 2013 ).

The evolution of campaign communication in the new media era can be construed as three distinct yet overlapping phases, as depicted in Figure 56.1 .

Old Media, New Politics

During the “old media, new politics” phase, candidates used established nonpolitical and entertainment media to bypass mainstream press gatekeepers, who reduced their messages to eight-second sound bites sandwiched between extensive commentary. Candidates sought to reach voters who were less attentive to print and television news through personal appeals in the media venues they frequented. “Old media, new politics” thrives in the current era, as candidates seek the favorable and widespread coverage they can garner from a cover story in People Weekly and appearances on the talk and comedy show circuit ( Baum, 2005 ). This type of election media laid the foundation for the personalized soft news coverage that permeates twenty-first-century new media campaigns. While rudimentary websites, or “brochureware,”—defined as web versions of traditional print campaign flyers—that served as digital repositories of campaign documents first appeared in 1992 ( Davis, 1999 ), old media technologies remained dominant during this phase.

New Media, New Politics 1.0

The second phase—“new media, new politics 1.0”—witnessed the introduction of novel election communication platforms made possible by technological innovations. By the year 2000 election, all major and many minor candidates had basic websites that were heavily text-based ( Bimber and Davis, 2003 ). Campaign websites incorporating interactive elements—including features that allowed users to engage in discussions, donate to candidates, and volunteer—became standard in the 2004 election. Election-related blogs also proliferated, offering voters an alternative to corporate news products ( Cornfield, 2004 ; Foot and Schneider, 2006 ). Internet use in midterm elections lagged somewhat behind presidential campaign applications. Many congressional candidates had basic websites in 2006, but few included blogs, fundraising tools, or volunteer-building applications ( The Bivings Group, 2006 ).

New Media, New Politics 2.0

The 2008 presidential election marked the beginning of the third phase in the evolution of election media—“new media, new politics 2.0.” This period is distinguished by innovations in digital election communication that facilitate networking, collaboration and community building as well as active engagement. Campaign websites became full-service multimedia platforms where voters could find extensive information about the candidates as well as election logistics, access and share videos and ads, blog, and provide commentary, donate, and take part in volunteer activities. The most notable development in 2008 was the use of social media, such as Facebook, and video sharing sites, like YouTube, for peer-to-peer exchange of election information, campaign organizing, and election participation. Mainstream media organizations kept pace with these developments by incorporating social media and video sharing features into their digital platforms. These new media innovations were amplified in the 2010 midterm elections, with Twitter and microblogging sites featured more prominently in the election media mix, and have continued to evolve in subsequent contests. Another important development is campaigns’ use of “big data”—large, detailed data sets compiled from voter files, social media analytics, and consumer data—to target voters with specific messages based on their preferences. Big data also are employed by campaigns and opinion organizations to make predictions about voter behavior and election outcomes ( Nickerson and Rogers, 2014 ).

The Importance of New Media in Elections

The new media’s influence on elections has been substantial. Campaigns provide a laboratory for the development of political applications that carry over to postelection politics and establish new norms for media politics in subsequent contests. The social media innovations that rose to prominence in the 2008 presidential contest became standard practice in the 2010 midterm elections and set the stage for the more prolific development of political applications for handheld devices than was the case in 2004, when the Bush campaign used handheld devices to show campaign ads door to door. As technology continues to advance and the number of social media platforms proliferates, the election media environment has become more diversified, specialized, and fragmented. Facebook, Twitter, and YouTube have been joined by a host of platforms, such as Reddit, Pinterest, Snapchat, and Vine, that support campaign activities.

Campaign Organizations, Parties, and Grassroots Movements

Candidates have incorporated new media into their organizational strategies for informing, contacting, and mobilizing voters. Candidate websites have come a long way from the days of brochureware and provide users with the opportunity for an individualized experience that can range from simply access biographical information to networking with supporters from across the country. Campaigns have also developed advanced microtargeting methods, including the use of focused text messages to reach specific constituencies, such as ethnic group members and issue constituencies ( Hillygus and Shields, 2008 ; Hendricks and Schill, 2014 ).

The Democratic and Republican parties have developed digital media strategies for enhancing personal outreach to voters. Their websites have become social media hubs that can engage voters during and after elections. The dominant function of the two major parties’ new media strategy is fundraising, and the “donate” button features prominently on all of their platforms. The parties’ outreach to voters continues between elections, especially through the use of regular email and text messages to supporters, which has revitalized parties’ electoral role.

Grassroots political movements have employed new media as a means of getting their message out and mobilizing supporters. In the 2010 midterm elections, the Tea Party movement used websites, blogs, social media, and email to bring national attention to state and local candidates and to promote its antigovernment taxing and spending message ( Lepore, 2010 ). Mainstream and new media coverage of the Tea Party was substantial and resulted in increased public awareness of and momentum behind little-known candidates ( Project for Excellence in Journalism, 2010 ). At the same time, new media strategies can backfire when the mainstream press publicizes unflattering or embarrassing information about candidates. Christine O’Donnell, an unsuccessful Tea Party‒backed candidate for the Senate in Delaware in 2010, received extensive national press coverage for statements about witchcraft she had once made that helped to derail her campaign.

Virtual third-party movements and nonpartisan social media–based platforms for electoral engagement gained traction in the 2012 presidential election. Americans Elect, the most visible and well-financed of these organizations, was unsuccessful in its bid to field a bipartisan presidential ticket through an online nomination process, but managed to get laws passed in more than thirty states that would allow candidates nominated through online processes to get on the ballot, setting the stage for future online presidential candidate recruitment efforts ( Owen, 2015 ).

Campaigns have had to adapt to a more negative and volatile electoral environment. Candidates are subject to constant scrutiny, as their words and actions are closely recorded. Reporters and average citizens can compile information and disseminate it using inexpensive technologies that link easily to networks, where rumors can be spread instantaneously. New media can sustain rumors well after an election. Rumors promulgated by the “birther movement”—that Barack Obama was not qualified to be president because he was not born in the United States—continued to circulate long after he took office.

Media Organizations

The relationship between traditional and new media has gone from adversarial to symbiotic, as new media have become sources of campaign information for professional journalists. Average citizens have become prolific providers of election-related content ranging from short reactions to campaign stories to lengthy firsthand accounts of campaigns events. Mainstream media have integrated new media features into their digital platforms, which have become delivery systems for content that originates from websites, Twitter feeds, blogs, and citizen-produced videos. As a result, messages originating in new media increasingly set the campaign agenda ( Pavlik, 2008 ). Still, established media organizations remain prominent hosts of public election discourse ( Gans, 2010 ).

New media constitute an abundant source of election information for an increasing number of voters. While television remains the main source of election news for a majority of people, online sources are gaining popularity ( Smith, 2011 ). The Internet has gone from a supplementary resource for election information to a main source of news for more than a third of voters during presidential campaigns and a quarter of voters during midterm elections. The use of the Internet as a main source in presidential elections has climbed from 3% in 1996 to 47% in 2012. Mainstream television news exposure and hardcopy print newspaper use has dropped markedly over time. Radio’s popularity as a resource for information on presidential elections has increased slightly since the 1980s and early 1990s, largely due to talk radio’s popularity (Table 56.1 ).

Note : Respondents could volunteer more than one main source. The option changed from “newspaper” to “print” in 2014, and magazines were not included as a source.

The Electorate

The role of the new media in fostering a more active electorate is perhaps their most consequential contribution to campaigns. The low barrier to entry allows more voters from diverse constituencies to participate ( Farrar-Myers and Vaughn, 2015 ). Voters use new media to participate in campaigns in traditional and novel ways, such as producing and distributing campaign content, including news stories, short observations, opinion pieces, audio and video accounts, and independent ads. Citizens can not only access and share information through peer-to-peer networks using email and an ever-increasing array of digital platforms but also engage in structured activities organized digitally by campaign organizations, parties, and interest groups; or they can organize campaign events on their own using social media.

Major Research Questions and Findings

A research tradition begun in the 1992 presidential campaign has addressed both macro-level issues about the importance of new media for democratic participation and also more specific questions about the form, content, role, audiences, and effects of new media in particular campaigns. Since the new media’s influence in elections has been dynamic, research findings should be considered within the context of the phases of new media development. As new media have matured, they have become more integral to the electoral process, and their effects are more pronounced.

Form, Function, and Content of New Election Media

In order to address issues dealing with the form, function, and content of new election media, researchers have asked: What distinguishes new media from traditional media in campaigns? Studies examining the characteristics of new media in elections have provided snapshots of new media developments in specific elections and tracked their evolution over time. Dominant traits that set new media apart from traditional ones are interactivity, network connectivity, and the ability to dynamically engage audience members in elections. New media are also flexible and adaptable, as they can accommodate a wide range of campaign applications. Some, such as fundraising, have offline counterparts, while others, like voter-produced election ads, are unique to the digital realm.

Research on candidate websites provides an illustration of research on the form, function, and content of new election media. Studies have traced the rising sophistication of websites across election cycles and analyzed their changing strategic value in campaigns ( Bimber and Davis, 2003 ; Cornfield, 2004 ; Davis, 1999 ; Druckman, Hennessy, Kifer, and Parkin, 2010 ; Druckman, Kifer, and Parkin, 2007 , 2010 ; Foot and Schneider, 2006 ; Stromer-Galley, 2000 ).

Despite the apparent boundary lines of the phases noted earlier, it has become increasingly difficult to draw clear-cut distinctions between traditional and new media. Technology enables the convergence of communication platforms and the formation of hybrid digital media. Convergence refers to the trend of different communication technologies performing similar functions ( Jenkins, 2006 ). Video sharing platforms, like YouTube, have converged with television in elections as they host campaign ads ( Burgess and Green, 2009 ; Pauwels and Hellriegel, 2009 ). As standard formats take on new media elements, hybrid media have evolved. For example, online versions of print newspapers that originally looked similar to their offline counterparts have come to resemble high-level blogs in style and function. Online newspapers have not only become less formal and more entertainment-focused but now also include mechanisms for interactive engagement and accommodate significant multimedia and user-generated content. Research examining the influence of convergence and hybridity on campaign communication has not kept pace with developments that have important consequences for elections.

Campaign Strategy

Scholars have addressed the ways in which candidates, campaign organizations, and political parties incorporate new media into their strategies. Successful political organizations employ multitiered strategies that integrate traditional and new media tactics. As they take into account the audiences for particular media forms, the strategies of candidates and political parties have become more specialized. A strong majority of senior voters rely primarily on traditional print and electronic sources for campaign data, while younger voters are inclined to consume such information on their smart phones. Digital media have made it possible for campaigns to gather data on voters ranging from their voting history and political leanings to their consumer product preferences. They can also take stock of the electorate’s pulse through a wide range of digital polling tools ( Howard, 2005 ).

The question of how much control candidates have over their campaign messaging in the new media environment has also been raised. Some candidacies are better suited to new media strategies than others ( Davis and Owen, 1999 ). Presidential candidates Bill Clinton and Barack Obama were able to negotiate old and new media comfortably. Others such as George H. W. Bush in 1992 and John McCain in 2008, had greater difficulty adapting to the less formal, more relational style of new media. Candidates’ increasing use of social media has influenced media coverage of campaigns but has had less of an influence on the public’s attention to and perceptions of candidates ( Hong and Nadler, 2012 ; Solo, 2014 ).

The growth in the number of actors who can actively participate in the media campaign in the new media era has created challenges for candidates seeking to control their message. Political organizations such as 527 groups, which are not subject to campaign contribution and spending limits, can run campaign ads and mobilize voters online as long as they do not coordinate with a candidate’s campaign committee. The ads they disseminate can complicate messaging strategies even for candidates they are meant to help.

New Media Audiences

Another body of research focuses on the audiences for new media in elections. Here the most basic question is: Who makes up the audiences for new election media? The answer has changed as Internet penetration has become more widespread and people adopt new forms of digital technology. Early political Internet users were younger, male, and educated. However, as the audiences for new election media have expanded exponentially, they increasingly resemble the general population ( Zickuhr, 2010 ).

Fifty-five percent of voters in the 2010 midterm contests used Internet media for some election-relevant purpose ( Smith, 2011 ); increasing to sixty-six percent in 2014 ( Pew Research Center, 2015 ). Still, younger and more educated people are the most inclined to use the most pioneering platforms. Enthusiasm over new media developments in campaigns can at times overshadow the reality that the audiences for all but a few political media sites are generally small ( Hindman, 2009 ) and use of the most innovative campaign applications can be slight (Owen 2011a , b ).

Related research examines the extent to which new outlets supplement or supplant mainstream media for voters. The dynamics underlying audience media use differ for presidential and midterm elections. Voters are gravitating from traditional television and print sources and moving to the Internet for presidential campaign news ( Owen and Davis, 2008 ). Rather than abandoning traditional sources entirely, many people are adding Internet media as a new source of information during midterm elections ( Smith, 2011 ). Local television news, in particular, remains important for midterm election voters ( Owen, 2011b ). Young people, however, are inclined to use online sources to the exclusion of television and print newspapers in both types of campaigns.

Audience use of campaign media is a research focus that raises a key question: What motivates voters to use new election media? Attempts to address this issue have employed uses and gratifications frameworks to examine the motivations underpinning voters’ media use. Many of these studies rely heavily on lists of media motivations and uses that were developed in the pre‒new media era (see Blumler, 1979 ; Owen, 1991 ). Studies adopting these frameworks reveal that voters use new campaign media for guidance, surveillance/information seeking, entertainment, and social utility ( Kaye and Johnson, 2002 ) as well as to reinforce their voting decisions ( Mutz and Martin, 2001 ).

These standard uses and gratifications have been supplemented by campaign media motivations and uses that take into account digital media’s interactivity, networkability, collaborative possibilities, ability to foster engagement ( Ruggiero, 2000 ), and convenience. New media use involves experiences that are more active and goal-directed than those associated with traditional media. These include problem solving, persuading others, relationship maintenance, status seeking, personal insight, and time consumption. Scholars have also identified uses and gratifications that are linked to specific aspects of new election media use ( Johnson and Kaye, 2008 ). Gratifications are derived from participating in virtual communities, as by establishing a peer identity ( LaRose and Eastin, 2004 ). The use of social media fulfills needs including enhancing social connectedness, self-expression, sharing problems, sociability, relationship maintenance, and self-actualization ( Quan-Haase and Young, 2010 ; Shao, 2009 ). Social media also provide a venue for “political mavericks” to express themselves in new ways ( Hendricks and Schill, 2014 ).

New Media Effects in Elections

Researchers have also investigated the relationship between voters’ use of new media and their levels of political attentiveness, knowledge, attitudes, orientations, and engagement. Early studies of the effects of new media on voters’ acquisition of campaign knowledge produced mixed results, while newer research reveals more consistent evidence of information gain ( Bimber, 2001 ; Drew and Weaver, 2006 ; Norris, 2000 ; Prior, 2005 ; Weaver and Drew, 2001 ; Wei and Lo, 2008 ; Semiatin, 2013 ; Hendricks and Schill, 2014 ; Denton, 2014 ). Scholars have also examined the influence of the use of new election media on the development of political attitudes and orientations, such as efficacy and trust ( Johnson, Braima, and Sothirajah, 1999 ; Kenski and Stroud, 2006 , Wang, 2007 ; Zhang, Johnson, Seltzer, and Bichard, 2010 ).

Some studies have found a positive connection between exposure to online media and higher levels of electoral engagement and turnout ( Gueorguieva, 2008 ; Gulati and Williams, 2010 ; Johnson and Kaye, 2003 ; Tolbert and Mcneal, 2003 ; Wang, 2007 ; Bond et al., 2012 ). However, the effects may not be overwhelming ( Boulianne, 2009 ). The online environment may be most relevant for people who are already predisposed toward political engagement (Park and Perry, 2008 , 2009 ). The use of social media does not necessarily increase electoral participation, although it has a positive influence on civic engagement, such as community volunteerism ( Baumgartner and Morris, 2010 ; Zhang, Johnson, Seltzer, and Bichard, 2010 ).

Young Voters

Young voters, those under age 30, came of political age during the Internet era. Unlike older citizens, who established their campaign media habits in the print and television age, this generation has embraced the election online from the outset. A growing body of literature focuses on the ways in which young voters are using new election media and their effects. Studies indicate that this demographic group is out front in terms of using new media for accessing information ( Lupia and Philpot, 2005 ; Shah, McLeod, and Yoon, 2001 ); indeed, many ignore traditional print and broadcast media and rely exclusively on digital sources ( Owen, 2011b ). Young people are also at the forefront of new election media innovation and participation ( Owen, 2008‒2009; Baumgartner and Morris, 2010 ; Gainous and Wagner, 2014 ). However, young voters’ domination of the digital campaign has been dissipating over time, as the “Internet generation” ages and older citizens gain facility with communication technologies.

Unanswered Questions and New Directions

Research to date has established useful baselines for understanding new media and elections. However, many of the questions that guided early work remain contested or only partially addressed. Much of the existing scholarship has employed well-worn theoretical frameworks that are not entirely appropriate for the new media age and have relied on orthodox methodological approaches, such as survey research and content analysis. In order to track new developments and voters’ use of campaign media innovations, theories explaining the new media’s role in elections should be refined or recast. Creative research methodologies such as the use of time gliders to catalogue the emergence and development of new campaign media should be employed, as well as network analysis that captures the dynamics of social media engagement. Political scientists and communication researchers should collaborate with computer science and technology scholars.

Going forward, scholars should critically and creatively address the basic question: How can new media’s influence in elections be identified, measured, assessed, and explained in the current environment? Since the new media environment is changeable, and tracking developments is difficult, this is a challenging proposition. New media applications are introduced and modified, and they sometimes disappear quickly. Audiences’ new media tastes shift, and their engagement with particular platforms can be mercurial. Candidates, parties, media organizations, and average citizens experiment with new media and introduce new scenarios in virtually every campaign.

Theoretical frameworks should be tested for their capacity to accommodate the unique characteristics of new media, with their inherent multipath interactivity, flexibility, unpredictability, and opportunities for more active engagement. Theories should elucidate the challenges new media present to entrenched media and political hierarchies. They also should address the manner in which new media are influencing campaign logistics and strategies. To address the effects of complex audience dynamics, scholars need to develop analytical categories beyond demographics and basic political orientations. Much excitement has been generated by the prospect of using new media for electoral engagement, but the substance and significance of these forms of activation are barely understood. Studies might more deeply assess whether or not this engagement constitutes meaningful and effective political activation.

Standard methodological approaches should be updated for the new media age or used in conjunction with cutting edge methods. Some of the very same tools that are employed by users of digital media can be used by scholars to collect and analyze data. Electronic sources—such as blogs, discussion forums, and email—can function as archives of material that can be automatically searched, retrieved, extracted, and examined using digital tools. Big data can be employed to examine voter orientations and preferences, with the caveat that their objectivity, reliability, and accuracy are suspect. Research strategies might blend big data analysis and traditional survey research ( Metaxas and Mustafaraj, 2012 ; Groves, 2013 ). Audience analysis also can benefit from fresh methodological approaches. People do not consume news online in the same linear fashion that they read the morning newspaper. Instead, they explore news offerings by following a series of links to particular content. Web crawler techniques can be used to examine online election communities. Digital utilities, such as online timeline creators, visually chart the development of new election media and serve as research tools ( Owen, 2011a ). Journals that can handle digital scholarship using multimedia graphics, and interactive exhibits are being developed.

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The Study of Election Campaigning

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Shaun Bowler &
David M. Farrell

Part of the book series: Contemporary Political Studies ((CONTPOLSTUD))

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Election campaigns attract great attention from voters, media and academics alike. The academics, however, tend to focus their research on the electoral result and on societal and long-term political factors influencing that result. The election campaign — the event of great interest, which has at least some role to play in affecting the result — is usually passed over or at most receives minimal attention. It is generally left to the journalists and pundits to give their insights into the campaign; scanning every television programme and newspaper for the latest news or gossip, scrutinising every campaign development — whether an initiative or gaffe — for its potential effect on the result. These are ‘the boys on the bus,’ the campaign journalists who, emulating Theodore White (1961), provide fascinating accounts of the nitty-gritty of election campaigning. 1 But such studies emphasise the short-term and the ephemeral, rather than the underlying process to any campaign. They necessarily stress the unique rather than the general and as such promote the view of campaigns and campaigning as behaviour specific to each election, indeed to each party.

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Bowler, S., Farrell, D.M. (1992). The Study of Election Campaigning. In: Bowler, S., Farrell, D.M. (eds) Electoral Strategies and Political Marketing. Contemporary Political Studies. Palgrave Macmillan, London. https://doi.org/10.1007/978-1-349-22411-1_1

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How social media is shaping political campaigns, august 17, 2020 • 11 min listen.

Political newcomers can leverage social media to raise money and gain recognition, which could help them compete against incumbents, according to new research co-authored by Wharton’s Pinar Yildirim.

Wharton’s Pinar Yildirim speaks with Wharton Business Daily on Sirius XM about how social media is changing political competition.

In his short-lived campaign for president, entrepreneur and former New York City Mayor Michael Bloomberg spent more than $1 billion of his own money before dropping out of the race in March 2020. More than 70% of that budget went toward advertising.

The extraordinary spend highlights just how much cash it takes to run for public office in America and why it’s so difficult for political newcomers to gain momentum at the polls without connections to influential donors (or in Bloomberg’s case, his own deep pockets). The problem perpetuates through election cycles, which is why up to 90% of incumbents are reelected in what research calls “the incumbency advantage.”

How Has the Internet Revolutionized Political Campaigns?

But social media has changed the game, allowing incumbents and newcomers alike to speak directly to constituents on everything from policy to what they had for dinner. Barack Obama was the first presidential candidate to use the medium, which was still nascent during his 2008 bid, and Donald Trump took to Twitter almost daily to express himself without the filter of traditional media.

“If you look at the way that politicians communicate today, it’s very different than the way that they used to communicate five, 10 years ago,” Wharton marketing professor Pinar Yildirim said. “They would speak through the official speakers or they would be on TV. They would be in print or official online newspapers. Today, they are communicating through places like Twitter. And I think that begs a question, why are they doing that? Is there any benefit to communicating on channels like Twitter?”

“This is not about the age of your constituency.” — Pinar Yildirim

A study co-authored by Yildirim offers some answers. “ Social Media and Political Contributions: The Impact of New Technology on Political Competition ,” written with Maria Petrova and Ananya Sen, finds that political newcomers can get a substantial boost in support by using social media channels, which cost next to nothing and are easily tapped by anyone with an internet connection. The finding is important because it indicates how social media can help level the playing field in politics, where money and access to formal communication channels pose huge barriers to new entrants.

“Never have politicians been so accessible to the public,” the authors wrote in an opinion piece for The Globe Post . Yildirim recently spoke about the researchers’ findings during a segment of the Wharton Business Daily radio show on Sirius XM . (Listen to the podcast at the top of this page.)

Using Social Media for Political Campaign Fundraising

The study, which will be published in Management Science , measured support for a candidate based on donations from individual citizens and whether that support increased after the candidate opened a Twitter or Facebook account. Yildirim said she and her colleagues were surprised to find such a significant effect: Within the first month of using Twitter, politicians were able to raise between 1% and 3% of what they would have raised in a two-year traditional campaign. But that gain flowed almost exclusively to newcomers, not incumbents. And it was amplified when candidates included hyperlinks to more information.

Yildirim made it clear that the advantage has nothing to do with assumptions about age; there is simply more to learn about new candidates.

“This is not about the age of your constituency. This is not because the political newcomers are somewhat more technologically savvy, or their base is younger and that’s where they can communicate and find those individuals on social media,” she said. “We tested all of these, and these are not the drivers.”

Beyond communicating their policy views, new candidates can humanize themselves through their social media accounts, and that helps voters feel more connected to them. For example, former Democratic presidential contender Pete Buttigieg introduced his shelter dogs to his 2 million Twitter followers , while U.S. Sen. Elizabeth Warren used her Instagram account to chat live with supporters who made small contributions to her presidential campaign.

Those small contributions — often between $5 to $100 — seem unlikely to move the needle in a multimillion-dollar political campaign. But the researchers said they are an important part of the voting process because they represent hope.

“There’s this idea that if there are many of us just donating in small amounts, eventually that will turn into a sea of donations, and that could help this person to get elected down the road,” Yildirim said. “So, donations are very meaningful in a number of ways.”

“You don’t have to have the big money, big bucks, big fundraisers, big supporters to be able to communicate on Twitter with your constituency.” — Pinar Yildirim

In Politics, All Communication Counts

If video killed the radio star, as the 1980 pop song declared, will Facebook kill nationally televised debates or news interviews that are the hallmark of old-school political campaigns? Probably not. As Yildirim pointed out, organic coverage from newspapers or television stations is free and reaches a wide audience. And while costly, paid advertising allows candidates to target a specific message to a specific audience. However, so does social media. It cannot be discounted as a low-cost, powerful tool in political competition.

“You don’t have to have the big money, big bucks, big fundraisers, big supporters to be able to communicate on Twitter with your constituency and tell them about what your ideas are for the future,” Yildirim noted. “You can tell them about who you are, what your values are, and this is typically what we see politicians do. They talk about themselves. They talk about their dog, they talk about their favorite sports team, they talk about their favorite place to go in the neighborhood. Of course, you can always talk about your policies and what you hope to achieve if you were elected into an office. And you can do this way before you officially declare running for an office.”

The scholars believe the intersection of social media and politics is ripe for more research, and their paper makes a notable contribution in the field. The finding suggests that, with enough strategy, social media could erase the incumbency advantage and bring American politics back to its grass roots.

“As political campaigns are becoming increasingly more expensive and the need to reach out to constituencies is becoming more vital, social media will undoubtedly play a more important role in determining electoral outcomes as it gives young politicians a platform,” they said in the op-ed.

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Analyzing voter behavior on social media during the 2020 US presidential election campaign

Loris belcastro.

DIMES Department, University of Calabria, Rende, Italy

Francesco Branda

Riccardo cantini, fabrizio marozzo, domenico talia, paolo trunfio, associated data.

The data that support the findings of this study are publicly available. In particular, this data was gathered using Twitter APIs ( https://developer.twitter.com .) and is hosted on Github ( https://github.com/SCAlabUnical/USA2020 ).

Every day millions of people use social media platforms by generating a very large amount of opinion-rich data, which can be exploited to extract valuable information about human dynamics and behaviors. In this context, the present manuscript provides a precise view of the 2020 US presidential election by jointly applying topic discovery, opinion mining, and emotion analysis techniques on social media data. In particular, we exploited a clustering-based technique for extracting the main discussion topics and monitoring their weekly impact on social media conversation. Afterward, we leveraged a neural-based opinion mining technique for determining the political orientation of social media users by analyzing the posts they published. In this way, we were able to determine in the weeks preceding the Election Day which candidate or party public opinion is most in favor of. We also investigated the temporal dynamics of the online discussions, by studying how users’ publishing behavior is related to their political alignment. Finally, we combined sentiment analysis and text mining techniques to discover the relationship between the user polarity and sentiment expressed referring to the different candidates, thus modeling political support of social media users from an emotional viewpoint.

Introduction

In recent years, the growing use of social media is generating an amount of information-rich data never seen before. This data, commonly referred as Big Social Data, can be effectively leveraged by a wide range of techniques aimed at modeling the interactions of users on social media, their collective sentiment and behavior, the dynamics of public opinion, and the patterns of information production (Pang and Lee 2008 ; Cesario et al. 2016 ; Marozzo and Bessi 2018 ; Cantini et al. 2022 ). All the knowledge extracted through such techniques allows to outline a precise profile of social users, by describing them from a behavioral and psychological viewpoint, and by modeling their perception of events and public decisions.

This manuscript presents an in-depth analysis of the posts published on Twitter during the 2020 US election campaign, aiming at outlining an accurate view of the political event from different points of view. Specifically, several techniques of topic discovery, opinion mining, and emotion analysis were combined in a unified data analysis workflow for investigating: ( i ) trending topics and their evolution over time, ( ii ) users’ political alignment and publishing behavior, and ( iii ) users’ sentiment and emotional aspects.

Firstly, we extracted the main discussion topics characterizing the 2020 US election campaign by leveraging the unsupervised approach proposed in Cantini et al. ( 2021 ), which relies on the density-based clustering of the latent representation of trending hashtags. Afterward, in order to achieve a more accurate representation of social media conversation, we studied the weekly evolution of the detected topics, which is useful to understand how online discussion evolves over time.

Secondly, we modeled the political alignment of social media users, in order to understand which candidate or party public opinion is most in favor of in the weeks preceding the Election Day. For this purpose, we exploited IOM-NN ( Iterative Opinion Mining using Neural Networks ), a neural-based opinion mining methodology we previously proposed in Belcastro et al. ( 2020 ). Specifically, a real-time analysis was carried out during the 2020 US presidential election campaign using data gathered from Twitter, correctly determining Joe Biden’s lead over Donald Trump before the Election Day. The achieved results, publicly available through our university web portal, 1 represent a remarkable step forward with respect to previous works present in the literature. In fact, to the best of our knowledge, experimental evaluations are carried out after the end of the considered event, while in our case we have given a proof of the real-time effectiveness of IOM-NN, which leads to the possibility of using it for enhancing or even replacing traditional opinion polls. Furthermore, for the sake of completeness, we extended the results of the real-time analysis by focusing on the main swing states, i.e., those states characterized by a high uncertainty about the winning candidate and for this reason by a marked strategic importance. We assessed the statistical significance of the collected data by studying the age, gender and geographical distribution of Twitter users for understanding whether they can be considered voters of the political event. The obtained results confirm the great effectiveness of our approach, which outperformed the average of the latest opinion polls by correctly identifying the leading candidate before the Election Day in 10 out of 11 swing states. Furthermore, the polarization information achieved by IOM-NN was also leveraged to investigate the temporal dynamics of social media conversation, with the aim of studying how users’ publishing behavior is related to their political alignment, and how it reflected the occurrence of external events like debates or rallies.

Thirdly, we analyzed the relationship between the emotional sphere of Twitter users and their political alignment. In particular, we jointly exploited sentiment analysis and text mining techniques for extracting the sentiment of social media users. Then, we combined this information with the polarization achieved by IOM-NN for investigating how a user refers to the candidates while supporting his/her preferred faction, with respect to a broad spectrum of emotions. This step is useful for understanding how the supporters of a particular candidate express their preference on social media. Specifically, they can praise, as in the case of pro-Trump users, their favorite candidate with positive content that shows emotions like joy and confidence. Alternatively, they may be more likely to discredit the opposing candidate, as in the case of pro-Biden users, by producing negative online content characterized by emotions like anger, disgust and sadness.

The rest of the paper is organized as follows. Section 2 reports the most relevant approaches in computational politics and sentiment analysis present in the literature. Section 3 describes the different techniques combined in the proposed analysis workflow. Section 4 describes the experimental evaluation. Finally, Sect. 5 concludes the paper.

Related work

Computational politics is a research area that involves a set of techniques aimed at analyzing users’ behavior during a political event of interest, both modeling and influencing their perception and opinion about facts, events and public decisions. With the rapid growth of social media usage, microblogging platforms have become a rich source of valuable information, which can be effectively exploited for investigating the patterns of information diffusion, the interactions between users and their opinion about a specific faction or candidate (Belcastro et al. 2020 ). According to a recent survey (Haq et al. 2020 ), existing literature on computational politics can be categorized into five classes, as discussed in the following.

Community and user modeling This class of works focuses on modeling the behavior of social media users from both an individual and collective viewpoint. Many works in this category are related to the analysis of homophily, i.e., the connection of groups of users driven by common interests, which leads to the formation of community structures of like-minded people (Grevet et al. 2014 ; Bastos et al. 2013 ; Fraisier et al. 2017 ). Other works focus on modeling political affiliation of social users, exploiting community information for predicting the results of a political event (Belcastro et al. 2020 ; Chiu and Hsu 2018 ; Takikawa and Nagayoshi 2017 ).

Information flow These works investigate how information flows within the network. Most of them analyze the misinformation spread, trying to detect fake news thus limiting its distortion effects on public opinion (Kim et al. 2018 ; Ciampaglia et al. 2015 ; Gyongyi et al. 2004 ). Other works in this category are also aimed at identifying echo chambers, i.e., situations in which the repetition and sharing of information causes the strengthening of an opinion inside a community (Garimella et al. 2018 ; An et al. 2013 ; Shu et al. 2019 ).

Political discourse Works in this category model online discussion from different points of view, taking into account demographic aspects, community structure and information diffusion patterns. Many works are aimed at extracting the main topics of discussion through topic modeling (Greene and Cross 2015 ; Trabelsi and Zaïane 2019 ), or identifying political crisis (Keneshloo et al. 2014 ). Opinion mining techniques can be also exploited for identifying the opinion or mood of social media users about those topics, as users’ interactions on social media can affect their political engagement (Hoffmann and Lutz 2017 ; Azarbonyad et al. 2017 ; Monti et al. 2013 ).

Election campaigns Research contributions in this class are aimed at measuring the engagement of the online audience, enabling large-scale opinion polls and the management of the political campaign. In fact, social media provide an effective platform for engaging users in political discussion, which is often used by politicians during the political campaigns (Wulf et al. 2013 ; Hong and Nadler 2015 ). Moreover, the analysis of political engagement of social users can accurately forecast the final results of the political event under analysis (Belcastro et al. 2020 ; Saleiro et al. 2016 ).

System design Works in this category propose a full system design of computation politics systems. As an example, Cambre et al. ( 2017 ) propose a system design that can help to break the echo chamber effect, moderating the online political discussion, while Dade-Robertson et al. ( 2012 ) discuss the relationship between political processes, urban environments and situated technologies.

In this work we use opinion mining and sentiment analysis techniques in order to investigate the polarization of the US social media users toward the different candidates involved in the 2020 US presidential election. Starting from this, we identify the emotional state (mood) of social users and its relation with their political orientation. Finally, we exploit the results of polarization analysis in order to forecast the final results.

There are several works in the literature that rely on text mining and natural language processing algorithms for investigating the opinion of social users and their collective sentiment toward political candidates or parties. Oikonomou and Tjortjis ( 2018 ) used Textblob, 2 a Python library for natural language processing, to predict the outcome of the US presidential election in three states of interest (i.e., Florida, Ohio and North Carolina). Wong et al. exploited (Wong et al. 2016 ) SentiStrength, 3 a lexicon-based sentiment analysis tool, for modeling the political behaviors of users by analyzing tweets and retweets. Alashri et al. ( 2016 ) analyzed Facebook posts about the 2016 US presidential election with CoreNLP 4 (Manning et al. 2014 ), one of the most popular tool for natural language processing, to examine the dynamics between candidate posts and comments they received on Facebook and calculate a score for each political candidate for measuring his/her credibility on a given issue. Singh et al. ( 2021 ) carried out a comparison among four machine and deep learning algorithms (i.e., TextBlob, Naive Bayes, SVM, and BERT (Devlin et al. 2018 )) for sentiment analysis. Authors used the 2020 US presidential election as a case study, finding that the use of BERT leads to the best results.

All of the aforementioned techniques are characterized by several issues related to the use of social media data for predicting the outcome of political events, which are language barrier , misclassification , data imbalance and reliability (Bilal et al. 2019 ). Consequently, in order to achieve a precise estimate of the political polarization of the US citizens, we leveraged the IOM-NN technique, specially designed to overcome these issues (Belcastro et al. 2020 ): ( i ) it is language-independent, as it uses a hashtag-based bag of words representation; ( ii ) it avoids misclassifications using a high threshold on the polarization probability; ( iii ) it uses randomized class balancing algorithms in order to avoid the learning process being biased toward majority classes; ( iv ) it requires a preliminary study of users’ representativeness, in order to understand whether they can be considered voters in the political event under analysis (See Sect. 4.1.1 ). Moreover, with respect to state-of-art techniques, IOM-NN allows the classification of a much greater number of tweets and users, due to its incremental and iterative nature, which leads to a better quality and robustness of the results.

Analysis workflow

In this work we present an in-depth analysis of the posts published on Twitter during the 2020 US election campaign, with the aim of outlining an accurate representation of this political event from different perspectives, including users’ publishing behavior, discussion topics, political alignment and its relationships with the emotional sphere.

For this purpose, several techniques were combined in a unified analysis workflow, represented in Fig. 1 , composed of the following steps:

Collection of posts : data are gathered from social media by using a set of keywords related to the considered political event.
Classification of posts : the collected posts are classified in favor of a faction according to the detected political support.
Polarization of users : the classified posts are analyzed for determining the polarization of users toward a faction.
Topic discovery : the collected posts are analyzed in order to identify the politically related discussion topics underlying the conversation on social media, modeling their evolution over time.
Temporal analysis : the temporal dynamics of social media conversation are analyzed and combined with the polarity information of classified posts in order to study users’ publishing behavior in relation to their political alignment.
Emotion analysis : the polarized posts are exploited for investigating the relationship between the political orientation of users and the different emotions they expressed in referring to the different candidates.

Among the aforementioned steps, the first three jointly constitute the IOM-NN methodology (Belcastro et al. 2020 ), while the fourth follows the approach to topic detection in social data proposed in Cantini et al. ( 2021 ). IOM-NN is an opinion mining technique aimed at discovering the political polarization of social media users during election campaigns characterized by the competition of political factions. The methodology relies on an iterative and incremental procedure based on feed-forward neural networks, aimed at discovering the political polarization of social media users by analyzing the posts they publish. An open-source implementation of IOM-NN is available on Github. 5

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A graphic representation of our analysis workflow

In the following sections we provide a detailed description of the proposed analysis workflow. Moreover, to facilitate understanding of the different steps, we will show practical examples by examining a small subset of the collected data.

Collection of posts

The goal of this step is to collect a set P of social media posts from different sources (e.g., Twitter), related to the political event E under analysis. As a first step, the different factions, parties or candidates involved in the political event are identified, defined as the set F = { f 1 , f 2 , ⋯ , f n } . In particular, in the case of the 2020 US presidential election, we focused on the two main candidates Joe Biden and Donald Trump. Afterward, geotagged posts are gathered by using a set of keywords K that is partitioned as follows:

neutral keywords ( K context ) that contains generic keywords that can be associated to E without referring to any specific faction in F (e.g., # v o t e , # e l e c t i o n 2020 );
faction keywords ( K F ⊕ = K f 1 ⊕ , ⋯ , K f n ⊕ ) that contains the keywords used for supporting each faction (e.g., # v o t e b i d e n , # m a g a ).

The keywords selection process requires a small amount of domain knowledge, as these keywords can be manually selected among the trending hashtags that people commonly use to refer to E on social media. Moreover, the keyword selection process can be automatized by searching for specific patterns, like “#vote + candidate ”, often used for labeling politically polarized posts. We assessed the statistical significance of the collected posts by studying the age, gender and geographical distribution of Twitter users for understanding whether they can be considered voters of the political event. For this purpose we used a wide range of information which can be directly extracted from users metadata (e.g., location and language), or examined starting from statistical reports about the usage of the social media platform in a given country (e.g., user distribution by age and gender). Furthermore, in order to improve the representativeness of the collected posts, user accounts are analyzed, filtering those that show anomalous publishing activity, such as social bots or news sites, or those that have inconsistent information in their profile, such as for example a location that is not defined or does not belong to any of the states considered (see Sect. 4.1.1 ).

Collected posts undergo the following preprocessing operations: i ) the text of each post is converted to lowercase and accented characters are normalized; ii ) words are lemmatized and stemmed (e.g., vote or votes or voted → vot); iii ) stopwords are removed; and iv ) bigrams are identified (e.g., San Francisco → San _ Francisco). Figure 2 shows an example of how posts are collected using keywords about the 2020 US presidential election. Some of these keywords are generic (e.g., # v p d e b a t e 2020 ), and others are used to support a specific candidate (e.g., # v o t e b l u e for Biden and # t r u m p 2020 for Trump).

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Example of how the collection of posts step works

Classification of posts and temporal analysis

During this step, the posts P collected in the previous step are classified in favor of a faction by using IOM-NN. Specifically, a preliminary iteration is performed for classifying input posts according to the keywords in K F ⊕ . Posts containing keywords related to exactly one faction are polarized toward that faction, while remaining posts are labeled as neutral. Then, neutral posts undergo an iterative classification process, during which the model exploits the knowledge acquired at the previous iterations. It is worth noticing that, due to the incremental nature of the annotation process, IOM-NN is not tied to a specific set of initial faction keywords and does not require an in-depth knowledge of the political event under consideration. In fact, even starting from a small but representative set of faction keywords, IOM-NN is able to infer new classification rules iteratively, which implies a good robustness and generalizability of the methodology. Figure 3 shows a classification example of a small set of tweets about the 2020 US presidential election, which exploits the following faction keywords.

K Biden ⊕ = { # v o t e b l u e , # b a c k t h e b l u e , # v o t e b i d e n , ... } ;
K Trump ⊕ = { # v o t e r e d , # t r u m p 2020 , # m a g a , ... } .

At iteration 0, IOM-NN uses the keywords in K F ⊕ for classifying five tweets. In the subsequent iterations, the neural model iteratively exploits the tweets classified in the previous steps for generating new classification rules based on co-hashtag relationships. As an example, at iteration 1 the model is trained with the tweet classified at iteration 0, discovering new political-oriented topics of discussion and generating the following classification rules:

tweets with keywords # b o u n t y g a t e are classified in favor of Biden since Donald Trump was accused of paying Moscow’s secret agents for the killing of the US servicemen in Afghanistan;
tweets with keywords # c r o o k e d b i d e n are classified in favor of Trump since Hunter Biden (i.e., second son of US President Joe Biden), was accused by Donald Trump of wrongdoing in regard to China and Ukraine.

This learning process iterates until the algorithm is no longer able to generate new classification rules and therefore to identify the polarization of new tweets.

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Example of how the classification of posts step works

After having classified the posts according to the polarity discovered by IOM-NN, this information is used for investigating how the user publishing behavior is related to their political alignment. Specifically, temporal dynamics of social media conversation are analyzed, studying how information is produced by the supporters of both candidates, and how this reflects the occurrence of external events such as debates and rallies.

Polarization of users

This step is aimed at analyzing the set of previously classified posts in order to determine the polarization of users toward a faction. Specifically, the list of classified posts for each user u is computed, filtering out those users that published a number of posts below a given threshold. Afterward, a score vector v s u for each user u is computed, which contains his/her score for each faction. Finally, IOM-NN calculates the overall faction score as the normalized sum of the score vectors. Figure 4 shows how the polarization of users step works on the classified posts reported in Fig. 3 . For each user, the posts in favor of Biden and Trump are counted, discarding those users who have published less than two tweets. Then, the polarization vector for each user is computed containing the percentage of posts published in favor of his/her preferred faction. Lastly, the final score vector is determined, that contains the overall polarization percentages for the two candidates.

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Example of how the polarization of users step works

Emotion analysis

This step analyzes the polarized posts for identifying the users’ sentiment underlying the online discussion about the presidential candidates. Specifically, we combined the information about the political alignment of social media users with their sentiment and emotional expressions. Firstly, in order to extract the sentiment from online published content, we exploited SentiStrength (Thelwall 2017 ) for the annotation of social media posts. In particular, for each polarized post we computed a positive S c + ( p ) and negative S c - ( p ) sentiment score, both ranging between 1 (neutral) and 5 (strongly positive/negative). Then, the overall sentiment score S c ( p ) of a polarized post is obtained as follows: S c ( p ) = S c + ( p ) - S c - ( p ) . Secondly, we modeled the political orientation of social media users from an emotional point of view by exploiting NRC-EmoLex (Mohammad and Turney 2013 ), a publicly available emotion lexicon which has proven its performance in several sentiment and emotion classification tasks, as described in Kiritchenko et al. ( 2014 ), Mohammad ( 2012 ), and Nakov et al. ( 2016 ). Specifically, NRC contains more than 14 thousand English terms labeled by the expressed polarity (i.e., positive or negative) and eight basic emotion categories of Plutchik ( 2001 ) (i.e., joy, trust, anticipation, sadness, surprise, disgust, fear or anger). Finally, we combined the obtained information with the political alignment discovered by using IOM-NN, in order to extract the overall sentiment and emotions expressed by social media users, while talking about the two candidates.

As an example, Figs. 5 and and6 6 show how the sentiment analysis step works on the polarized tweets obtained at the previous step (i.e., a small subset of the collected tweets) for understanding the emotional state of the users who support the different candidates. As we can see, polarized tweets are quite positive for both candidates, but show different emotional profiles. In particular, Biden’s supporters show trust in the new presidential candidate, while Trump’s ones express their joy at having Trump as president.

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Example of pro-Biden tweets

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Example of pro-Trump tweets

Topic discovery

This step is aimed at identifying the main politically-related discussion topics characterizing the 2020 US election campaign, by following the unsupervised approach used in Cantini et al. ( 2021 ). As a first step, a Word2Vec model is trained on the entire corpus of tweets, in order to get the latent representation of hashtags and words in a 150-dimensional vector space. We selected the dimension of the embedding space by conducting several experiments, finding out the smallest size for which a clear clustering structure emerged, i.e., the best trade-off between complexity and representativeness. Subsequently, all hashtags are embedded in that 150-dimensional space, whose dimensionality is then reduced by using the t-distributed stochastic neighbor embedding (t-SNE) technique, initialized through principal component analysis (PCA), to obtain a 2D projection of that space. Moreover, in order to reduce noise, all hashtags with a frequency lower than a given threshold are filtered out. Finally, the OPTICS algorithm is used for extracting a clustering structure based on the topic-based separation of hashtags, induced by the projection of their semantic distribution. We have chosen this clustering algorithm due to its ability to discover clusters with arbitrary shape. In addition, compared to classical density-based algorithms such as DBSCAN, it is able to extract clustering structures at different density levels, which in our work is useful for dealing with micro-topics.

The US 2020 Presidential election analysis and experimental results

In this section we provide an accurate description of the results coming from the analysis of the 2020 US presidential campaign, characterized by a strong rivalry between Joe Biden and Donald Trump. In particular, we analyzed election-related tweets with the aim of outlining a precise representation of this political event from different points of view, in terms of users’ publishing behavior, sentiment, political alignment and discussion topics. For this purpose we combined several techniques in an analysis workflow, whose steps are accurately described in Sect. 3 and whose results are reported in the following sections.

Data description

The data used to perform the experimental evaluation comes from a public repository that contains a real-time collection of tweets related to the 2020 US presidential election from December 2019 to June 2021 (Chen et al. 2021 ). From such repository we considered only the tweets published close to the election event (from September 1 to October 31, 2020), i.e., about 160 million of which 18 million are tweets (11%), 110 million are retweets (69%), and 32 million are replies (20%), posted by about 29 million users. Only 22% of filtered data contain hashtags (e.g., # t r u m p 2020 , #bidenharris2020 ), useful to understand the arguments used in favor of the different candidates. In particular, the percentage of tweets published with at least one hashtag related to Trump (i.e., # t r u m p , # t r u m p 2020 , and # m a g a ) and Biden (i.e., # b i d e n h a r r i s 2020 , # b i d e n ) is about 31% and 11%, respectively. However, 7% of tweets contain at least one negative hashtag about Trump (i.e., # t r u m p k n e w , # p e d o t r u m p , # t r u m p h a s c o v i d , # t r u m p t a x r e t u r n s , #bountygate ), whereas only 1% of tweets contain a negative hashtag for Biden (i.e., # c r o o k e d j o e b i d e n ). In order to ensure the representativeness of the collected posts, we analyzed users’ account information, filtering out content posted by users that show an anomalous publishing activity or inconsistent profile information. This step allows to avoid the negative effects caused by the presence of content published by new sites and social bots, which can introduce a heavy bias in social media data (Cantini et al. 2022 ). We further analyzed the publishing behavior of the users in the filtered dataset by determining the Complementary Cumulative Density Function (CCDF) of shared tweets per user. Specifically, given the random variable X representing the number of shared tweets, it is determined by the frequency of users publishing a number of posts greater than x , i.e., the probability P ( X > x ) . The scatter plot in log-scale shown in Fig. 7 , reveals a highly skewed distribution, with few active Twitter users posting a huge amount of tweets, and many users posting infrequently or not at all, the so-called social lurkers.

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Complementary Cumulative Density Function (CCDF) of published tweets per user

Statistical significance of collected data

Here we investigate the statistical significance of the collected data in order to assess users representativeness, i.e., whether they can be considered voters of the political event under analysis.

Firstly, from tweets metadata we extracted aggregate information on the used language of social media users, discovering that most of tweets have the lang field set to English (about 90%), whereas the remaining 10% is Undefined or set to other languages like Spanish . Secondly, we compared the number of Twitter users in our dataset, grouped by state, with the number of adult citizens actually living in that state, belonging to the voting-eligible population (VEP). 6 Specifically, users were associated with states via Twitter metadata, by analyzing the location field present in each tweet, which indicates the location defined by the user in his/her Twitter account (e.g., Austin, TX). It is worth noting that, from the textual analysis of this field, it is not always easy to extract a meaningful city/state, as many users either left the field blank, or did not provide precise information (e.g., “USA”), or specified fictitious or nonexistent locations (e.g., “the moon” or “NY, Italy”). We measured the strength of this correlation, finding a Pearson coefficient r = 0.97 , significant at p < 0.01 . The linear relationship that links users and the voting-eligible population can be easily seen in Fig. 8 , which depicts an interpolation of the related scatter plot, with a goodness-of-fit R 2 = 0.93 . Notice that outlier states were not considered in this step in order to achieve meaningful results, by excluding data of different magnitude. In addition, we explored age and gender distribution of analyzed users, finding out that about 94% of them are adults (at least 18 years old) 7 and almost equally divided by gender . 8

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Linear interpolation: analyzed users versus voting-eligible population grouped by the US states

Among all the available tweets we have selected those published by users located in the 11 main swing states (i.e., Arizona , Florida , Georgia , Michigan , Minnesota , Nevada , New Hampshire , North Carolina , Pennsylvania , Texas , Wisconsin ). We analyzed only these states as they are characterized by a marked political uncertainty and their outcomes have a high probability of being a decisive factor of the electoral event. We made this data, used in all the subsequent analysis steps, publicly available on Github. 9

Table 1 reports a comparison between the users we were able to capture for each swing state and the VEP. The high correlation between the number of analyzed users per state and the VEP leads to a significant set of social media data effectively exploitable to determine the polarization of public opinion. However, despite the representativeness of the considered posts, the results achieved by the analysis of the online conversation can be influenced by platform biases. Specifically there exist usage biases due to the distribution of users of a social media platform in terms of gender, age, culture and social status, as well as technical biases related to platform policies about data availability and restrictions imposed in some areas of the world.

Number of Twitter users versus voting-eligible population (VEP) grouped by swing states

Temporal analysis

In this step we investigated the temporal dynamics of social media conversation, in order to analyze users’ publishing behavior, studying how it is related to the detected polarity and how it reflected the occurrence of external events (e.g., debates, rallies, etc.). However, as described by the repository owners in Chen et al. ( 2021 ), there may be gaps in the dataset due to several issues. Firstly, the data collection step was highly contingent upon the stability of the network and hardware. Secondly, Twitter significantly limits the number of tweets that can be rehydrated. Finally, tweets may no longer be available as users have been removed, banned, or suspended.

Figure 11 shows the timeline of polarized tweets volume annotated with the four main political debates occurring during the election campaign, i.e., between September 1 and October 31. The first observation period (September 1 to September 28) exhibits significantly different communication dynamics prior to the first debate. Interestingly, this image shows an intense activity spikes of Biden’s supporters, as a likely consequence of President Trump’s actions:

September 10 : president Trump has attacked Democratic Vice Presidential candidate Kamala Harris.
September 15 : despite being banned by state authorities from holding rallies, President Trump still decided to hold one in Nevada.
September 18 : president Trump blamed blue states for the high number of the US Covid-19 fatalities.
September 28 : during a rally in Pennsylvania, Trump called Biden “a dishonest politician and a puppet in the hands of the radical left”.

The second and third observation windows (from September 30 to October 31) show typical weekly cycles of social media chatter, with no particular explosion or shock-related spike from external events, except for October 6 (before the Vice Presidential debate) and October 13 (before the second Presidential debate).

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Time series of polarized tweets published from September 1 to October 31, 2020

Comparative analysis with opinion polls

In this step we assessed the effectiveness of our approach in determining the polarity of social media users with the aim of understanding which candidate or party public opinion is most in favor of. A first remarkable result was obtained through a real-time analysis, carried out on Twitter data collected during the two weeks before the Election Day. Specifically, IOM-NN was able to correctly determine Joe Biden’s lead over Donald Trump, especially in Georgia, where a Democratic candidate had not won since 1992 with the election of Bill Clinton. This promising result, publicly available through our university web portal, 10 represents a step forward with respect to our previous work, as it gives a clear proof of the real-time effectiveness of IOM-NN, which suggests the possibility of using it to enhance or even replace traditional opinion polls.

Starting from the encouraging real-time results, we extended that analysis by focusing on the main eleven swing states, as described in Sect. 4.1.1 . Specifically, we compared the results obtained through IOM-NN with the average values of the latest opinion polls before the election. 11 For each analyzed state, Table 3 reports the real voting percentages, opinion polls, and IOM-NN estimates. The two candidates (i.e., Joe Biden and Donald Trump) are indicated with “ B ” and “ T ”, respectively. The winning candidate is written in bold when it is correctly identified.

Comparison between voting percentages estimated by IOM-NN and the latest opinion polls

The results of the comparison are summarized in Fig. 12 , which shows that the estimates achieved by IOM-NN, related to the voting intentions of social media users are more in-line with the actual behaviors of voters with respect to the opinion polls, thus giving a clue to the final result in 10 out of 11 swing states (with an average accuracy of 91%). Using this metric we penalize the inversions of polarity which can be a crucial issue while analyzing these kinds of states characterized by a high degree of uncertainty. Notice that, for what concerns North Carolina, neither the estimates achieved by IOM-NN nor the opinion polls were in-line with the actual outcome in this state. This is a common situation as the results achieved by the polls and IOM-NN must be understood as an estimate of the polarization of public opinion in the weeks preceding the Election Day, not always in accordance with the actual behavior of voters. Moreover, a noteworthy advantage of IOM-NN with respect to traditional opinion polls, is the ability to capture the opinion of a larger number of people more quickly and at a lower cost. This makes IOM-NN a valid support to enhance or even replace opinion polls, by providing relevant insights useful to understand the dynamics of the election campaign.

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Comparison between IOM-NN and the latest opinion polls in identifying the winning candidate

The goal of this last step is to model the political orientation of Twitter users from an emotional point of view. To this purpose, we used the SentiStrength tool (as explained earlier in Sect. 3.4 ), for discovering the existing relationships between user polarity and the sentiment expressed in referring to the two presidential candidates. Then, for each polarized tweet we explored the emotion the tweet conveys. Figures 13 and and14 14 describe the sentiment and the emotional state of the tweets with the relative intensity of the tweets produced by Trump and Biden supporters, respectively.

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Distribution of sentiments and emotions of pro-Trump tweets

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Distribution of sentiments and emotions of pro-Biden tweets

What appears evident is that, on average, the tweets produced by Trump’s supporters are significantly more positive than those produced by Biden’s supporters, which devote a significant number of negative tweets to their opponent.

For what concerns the detected emotions, Trump’s supporters express joy and confidence about Trump, while fear about Biden’s election. Biden’s supporters, instead, show trust and anticipation in having Biden as future president of the USA, with a more marked presence of negative emotions about Trump, like anger , disgust and sadness .

Tables 4 and and5 5 show various examples of tweets including in the analysis, showing how our approach can model social media conversation from an emotional point of view.

A sample of pro-Trump tweets showing different emotions

A sample of pro-Biden tweets showing different emotions

Conclusions and final remarks

The widespread use of social media can be exploited to extract useful information concerning people’s behaviors and interactions.

In this paper we presented an in-depth analysis of the posts published on Twitter during the 2020 US election campaign, jointly exploiting several techniques for topic discovery, opinion mining and emotion analysis in a unified analysis workflow, with the aim of outlining an accurate representation of this political event from different points of view. In particular, we extracted the main discussion topics following a clustering-based approach, monitoring their weekly impact on social media conversation. Moreover, we leveraged IOM-NN to estimate the polarization of Twitter users regarding the two main candidates Donald Trump and Joe Biden, both in real-time and by focusing on the main US swing states. We also investigated the temporal dynamics of the online discussion, combining it with the polarization information coming from IOM-NN, in order to study how users’ publishing behavior reflected external events, over time, in relation to their political orientation. Finally, we exploited sentiment analysis and text mining techniques to discover the relationship between the user polarization, determined with the aid of IOM-NN, and the sentiment expressed in referring to the different candidates, thus modeling political support of Twitter users from an emotional viewpoint.

Experimental evaluation shows that in the early weeks online conversation focused on the relationship between Trump and Covid-19 pandemic and on the nomination of Judge Amy Coney Barrett as Associate Justice of the US Supreme Court. In the following weeks, instead, the focus of the online conversation shifted to other topics including the accusations leveled to Hunter Biden and the criticism leveled against Trump linked to his position about the climate crisis and veterans. Regarding the political polarization of public opinion, IOM-NN was able to achieve meaningful estimates of the voting intentions of social media users, which makes it a valid solution to go beyond traditional opinion polls, by providing relevant insights useful to understand the dynamics of the election campaign. One major drawback of this approach lies in different possible platform biases, such as usage biases due to the distribution of users of a social media platform in terms of gender, age, culture and social status, as well as technical biases related to platform policies about data availability and restrictions imposed in some areas of the world. Finally, as for the analysis of the emotional state of social users, we found out that the tweets produced by Trump’s supporters are significantly more positive than those produced by Biden’s supporters. In particular, i ) Trump’s supporters express joy and confidence about Trump, while fear about Biden’s election; ii ) Biden’s supporters show trust and anticipation in having Biden as future president of the USA, with a more marked presence of negative emotions about Trump, like anger , disgust and sadness .

As future work, we will apply the presented analysis workflow to other scenarios, such as product adoption analysis and reputation evaluation of companies. In fact, it can be easily generalized to different use cases, as it is not tied to any specific application domain, and only relies on the representativeness of the analyzed posts. Moreover, we can integrate other techniques in our workflow, introducing new steps aimed at improving the quality of the achieved results. As an example, a hashtag recommendation model can be used for enriching the information content of the analyzed data, since keyword-based approaches like IOM-NN are strongly dependent on the availability of consistent hashtags in social media posts (Cantini et al. 2021 ).

Acknowledgements

This project has received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 955558. The JU receives support from the European Union’s Horizon 2020 research and innovation program and Spain, Germany, France, Italy, Poland, Switzerland, Norway.

Open access funding provided by Università della Calabria within the CRUI-CARE Agreement.

Data availability

1 https://scalab.dimes.unical.it/usa-2020/ (text in Italian).

2 https://textblob.readthedocs.io/en/dev/ .

3 http://sentistrength.wlv.ac.uk/ .

4 https://stanfordnlp.github.io/CoreNLP/ .

5 https://github.com/SCAlabUnical/IOM-NN .

6 http://www.electproject.org/2020g .

7 https://www.statista.com/statistics/265647/share-of-us-internet-users-who-use-twitter-by-age-group/ .

8 https://www.statista.com/statistics/265643/share-of-us-internet-users-who-use-twitter-by-gender/ .

9 https://github.com/SCAlabUnical/USA2020 .

10 https://scalab.dimes.unical.it/usa-2020/ (text in Italian).

11 https://www.270towin.com/2020-polls-biden-trump/ .

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Contributor Information

Loris Belcastro, Email: ti.lacinu.semid@ortsaclebl .

Francesco Branda, Email: ti.lacinu.semid@adnarbf .

Riccardo Cantini, Email: ti.lacinu.semid@initnacr .

Fabrizio Marozzo, Email: ti.lacinu.semid@ozzoramf .

Domenico Talia, Email: ti.lacinu.semid@ailat .

Paolo Trunfio, Email: ti.lacinu.semid@oifnurt .

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Americans’ Dismal Views of the Nation’s Politics

8. the presidency and presidential politics, table of contents.

The impact of partisan polarization
Persistent concerns over money in politics
Views of the parties and possible changes to the two-party system
Other important findings
Explore chapters of this report
In their own words: Americans on the political system’s biggest problems
In their own words: Americans on the political system’s biggest strengths
Are there clear solutions to the nation’s problems?
Evaluations of the political system
Trust in the federal government
Feelings toward the federal government
The relationship between the federal and state governments
Americans’ ratings of their House member, governor and local officials
Party favorability ratings
Most characterize their party positively
Quality of the parties’ ideas
Influence in congressional decision-making
Views on limiting the role of money in politics
Views on what kinds of activities can change the country for the better
How much can voting affect the future direction of the country?
Views of members of Congress
In their own words: Americans’ views of the major problems with today’s elected officials
How much do elected officials care about people like me?
What motivates people to run for office?
Quality of recent political candidates
In elections, is there usually at least one candidate who shares your views?
What the public sees as most important in political candidates
Impressions of the people who will be running for president in 2024
Views about presidential campaigns
How much of an impact does who is president have on your life?
Whose priorities should the president focus on?
How different are the Republican and Democratic parties?
Views of how well the parties represent people’s interests
What if there were more political parties?
Would more parties make solving problems easier or harder?
How likely is it that an independent candidate will become president?
Americans who feel unrepresented by the parties have highly negative views of the political system
Views of the Electoral College
Should the size of the U.S. House of Representatives change?
Senate seats and population size
Younger adults more supportive of structural changes
Politics in a single word or phrase: An outpouring of negative sentiments
Negative emotions prevail when Americans think about politics
Americans say the tone of political debate in the country has worsened
Which political topics get too much – and too little – attention?
Majority of Americans find it stressful to talk politics with people they disagree with
Acknowledgments

Americans’ dissatisfaction with politics extends to their views of presidential campaigns, both present and past. Only about a third (35%) say they are satisfied with the people who will be running for president next year.

Looking back at recent presidential campaigns, sizable majorities say they were not informative, too long and not focused on the right issues. Nearly eight-in-ten Americans (78%) say recent campaigns did not feature party nominees who were good candidates.

Most Americans say who the president is makes a big difference for such areas as the U.S. standing in the world and the mood of the country. Far fewer say who is president makes a big difference to their personal life.

Large shares of both Republicans and Democrats say the president should focus at least a fair amount on the priorities of people who voted for them. Smaller majorities say they should focus on the interests of people who did not vote for them. Republicans are less likely than Democrats to say the president should focus on the priorities of people who did not vote at all.

Chart shows Americans express little satisfaction with the 2024 presidential field

Most Americans (63%) say they are not too or not at all satisfied when thinking about the people who will be running for president in 2024. In the survey, conducted in mid-July, about a quarter (26%) say they are fairly satisfied. Just 8% are extremely or very satisfied.

While there is widespread dissatisfaction in both party coalitions, Republicans are somewhat more satisfied than Democrats. Among Democrats and Democratic-leaning independents, just 24% are at least fairly satisfied (with only 4% reporting they are extremely or very satisfied). Among Republicans and Republican leaners, about half (48%) are at least fairly satisfied (including 13% who are extremely or very satisfied).

Chart shows most say recent presidential campaigns have been too long, focused on wrong issues, had subpar nominees

Americans are critical of recent presidential campaigns in negative ways, with seven-in-ten or more saying they haven’t been informative (71%), have focused on the wrong issues (77%), have lasted too long (72%) and haven’t featured nominees who were good candidates (78%).

Americans are more evenly split over whether campaigns have been interesting or dull, although they are still more negative than positive. About half (52%) say recent presidential campaigns have been dull, while 44% say they have been interesting.

Partisan and engagement differences on recent presidential campaigns

There are relatively modest partisan gaps in views of whether presidential campaigns are interesting, informative, go on too long, focus on the right issues or result in good nominees. But in both partisan coalitions, there are some notable differences between more and less politically engaged people.

Dull or interesting?

People who are less politically engaged are considerably more likely to say recent political campaigns have been dull, rather than interesting. And this holds among both Republicans and Democrats. More than six-in-ten (64%) less politically engaged people say campaigns have been dull, but that falls to about four-in-ten among people who are highly politically engaged.

Chart shows less politically engaged Americans are more likely than highly engaged adults to say campaigns have been dull and less likely to say they have lasted too long

Are too long or not too long?

People with higher levels of political engagement are particularly likely to say presidential elections go on too long. Though majorities of those at all levels of political engagement say this, 80% of high-engagement people hold this view (including 76% of high-engagement Republicans and 85% of high-engagement Democrats), this drops to about six-in-ten (62%) among people with low levels of political engagement.

Have the nominees been good candidates?

Although clear majorities also say recent presidential nominees have not been good candidates, Democrats overall are somewhat more likely than Republicans to say this (82% vs. 73%). The partisan gap is somewhat wider among more engaged Americans: 85% of highly engaged Democrats and 69% of highly engaged Republicans say recent presidential elections have not yielded good candidates.

Chart shows most Americans say who is president makes a big difference for U.S. standing in the world, mood of the country, national security – but not them personally

Today, 67% of adults say who is president makes a big difference for the United States’ standing in the world, with clear majorities saying the same for the mood of the country (65%) and national security (58%).

Roughly half of Americans (52%) also say who is president makes a big difference for the health of the economy.

By comparison, far fewer Americans say who is president makes a big difference in their personal lives. About a quarter (24%) say this (though half say this makes some difference).

Chart shows Americans say a president of either party should focus on the priorities of people who voted for them

Regardless of whether the winner of a close presidential election is a Democrat or Republican, Americans overwhelmingly say the president should focus on the priorities of the people who voted for them. But sizable shares also say the president should focus on the priorities of those who voted for the president’s opponent and the concerns of those who didn’t vote.

About eight-in-ten adults say a president of either party who takes office after a close election should focus at least a fair amount on the priorities of those who voted for them, with nearly identical shares saying this about a Republican president who wins narrowly (82%) and a Democratic president who wins narrowly (83%). Roughly a third say this group should receive a great deal of focus from a president of either party.

Clear majorities of Americans also say a newly elected Republican (70%) or Democratic (73%) president should focus on the priorities of the people who voted for the other candidate. However, the public is less likely to say that the president should focus a great deal on this group’s priorities (22% for a Republican president, 23% for a Democratic president).

Americans are less likely to say a new president should focus on the priorities of the people who didn’t vote. Still, six-in-ten say a president of either party should focus at least a fair amount on this group’s priorities.

Partisan views of presidential winners

Chart shows partisans more likely to say presidents of the same party should focus on the priorities of people who voted for them

Democrats and Republicans alike overwhelmingly say that presidents should focus a great deal or fair amount on the priorities of the people who voted for them following a close election.

Those who identify with or lean to each party are slightly more likely to say this when asked about a presidential winner of their party (86% each). Still, at least eight-in-ten say that a winning president should focus on the priorities of their voters – even when the president they are being asked about is a member of the opposing party.

But when asked about the priorities of people who voted for the losing candidate, both Democrats and Republicans are more likely to think the losing side’s concerns should matter when the losers are in their partisan coalition.

For instance, 80% of Democrats and Democratic leaners say a Republican president should listen at least a fair amount to the people who voted for that president’s Democratic opponent. But a smaller share (69%) of Democrats say a Democratic victor should listen to people who voted for that president’s Republican opponent.

The pattern is nearly the same among Republicans: When asked about a Democratic president, 81% of Republicans say the president should listen to those who voted for the Republican, while 63% say a GOP president should listen to Democratic voters.

Democrats are more likely than Republicans to say a president should focus on the concerns of people who did not vote – and this is the case when asked about both Democratic and Republican winners. About two-thirds of Democrats, compared with 54% of Republicans, say this in both scenarios.

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Exploring Role of Political Human Brand Personality, Appearance and Behavioral as Political Brand Association in Political Campaigns for Voters’ Preferences

Qazi Fahad Iqra University
Mirza Amin ul Haq

The purpose of this paper is to explore how political branding is defined and exhibited during the local body elections in Pakistani regions where voter turnout is high in number. This paper offers new appreciation in the study of political branding which is widely explored in the West. Data were collected through in-depth interviews among 37 campaign marketers who played an important role during elections and were participants in the World Marketing Summit 2020, Lahore, Pakistan. Political branding rotates around the personality measures through his/her intensity of charisma and charm, being smart, responsible and attentive, compassionate, trustworthy, and hardworking. While brand appearance includes visual representation. Musical entertainment in public meetings can also be a political brand-identifying agent. Political Brand Behavior covers emotional gestures, conscientiousness, and consistency. Overall, the study demonstrates how political branding helps voters differentiate candidates and facilitates recall at the ballot box, thereby potentially increasing voter turnout. Enhancing the likability of political brands through strategically crafted branding strategies may lead voters to respond more favorably to those campaigns. The research holds significance for advancing the understanding of political marketing practices in non-Western contexts. The findings provide actionable insights for campaign strategists seeking to optimize branding approaches. Moreover, the study contributes novel perspectives on how personality, appearance, and behavior collectively shape a candidate's political brand identity and popularity among constituents

Keywords: political branding, brand personality, brand appearance, brand behavior, political campaigns, Pakistan, local body elections

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Social Media in Political Campaigning Around the World: Theoretical and
The impact of social media in political campaigning around the world is undeniable. Latest statistics show that close to three fourth of U.S. adults use social networking sites such as Facebook and Twitter, with social network use becoming almost ubiquitous among young adults, according to recent data from the Pew Research Center (2018).Globally, an estimated 2.62 billion people use social ...
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His research interests include political communication, elections, election campaigns, media effects, and political elites. He has edited the book Parliamentary Candidates between Voter and Parties. A Comparative Perspective (with Lieven De Winter and Hermann Schmitt) and authored the book After the Mass Party (with Elin Allern and Knut Heidar).
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Abstract. Social media has been a part of election campaigns for more than a decade. In this special issue, we combine longitudinal and cross-national studies of social media in election campaigns, expanding the time span as well as number of countries compared to former comparative studies. The four papers present examples of longitudinal ...
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RELATED LITERATURE AND RESEARCH GAPS. Our study is located at the intersection of cross-media and social media research. There is an established research tradition relating the use of different media to outcomes and processes like political knowledge, participation, and voting (Prior, Citation 2007), news consumption (Althaus & Tewksbury, Citation 2002) and political communication (Druckman et ...
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This paper explores the critical role of strategic communication in contemporary political campaigns. It focuses on the integration of social media, data analytics, and persuasive messaging to ...
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Papers whose main research question, or primary analysis, was based on how media relates to political polarization were deemed as having a main focus on this topic. With this ... European countries - where there is rising extremism (Koehler, Citation 2016) and increasing use of social media by political campaigns (e.g. Baxter ...
56 New Media and Political Campaigns
New media have triggered changes in the campaign strategies of political parties, candidates, and political organizations; reshaped election media coverage; and influenced voter engagement. This chapter examines the stages in the development of new media in elections from the use of rudimentary websites to the rise sophisticated social media.
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A review of the evidence leaves no doubt election campaigns do matter in a variety of important ways. The serious questions concern when, where, why, how, for what, and for whom they matter. This essay reviews a selection of high-quality studies that address these questions, focusing on several distinct lines of research that have been particularly productive in recent years: on the effects of ...
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A narrow majority of U.S. adults who report having voted in the general election - 53% - say they engaged in at least one of six different political activities over the past six months, including contributing money to candidates, attending political rallies or events or working for campaigns. More than a third (36%) say they demonstrated ...
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political campaigning studies between 2011 and 2021. The. analysis was carried out on ve broad levels of bibliometric. indicators - scienti c production, authors, country level, a liation, scienti ...
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Our research for this paper draws from our extensive experience tracking the growth of digital marketing over the past two decades in the United States and abroad, monitoring and analysing ... political campaign marketing urges political campaigns to use all the social media platform tools.
Social Media in Political Campaigning Around the
during the 2016 U.S. Campaign and connect that to news values and gender leadership qualities. Extending social media research outside the U.S. context, Bruns examines the role of Twitter in Australian federal elections, comparing its use between the 2013 and 2016 campaigns. Another important aspect of the political conversation on social media
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Abstract. Election campaigns attract great attention from voters, media and academics alike. The academics, however, tend to focus their research on the electoral result and on societal and long-term political factors influencing that result. The election campaign — the event of great interest, which has at least some role to play in ...
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00:00. 00:00. Wharton's Pinar Yildirim speaks with Wharton Business Daily on Sirius XM about how social media is changing political competition. In his short-lived campaign for president ...
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Election campaigns Research contributions in this class are aimed at measuring the engagement of the online audience, enabling large-scale opinion polls and the management of the political campaign. In fact, social media provide an effective platform for engaging users in political discussion, which is often used by politicians during the ...
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This article presents the results of a systematic literature review of 127 studies addressing the use of Twitter in election campaigns. In this systematic review, I will discuss the available research with regard to findings on the use of Twitter by parties, candidates, and publics during election campaigns and during mediated campaign events.
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As this review essay has suggested, recent research has begun to take a toll on the long-dominant "minimalist" view of campaigns. A major stimulus to this progression has been the increasing volume of traffic between political science, communications, and allied disciplines.
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The 2016 and the 2020 elections provide a relevant comparative framework for a set of reasons. First, while social media is no longer a novelty, scholars raised concerns about the 2016 campaigns and how platforms were used (and at times, abused) by political actors (Kreiss et al., 2018).Second, both elections featured Donald Trump—a challenger in 2016 and the incumbent in 2020—whose use of ...
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The role of political campaigns on electoral outcomes has been an intensively studied issue in electoral research (Rady & Johnston, 2006;Schmitt-Beck & Farrell, 2002). Campaigns are supposed to ...
8. The presidency and presidential politics
Views about presidential campaigns. Americans are critical of recent presidential campaigns in negative ways, with seven-in-ten or more saying they haven't been informative (71%), have focused on the wrong issues (77%), have lasted too long (72%) and haven't featured nominees who were good candidates (78%). Americans are more evenly split ...
Political communication and campaigning in India: opportunities for
1. Farrell, Kolody and Medvic, "Parties and Campaign Professionals"; Gibson and Römmele, "A Party Centered Theory of Professionalized Campaigning"; Johnson, No Place for Amateurs: How Political Consultants Are Reshaping American Democracy; Lees-Marshment and Lilleker, "Knowledge Sharing and Lesson Learning"; and Gibson and Römmele, "Measuring the Professionalization of ...
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