quantitative research paper related to humss pdf strand

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A Perception-Based Curricular Review on the K to 12 HUMSS Strand Curriculum

Author: Edwin B. Estrera, University of the Philippines Cebu, Philippines Email: [email protected] Published: November 27, 2020 https://doi.org/10.22492/ije.8.4.02

Citation : Estrera, E. B. (2020). A Perception-Based Curricular Review on the K to 12 HUMSS Strand Curriculum. IAFOR Journal of Education: Studies in Education , 8 (4). https://doi.org/10.22492/ije.8.4.02

This study examines the perception of Humanities and Social Science teachers among public Senior High Schools in the Department of Education’s Humanities and Social Sciences strand in the Philippines. It uses Erden’s element-based model of evaluation by considering the alignment to the goals of the Humanities and Social Sciences disciplines, the purpose, and core courses of the program, and the teaching-learning process. It also uses Tyler’s Rationale as frameworks in assessing the curriculum. Likewise, the study examines the problems and difficulties in curricular implementation. Upon administering a survey to 25 Humanities and Social Science teachers among four public senior high schools, data revealed that the respondents perceived the curriculum goals, and the purpose of the program as highly observed, while the core courses of the program and teaching-learning process were satisfactorily observed in the curriculum. Also, sex and age were not factors in their level of assessment of Humanities and Social Science goals. The problems and difficulties encountered by teachers included unbalanced time allocation of learning competencies, lack of available learning materials, and lack of specialized teachers. Based on the findings, it is suggested that the government provide stronger teacher support programs to address the gap in curriculum implementation. The K to 12 program also needs a full review, as the study only provides a presurvey to more significant institutional issues. While the Humanities and Social Science curriculum appears aligned with the goals of their disciplines, and to the country’s educational goals, its realization still depends upon the teachers’ implementation in the classroom level.

basic education, HUMSS, basic education, educational reform, teacher perceptionIntellectual Schools, Nazarbayev, perception, teacher professionalism

• Navigation: Journal Home Submit Manuscript JoE Editors & Reviewers Aims & Scope / About Issues Review / Publication Process Author Guidelines Publication Ethics

Benefits of HUMSS Strand to Develop Self-Esteem of Grade 11 Students

• Dindie Grace Magana
• Elizabeth Barce
• Necel Ellema
• Jasmin Jetajobe
• Roymar Angelo Soriso
• Midel Mirasol

Self-esteem is a person’s overall self-worth or personal value; it is how people appreciate and love themselves, which is often seen as a personality trait. This also means being stable and enduring; people who have low self-esteem cannot take up the world filled with optimism and determination or have difficulties in interacting with other people to help develop themselves by determining the benefits of choosing the Humanities and Social Sciences (HUMSS) Strand to develop the self-esteem of the students through different activities that HUMSS offers. The researchers used the descriptive method to describe the characteristics of the population or phenomenon that is being studied to provide information with the use of a survey questionnaire provided by the researchers for the respondents to easily express their thoughts and ideas about a certain topic or study. This study was conducted at Bestlink College of the Philippines. The data gathered from the questionnaires served as the primary data, and the information gathered from the literature and studies was the secondary data. The results showed that HUMSS is beneficial in developing ones’ self-esteem based on a survey questionnaire in terms of academic performance with 3.96% where the students are encouraged to participate in class. Extracurricular activities had 3.95%, which helped the students to develop their confidence by joining any sport and club at school. Interpersonal skills had 3.66%, where the students can confidently express appropriate emotion to others. Developing student self-esteem will help them to gain more knowledge, improve their research skills, and teach them how to communicate with other people without uncertainty. The study found that academic performance, extracurricular activities, and interpersonal skills were significantly affected to develop the self-esteem of students.

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HumSS Research Topics – Humanities & Social Sciences Topics

Humss (Humanities & Social Sciences) is an interesting field of study featuring college courses like Journalism, Communication Arts, and Education. Research projects for humss revolve around intellect, change, societal issues, and human conditions. Finding humss research topics is not as hard as it seems. For instance, you should know that research topics for humss differ from science topics because scholars are more interested in questions than answers. Also, your topics should be interesting and controversial to capture your readers. Choosing the right research topic about humss will simplify finding content and buy research paper .

Exciting Research Topic about Humss Strand

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Humss strand is one of the courses offered to students who want to pursue college degrees in education, liberal arts, or other social sciences. Choose any of the exciting topics below for your high school humss research project:

• The impact of aging on social interactions
• Anti-vaccination is the latest trending social movement
• Remote working is the latest trend in the corporate world
• What is the root cause of social media addiction?
• Is there a valid connection between social class and success?
• How much control should parents have over their kid’s social life?
• What is the appropriate age to start teaching students about gender studies?
• The impact of single parenting on a child’s social connection

Choosing interesting research about humss strand will help you stand out from the rest and impact the quality of your paper. Below are some thought-provoking humss research topics you can explore:

• Feminism in the corporate place: a critical analysis
• Does parental control influence a child’s social personality?
• Conventional families: how do they impact a child’s development?
• Growing up in an LGBTQ family: How does it influence a child’s sexual identity?
• The effects of social media on teens and youths
• The outcomes of social networking
• Are unconventional families beneficial for child development?
• Young motherhood: How does it impact a child’s wellbeing?

Are you a humss student looking for good topics for your research paper about the humss strand? Below are some ideas worth considering:

• The impacts of foreign education on professional growth
• The link between economic prosperity and the feeling of patriotism among citizens
• The right to privacy: a critical analysis in the digital era
• Social media preferences among different age and social groups
• Does social media increase or reduce loneliness among individuals?
• Is there a link between social media addiction and age?
• How important is adding food education to the modern education curriculum?
• A case study on the correlation between food and national identity

Whether you specialize in education, media, communication, liberal arts, or other social sciences, your humss research topic will influence your grade. You can choose an example of a research title about humss strand from the suggestions below:

• The changes that feminism has bought on gender roles at home
• The social perception of vegetarianism in different cultures
• Spirituality and raw food diets: what is the connection?
• Factors that affect students’ productivity during their free time
• Social media activism: is it as effective as old-fashioned street protests?
• Why you should take body language seriously during online interviews
• Twitter: How it shifted from an ordinary social media platform to a political platform
• Gender bias: concept definition

You can make your essay or research paper stand out and earn good marks by selecting quality topics. Pick a topic about humss strand from the ideas below:

• How has the digital era negatively influenced the social concept of morality?
• The impact of social media on people’s ability to understand others’ feelings
• Justice and wars: Who is the right person to judge?
• The influence of the mass media on political attitudes and statistics
• Awareness of public choice: Why is it so important?
• Framing: What is its role in the political sector?
• The root cause of reduced voter turnout: A case study of the United States
• What impact do advertisements have on political views?

Quantitative research involves collecting and analyzing data from deductive approaches like questionnaires while focusing on testing a specific theory. Finding a good top quantitative research topic about humss strand can make your study easier and more effective. Here are some noteworthy ideas:

• The electoral process in Michigan (specify location): A quantitative analysis
• The cultural practices related to childbirth rates in third-world countries
• An evaluation of the factors promoting teenage pregnancies in the 21 st century
• The rate of teenage pregnancies in third-world countries Vs. first-world countries
• Mass Media: Its impact on political statistics and voter behaviors
• How critical are self-defending networks?
• A critical analysis of the voter turnout in the recent elections in (state country or state)
• Can technology upgrades influence relationships?

Quantitative research involves data collection using questionnaires, interviews, and online or offline surveys. Below are some interesting topics you can write about in this area:

• How can cyber-crimes affect human lives?
• Racial bullying on social media: a critical analysis
• Drug testing in the workplace: is it necessary?
• How practical are modern components of sex education in High Schools?
• The impacts of the government controlling women’s reproductive rights?
• The root cause of stereotypes in society
• How gambling feels to an addict
• Group social education: What are its benefits?

Qualitative research depends on data obtained through first-hand observation, recordings, or focus groups. You can pick a good qualitative research topic about the humss strand from the following examples:

• Why do many students perform poorly in sciences?
• The rate of college acceptance in developing nations
• Academic preparedness of university students in the United States
• Victims of bullying in schools: a case study of (state a specific school or location)
• The relationship between android and apple products
• Online digital marketing: what is it all about?
• Virtual reality worlds: their role in transforming society
• Should kids under four years get a preschool education?

Humss is a vast field with thousands of research topic options for students with various specialties. Choose a research topic related to humss from the following option:

• The cultural construct of the masculine and feminine identity
• How individuals interact with various physical elements
• Inter-nation relationships: what challenges hinder healthy relationships between nations?
• The value of language in societal success
• How has the political sector in the United States evolved in the past century?
• The implications of philosophical studies for the growth of a society
• Diversity: how does it make society better?
• Peace and harmony: why are differences vital for peace and harmony?

Choosing a research title about humss can be challenging if you have not done one before. For this reason, we prepared the following title ideas:

• Religious discrimination in the digital era
• The conflict between religion and the digital era
• Social relations between Islam and Christianity
• The unification of Germany: a look at the process
• The great migration: a critical analysis
• Feminism movements and their impacts on society
• Does studying social sciences give you a better chance of success?
• The impact of the Ottoman Empire on socialization

When choosing the perfect research topics for humss, you should consider your specialization and research type (qualitative or quantitative). Here are some examples to consider:

• The impact of the pandemic on people’s social media behaviors
• Internet purchases: how sales taxes affect them
• The significance of understanding history in studying humanities
• Are all human beings anatomically similar?
• The role of humanities in higher learning institutions
• Do humanities help students achieve higher analytical and problem-solving skills?
• Why do universities require multiple humanities courses?
• The influence of William Shakespeare’s plays on modern literature

Focusing on a social issue is the best way to get a unique and interesting research topic for humss students. Here are some examples:

• The beginning of the feminist era
• How has the pandemic influenced the education sector?
• The implications of social media on religion and culture
• The impact of healthy doctor-patient relationships on the healthcare sector
• The relationship between social media interaction and personality development
• How is the digital era affecting the elderly in society?
• Modern inter-nation wars: implications of the war between Ukraine and Russia
• Is the United States still the most powerful country in the world?

Writing a research paper is as easy or hard as the topic you choose. Here are some humss research title ideas:

• The relationship between empathy and the experience of illness
• The impact of media on the study of medicine
• The relationship between social media and education
• Is diversity vital in society?
• The impact of gun violence on school attendance
• Modern aspects of poetry: a critical analysis
• The COVID-19 pandemic’s influence on social media addiction
• Social media addiction and age: what is the correlation?

Below are some key ideas on the topic about humss you can focus your research on:

• How do parents influence their children’s social behaviors
• Social education: how it helps students develop
• How do teachers include their student’s course choices?
• Boarding schools for boys Vs. boarding schools for girls
• How has social media influenced people’s views of celebrities?
• The role of social influencing in purchasing behaviors
• When is military force justifiable
• Should community service be mandatory for all students?

Your research title for humss will help you determine your paper’s outline and research methods. Below are some incredible topics you should consider:

• Do advertisements still influence people’s purchasing behaviors?
• Social media marketing Vs. conventional advertising
• Dual nationality: its impact on political views
• The implications of personality on political attitudes
• The correlation between collective action and public policies
• Do changes in public policies influence public opinions?
• The correlation between law-making and bureaucracy
• The influence of public policy on innovation

A concept paper provides your research’s purpose, background, and outline. Therefore, choosing the perfect topic is vital. Below are some ideas to look into:

• The US-Mexico Border Dilemma: an analysis
• Perfectionist policy: concept definition
• Why are more people turning to digital work in the 21 st century?
• Ethical issues in the dialysis of homelessness
• Effects of stigma among leaders
• How is technology reshaping the future of social interaction?
• Importance of practical counseling sessions for Psychology students
• How can parents cope with their kids’ disabilities

A good humss research paper should have a background research topic. Here are some great examples:

• The root cause of international cyber-attacks
• The history of Europe and its importance in humanities studies
• The root of punishment in households
• Should religious freedom be granted to kids under 18 years?
• The growth and spread of Islam in African nations
• How missionaries shaped Africans’ views on religion
• The impact of the Great Awakenings on US history
• The growth of Pentecostalism in Latin nations

Quantitative research is a dominant research technique in social sciences, where students can focus on topics like politics and elections. Here are some good ideas:

• The effectiveness of home care against nursing homes
• The development of telehealth in the 21 st century
• How effective are cardiovascular treatments?
• The link between mortality rates and gender
• The changes in critic ratings and their impact on equity returns
• Do people’s decision-making processes depend on their subconscious?
• Impact of racism on mental health
• Social anxiety triggers in youths

Let’s Help You

The humss strand is so vast that you can easily find a topic depending on your area of specialization. You can also pick a topic based on interesting social issues . Also, you must be keen on selecting a quality research title that stands out and makes your writing easier. If you feel overwhelmed choosing a title or writing a humss paper, we are here to help you. Talk to us now!

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HUMANITIES AND SOCIAL SCIENCES (HUMSS) Strand

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Qualitative and quantitative research in the humanities and social sciences: how natural language processing (NLP) can help

• Published: 23 September 2021
• Volume 56 , pages 2751–2781, ( 2022 )

Cite this article

• Roberto Franzosi   ORCID: orcid.org/0000-0001-8367-5190 1 ,
• Wenqin Dong 2 &
• Yilin Dong 2  

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The paper describes computational tools that can be of great help to both qualitative and quantitative scholars in the humanities and social sciences who deal with words as data. The Java and Python tools described provide computer-automated ways of performing useful tasks: 1. check the filenames well-formedness; 2. find user-defined characters in English language stories (e.g., social actors, i.e., individuals, groups, organizations; animals) (“find the character”) via WordNet; 3. aggregate words into higher-level aggregates (e.g., “talk,” “say,” “write” are all verbs of “communication”) (“find the ancestor”) via WordNet; 4. evaluate human-created summaries of events taken from multiple sources where key actors found in the sources may have been left out in the summaries (“find the missing character”) via Stanford CoreNLP POS and NER annotators; 5. list the documents in an event cluster where names or locations present close similarities (“check the character’s name tag”) using Levenshtein word/edit distance and Stanford CoreNLP NER annotator; 6. list documents categorized into the wrong event cluster (“find the intruder”) via Stanford CoreNLP POS and NER annotators; 7. classify loose documents into most-likely event clusters (“find the character’s home”) via Stanford CoreNLP POS and NER annotators or date matcher; 8. find similarities between documents (“find the plagiarist”) using Lucene. These tools of automatic data checking can be applied to ongoing projects or completed projects to check data reliability. The NLP tools are designed with “a fourth grader” in mind, a user with no computer science background. Some five thousand newspaper articles from a project on racial violence (Georgia 1875–1935) are used to show how the tools work. But the tools have much wider applicability to a variety of problems of interest to both qualitative and quantitative scholars who deal with text as data.

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Natural Language Processing

The Use of Artificial Intelligence in Writing Scientific Review Articles

Melissa A. Kacena, Lilian I. Plotkin & Jill C. Fehrenbacher

The disaster of misinformation: a review of research in social media

Sadiq Muhammed T & Saji K. Mathew

On PEA see Koopmans and Rucht ( 2002 ) and (Hutter 2014 ); on PEA and its more rigorous methodological counterpart rooted in a linguistic theory of narrative and rhetoric, Quantitative Narrative Analysis (QNA), see Franzosi ( 2010 ).

See, for instance, Franzosi’s PC-ACE (Program for Computer-Assisted Coding of Events) at www.pc-ace.com (Franzosi 2010 ).

For recent surveys, see Evans and Aceves ( 2016 ), Edelmann et al. ( 2020 ).

The GitHub site will automatically install not only all the NLP Suite scripts but also Python and Anaconda required to run the scripts. It also provides extensive help on how to download and install a handful of external software required by some of the algorithms (e.g., Stanford CoreNLP, WordNet). The goal is to make it as easy as possible for non-technical users to take advantage of the tools with minimal investment.

We rely on the Python package openpyxl and ad hoc functions.

The newspaper collections found in Chronicling America of the Library of Congress ( http://chroniclingamerica.loc.gov/newspapers/ ), the Digital Library of Georgia ( http://dlg.galileo.usg.edu/MediaTypes/Newspapers.html?Welcome ), The Atlanta Constitution, Proquest, Readex.

Multiple cross-references are also possible, whereby a document deals with several different events.

Contrary to some protest event projects based on a single newspaper source (e.g., The New York Times in the “Dynamics of Collective Action, 1960–1995” project that involved several social scientists, notably, Doug McAdam, John McCarthy, Susan Olzak, Sarah Soule, and led to dozens of influential publications; see for all McAdam and Su 2002 ), the Georgia lynching project is based on multiple newspaper sources for each event.

Franzosi reports 1,600 distinct entries for subjects and objects and 7,000 for verbs for one of his projects (Franzosi 2010 : 93); similar figures are reported by Ericsson and Simon ( 1996 : 265–266) and Tilly ( 1995 : 414–415).

The most up-to-date numbers of terms are given in https://wordnet.princeton.edu/documentation/wnstats7wn .

A common critique of WordNet is that WordNet is better suited to account for concrete concepts than for abstract concepts. It is much easier to create hyponyms/hypernym relationships between “conifer” as a type of “tree”, a “tree” as a type of “plant”, and a “plant” as a type of “organism”. Not so easy to classify emotions like “fear” or “happiness” into hyponyms/hypernym relationships.

https://projects.csail.mit.edu/jwi/

The WordNet databases comprises both single words or combinations of two or more words that typically come together with a specific meaning (collocations, e.g., coming out, shut down, thumbs up, stand in line, customs duty). Over 80% of terms in the WordNet database are collocations, at least at the time of Miller et al.’s Introduction to WordNet manual (1993, p. 2). For the English language (but WordNet is available for some 200 languages) the database contains a very large set of terms. The most up-to-date numbers of terms are given in https://wordnet.princeton.edu/documentation/wnstats7wn .

Data aggregation is often referred to as “data reduction” in the social sciences and as “linguistic categorization” in linguistics (on linguistic categorization, see Taylor 2004 ; on verbs classification, Levin 1993 ; see also Franzosi 2010 : 61).

On the way up through the hierarchy, the script relies on the WordNet concepts of hypernym – the generic term used to designate a whole class of specific instances (Y is a hypernym of X if X is a (kind of) Y) – and holonym – the name of the whole of which the meronym names is a part. Y is a holonym of X if X is a part of Y.

Collocations are sets of two or more words that are usually together for a complete meaning, e.g., “coming out,” “sunny side up”. Over 80% of terms in the WordNet database are collocations, at least at the time of Miller et al.’s Introduction to WordNet manual (1993, p. 2). For the English language (but WordNet is available for some 200 languages) the database contains a very large set of terms. The most up-to-date numbers of terms in each category are given in https://wordnet.princeton.edu/documentation/wnstats7wn

The 25 top noun synsets are: act, animal, artifact, attribute, body, cognition, communication, event, feeling, food, group, location, motive, object, person, phenomenon, plant, possession, process, quantity, relation, shape, state, substance, time.

The 15 top verb synsets are: body, change, cognition, communication, competition, consumption, contact, creation, emotion, motion, perception, possession, social, stative, weather.

Unfortunately, there is no easy way to aggregate at levels lower than the top synsets. Wordnet is a linked graph where each node is a synset and synsets are interlinked by means of conceptual-semantic and lexical relations. In other words, it is not a simple tree structure: there is no way to tell at which level the synset is located at. For example, the synset “anger” can be traced from top level synset “feeling” and follows the path: feeling—> emotion—> anger. But it can also be traced from top level synset “state” and follows the path: state—> condition—> physiological condition—> arousal—> emotional arousal—> anger. In the first case, “anger” is at level 3 (assuming “feeling” and or other top synsets are level 1). In the second case, “anger” is at level 6. Programmatically, if one gives users more freedom to control the level of aggregating up, it is hard to build a user-friendly communication protocol. If the user wants to aggregate up to level 3 (two levels below the top synset), then should “anger” be considered as a level 3 synset? Does the user want “anger” to be considered as a level 3 synset? Since there is no clear definition of how far away a synset is from the root (top synsets), our algorithm aggregates all the way up to root.

Suppose that you wish to aggregate the verbs in your corpus under the label “violence.” WordNet top synsets for verbs do not include “violence” as a class. Verbs of violence may be listed under body, contact, social. You could use the Zoom IN/DOWN widget of Figure 24 to get a list of verbs in these top synsets, then manually go through the list to select only the verbs of violence of interest. That would mean go through manually the list of 956 verbs in the body class (e.g., to find there the verb “attack,” among others), the 2515 verbs of contact (e.g., to find there the verb “wrestle”), and the 1688 verbs of social (e.g., to find there the verb “abuse”). In total, 5159 distinct verbs. A restricted domain, for example newspaper articles of lynching, may have many fewer distinct verbs, indeed 2027, extracted using the lemma of the POS annotator for all the VB* tags. Whether using the WordNet dictionary (a better solution if the list of verbs of violence has to be used across different corpora) or the POS distinct verb tags, the dictionary list can then be used to annotate the documents in the corpus via the NLP Suite dictionary annotator GUI.

Current computational technology makes available a different approach to creating summaries: an automatic approach where summaries are generated automatically by a computer algorithm, rather than a human (Gambhir and Gupta 2017 ; Lloret and Palomar 2012 ; Nenkova and McKeown 2012 ).

We use the word “compilation”, rather than “summary”, since, by and large, we maintained the original newspaper language (e.g., the word “negro”, rather than “African American”) and original story line, however contrived the story may have appeared to be.

https://stanfordnlp.github.io/CoreNLP/ Manning et al. ( 2014 ).

More specifically, for locations, the NER tags used are: City, State_or_Province, Country. Several other NER values are also recognized and tagged (e.g., Numbers, Percentages, Money, Religion), but they are irrelevant in this context.

The column “List of Documents for Type of Error” may be split in several columns depending upon the number of documents found in error.

The algorithm can process all or selected NER values, comparing the associated word values either within a single event subdirectory or across all subdirectories (or all the files listed in a directory, for that matter).

We calculated the relativity index by using cosine similarity (Singhal 2001 ). We use the two list of NN, NNS, Location, Date, Person, and Organization from the j doc (L1) and from all other j-1 docs (L2) and compute cosine similarity between the two lists. We construct a vector from each list by mapping the word count onto each unique word. Then, relativity index is calculated as the cosine similarity between two vectors and n is the count of total unique words. For instance, L1 is {Alice: 2, doctor: 3, hospital: 1}, and L2 is {Bob:1, hospital: 2}. If we fix the order of all words as {Alice, doctor, hospital, Bob}, then the first vector (V1) is (2, 3, 1, 0), the second vector (V2) is (0, 0, 2, 1), and the length n of the vector is 4. The relativity is the dot product of two vectors divided by the product of two vector lengths. Documents with index of relativity significantly lower than the rest of the cluster are signalled as unlikely to belong to the cluster.

\({\text{relativity}}\;{\text{index}} = \frac{{\sum\nolimits_{i = 1}^{n} {\left( {V1_{i} V2_{i} } \right)} }}{{\sqrt {\sum\nolimits_{i = 1}^{n} {V1_{i}^{2} } } \sqrt {\sum\nolimits_{i = 1}^{n} {V2_{i}^{2} } } }}\)

The relativity index ranges from 0 to 1, where 0 means two documents are totally different, and 1 means two documents have exactly the same list of NN, NNS, Location, Date, Person, and Organization.

The bar chart displays the distribution of most frequent threshold index values as intervals, with most records in the 0.25 ~ 0.29 interval.

It should be noted that the use of the words plagiarism and plagiarist in this context should be taken with a grain of salt. First, the data do not tell us anything about who copied whom, but only that the two different newspapers shared content, wholly or in part; furthermore, the shared content may well have come from an unacknowledged wire service (on the development and spread of news wire services in the United States during the second half of the nineteenth century, see Brooker-Gross 1981 ; on computational tools for plagiarism and authorship attribution, see, for instance, Stein et al. 2011 ).

http://lucene.apache.org/core/downloads.html . For a summary of approaches to document similarities, see Forsyth and Sharoff ( 2014 ).

Other approaches are also available. After all, determining document similarity has been a major research area due to its wide application in information retrieval, document clustering, machine translation, etc. Existing approaches to determine document similarity can be grouped into two categories: knowledge-based similarity and content-based similarity (Benedetti et al., 2019 ).

Knowledge-based similarity approaches extract information from other sources to supplement the corpus, so as to draw from more document features to analyze. For example, Explicit Semantic Analysis (ESA) (Gabrilovich and Markovitch 2007 ) represents documents in high dimensional vectors based on the features extracted from both original articles and Wikipedia articles. Then, similarity of documents is calculated using vector space comparison algorithm. Since our main focus in this work is to detect plagiarism among texts in the same corpus, knowledge-based similarity approaches are not very fruitful.

Content-based similarity approaches focus on using only textual information contained in documents. Popular proposed techniques in this fields are Vector Space Models (Turney and Pantel 2010 ), probabilistic models such as Okapi BM-25 (Robertson and Zaragoza 2009 ). These methods all transform documents into some form of representations, and then either do a vector space comparison or query search match on the constructed representations.

document_duplicates.txt.

Users can specify different spans of temporal aggregation (e.g., year, quarter/year, month/year).

In this specific application, documents are newspapers where document name refers to the name of the paper (e.g., The New York Times) and document instance refers to a specific newspaper article (e.g., The New York Times_12-11-1912_1, referring to a The New York Times of December 11, 1912 on page 1). But the document name could refer to an ethnographic interview with document instance referring to an interviewer’s ID (by name or number), an interview’s location, time, or interviewee (by name or ID number).

The numbers in each row of the table add up to approximately the total number of newspaper articles in the corpus. This number of not exact due to the way the Lucene function “find top similar documents” computes similar documents with discrepancies numbering in the teens.

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See Figs. 23 , 24 and 25

Screenshot of the Graphical User Interface (GUI) for the filename checker

Graphical User Interface (GUI) for WordNet options

Graphical User Interface (GUI) for Word Similarities

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Franzosi, R., Dong, W. & Dong, Y. Qualitative and quantitative research in the humanities and social sciences: how natural language processing (NLP) can help. Qual Quant 56 , 2751–2781 (2022). https://doi.org/10.1007/s11135-021-01235-2

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Accepted : 02 September 2021

Published : 23 September 2021

Issue Date : August 2022

DOI : https://doi.org/10.1007/s11135-021-01235-2

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