scholarly journals Linguistic Markers of Self-Disclosure: Using YouTube Coming Out Videos to Study Disclosure Language

2020 ◽  
Author(s):  
Patrick Charles Doyle ◽  
William Keith Campbell
Keyword(s):  
Coming Out ◽  
Text Analysis ◽  
Word Count ◽  
Analysis Software ◽  
Hiv Diagnosis ◽  
Self Disclosure ◽  
Linguistic Markers ◽  
Behavior Outcomes ◽  
Youtube Videos ◽  
Linguistic Inquiry

Traditional attempts at measuring self-disclosure actually measure self-reported perceptions of disclosure, which is conflated with individual difference characteristics, or rely on trained coders, which is time-consuming. Across a pilot and two studies and using a known-groups paradigm with repeated transcripts from YouTube videos in which creators express a concealable stigmatized identity(LGBTQ, HIV diagnosis, or mental illness), we measured self-disclosure with the Linguistic Inquiry and Word Count and found support for the utility of text-based analyses for operationalization of disclosure. This output was correlated with trained coders’ ratings and was effective for predicting audience behavior outcomes, including reciprocal disclosure. Finally, we discuss the utility of text-analysis software for theoretical and applied work.

Linguistic Markers in Women’s Discussions on Miscarriage and Abortion Illustrate Psychological Responses to Their Experiences

2020 ◽  
pp. 0261927X2096564
Author(s):  
Kate G. Blackburn ◽  
Weixi Wang ◽  
Rhea Pedler ◽  
Rachel Thompson ◽  
Diana Gonzales
Keyword(s):  
Emotion Regulation ◽  
Text Analysis ◽  
The Other ◽  
Psychological Responses ◽  
Word Count ◽  
Future Directions ◽  
Trauma Research ◽  
Linguistic Markers ◽  
Linguistic Inquiry Word Count ◽  
Linguistic Inquiry

This study analyzed thousands of women’s online conversations in relation to their miscarriage or abortion experiences, classified as unplanned and planned traumas, respectively. Linguistic Inquiry Word Count text analysis revealed that people experiencing a planned trauma use distancing language patterns in higher frequency and engage in emotion regulation more than those who experienced trauma unexpectedly. On the other hand, planned trauma conversations used more self-focused language and more social-based language. Implications and future directions for trauma research are discussed.

scholarly journals Linguistic sleuthing for innovators

2020 ◽  
Author(s):  
Francesca Greco ◽  
Ken Riopelle ◽  
Francesca Grippa ◽  
Andrea Fronzetti Colladon ◽  
Julia Gluesing
Keyword(s):  
Research Methodology ◽  
Text Analysis ◽  
Statistical Difference ◽  
Word Count ◽  
Analysis Software ◽  
Search Results ◽  
Identification Method ◽  
Academic Researchers ◽  
Innovative Thinking ◽  
Linguistic Inquiry

Abstract For centuries “innovation” has been a topic of book authors and academic researchers as documented by Ngram and Google Scholar search results. In contrast, “innovators” have had substantially less attention in both the popular domain and the academic domain. The purpose of this paper is to introduce a text analysis research methodology to linguistically identify “innovators” and “non-innovators” using Hebert F. Crovitz’s 42 relational words. Specifically, we demonstrate how to combine the use of two complementary text analysis software programs: Linguistic Inquiry and Word Count and WORDij to simply count the percent of use of these relational words and determine the statistical difference in use between “innovators” and “non-innovators.” We call this the “Crovitz Innovator Identification Method” in honor of Herbert F. Crovitz, who envisioned the possibility of using a small group of 42 words to signal “innovation” language. The Crovitz Innovator Identification Method is inexpensive, fast, scalable, and ready to be applied by others using this example as their guide. Nevertheless, this method does not confirm the viability of any innovation being created, used or implemented; it simply detects how a person’s language signals innovative thinking. We invite other scholars to join us in this linguistic sleuthing for innovators.

A Text Analysis of the Poems of Sylvia Plath

2011 ◽  
Vol 109 (1) ◽  
pp. 73-76 ◽  
Author(s):  
David Lester ◽  
Stephanie McSwain
Keyword(s):  
Computer Program ◽  
Text Analysis ◽  
Positive Emotions ◽  
Sylvia Plath ◽  
Word Count ◽  
Linguistic Inquiry

Changes in the words used in the poems of Sylvia Plath were examined using the Linguistic Inquiry and Word Count, a computer program for analyzing the content of texts. Major changes in the content of her poems were observed over the course of Plath's career, as well as in the final year of her life. As the time of her suicide came closer, words expressing positive emotions became more frequent, while words concerned with causation and insight became less frequent.

scholarly journals Creating and Testing Specialized Dictionaries for Text Analysis

2019 ◽  
Vol 6 (1) ◽  
pp. 65-75 ◽  
Author(s):  
Роман Тарабань ◽  
Джесіка Піттман ◽  
Талін Налбандян ◽  
Winson Fu Zun Yang ◽  
Вільям Марсі ◽  
...  
Keyword(s):  
Text Analysis ◽  
Latent Dirichlet Allocation ◽  
Naive Bayes ◽  
Qualitative Content Analysis ◽  
Naïve Bayes ◽  
Computer Application ◽  
Word Count ◽  
Constructive Feedback ◽  
Discourse Processes ◽  
Linguistic Inquiry

Practitioners in many domains–e.g., clinical psychologists, college instructors, researchers–collect written responses from clients. A well-developed method that has been applied to texts from sources like these is the computer application Linguistic Inquiry and Word Count (LIWC). LIWC uses the words in texts as cues to a person’s thought processes, emotional states, intentions, and motivations. In the present study, we adopt analytic principles from LIWC and develop and test an alternative method of text analysis using naïve Bayes methods. We further show how output from the naïve Bayes analysis can be used for mark up of student work in order to provide immediate, constructive feedback to students and instructors. References Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research 3, 993-1022. Boot, P., Zijlstra, H., & Geenen, R. (2017). The Dutch translation of the Linguistic Inquiry and Word Count (LIWC) 2007 dictionary. Dutch Journal of Applied Linguistics, 6(1), 65-76. Chung, C. K., & Pennebaker, J. W. (2008). Revealing dimensions of thinking in open-ended self-descriptions: An automated meaning extraction method for natural language. Journal of research in personality, 42(1), 96-132. Hsieh, H-F., & Shannon, S. E. (2005).Three approaches to qualitative content analysis. Qualitative health research, 15(9), 277-1288. Kintsch, W. (1998). Comprehension: A paradigm for cognition. New York: Cambridge University Press. Landauer, T. K., Foltz, P. W., & Laham, D. (1998). An introduction to latent semantic ana­lysis. Discourse processes, 25(2-3), 259-284. Lund, K., & Burgess, C. (1996). Producing high-dimensional semantic spaces from lexical co-occurrence. Behavior Research Methods, Instruments, & Computers, 28(2), 203-208. Massó, G., Lambert, P., Penagos, C. R., & Saurí, R. (2013, December). Generating New LIWC Dictionaries by Triangulation. In Asia Information Retrieval Symposium (pp. 263-271). Springer, Berlin, Heidelberg. Newman, M., Groom, C.J., Handelman, L.D., & Pennebaker, J.W. (2008). Gender differences in language use: An analysis of 14,000 text samples. Discourse Processes, 45(3), 211-236. Pennebaker, J.W., Boyd, R.L., Jordan, K., & Blackburn, K. (2015). The development and psychometric properties of LIWC 2015. Austin, TX: University of Texas at Austin. Tausczik, Y. R., & Pennebaker, J. W. (2010). The psychological meaning of words: LIWC and computerized text analysis methods. Journal of language and social psychology, 29(1), 24-54. Van Wissen, L., & Boot, P. (2017, September). An Electronic Translation of the LIWC Dictionary into Dutch. In: Electronic lexicography in the 21st century: Proceedings of eLex 2017 Conference. (pp. 703-715). Lexical Computing.

Using LIWC to Analyze Participants’ Psychological Processing in Accounting JDM Research

2021 ◽  
Author(s):  
Sanaz Aghazadeh ◽  
Kris Hoang ◽  
Bradley Pomeroy
Keyword(s):  
Text Analysis ◽  
Affective States ◽  
Word Count ◽  
Psycholinguistic Research ◽  
Psychological Constructs ◽  
Written Responses ◽  
Linguistic Inquiry Word Count ◽  
Control Procedures ◽  
Linguistic Inquiry ◽  
Psychological Processing

This paper provides methodological guidance for judgment and decision-making (JDM) researchers in accounting who are interested in using the Linguistic Inquiry Word Count (LIWC) text analysis program to analyze research participants’ written responses to open-ended questions. We discuss how LIWC’s measures of psychological constructs were developed and validated in psycholinguistic research. We then use data from an audit JDM study to illustrate the use of LIWC to guide researchers in identifying suitable measures, performing quality control procedures, and reporting the analysis. We also discuss research design considerations that will strengthen the inferences drawn from LIWC analysis. The paper concludes with examples where LIWC analysis has the potential to reveal participants’ deep, complex, effortful psychological processing and affective states from their written responses.

Understanding the Role of Language in Conflict

2019 ◽  
Vol 64 (1) ◽  
pp. 97-117 ◽  
Author(s):  
William A. Donohue ◽  
Qi Hao ◽  
Richard Spreng ◽  
Charles Owen
Keyword(s):  
Text Analysis ◽  
Word Count ◽  
Data Set ◽  
Open Source Data ◽  
Communication Events ◽  
Source Data ◽  
Linguistic Inquiry ◽  
Mind Set ◽  
The Mind

The purpose of this article is to illustrate innovations in text analysis associated with understanding conflict-related communication events. Two innovations will be explored: LIWC (Linguistic Inquiry and Word Count), the text modeling program from the open-source data analysis software program R, and SPSS Modeler. The LIWC analysis revisits the 2009 study by Donohue and Druckman and the 2014 study by Donohue, Liang, and Druckman focusing on text analyses of the Oslo I Accords between the Palestinians and Israelis to illustrate this approach. The R and SPSS modeling of text analysis use the same data set as the LIWC analysis to provide a different set of pictures associated with each leader’s rhetoric during the period in which the Oslo I accords were being negotiated. Each innovation provides different insights into the mind-set of the two groups of leaders as the secret talks were emerging. The implications of each approach in establishing an understanding of the communication exchanges are discussed to conclude the article.

scholarly journals Machine-translated texts as an alternative to translated dictionaries for LIWC

2021 ◽  
Author(s):  
Peter Boot
Keyword(s):  
Machine Translation ◽  
Text Analysis ◽  
Analysis Program ◽  
Word Count ◽  
Alternative Approach ◽  
Linguistic Inquiry ◽  
Better Than ◽  
Dictionary Translation

Linguistic Inquiry and Word Count (LIWC) is a text analysis program developed by James Pennebaker and colleagues. At the basis of LIWC is a dictionary that assigns words to categories. This dictionary is specific to English. Researchers who want to use LIWC on non-English texts have typically relied on translations of the dictionary into the language of the texts. Dictionary translation, however, is a labour-intensive procedure. In this paper, we investigate an alternative approach: to use Machine Translation (MT) to translate the texts that must be analysed into English, and then use the English dictionary to analyse the texts. We test several LIWC versions, languages and MT engines, and consistently find the machine-translated text approach performs better than the translated-dictionary approach. We argue that for languages for which effective MT technology is available, there is no need to create new LIWC dictionary translations.

scholarly journals Linguistic Markers of Grandiose Narcissism: A LIWC Analysis of 15 Samples

2019 ◽  
Vol 38 (5-6) ◽  
pp. 773-786 ◽  
Author(s):  
Nicholas S. Holtzman ◽  
Allison M. Tackman ◽  
Angela L. Carey ◽  
Melanie S. Brucks ◽  
Albrecht C. P. Küfner ◽  
...  
Keyword(s):  
Language Use ◽  
First Person ◽  
Word Count ◽  
Grandiose Narcissism ◽  
Second Person ◽  
Linguistic Markers ◽  
Linguistic Inquiry

Narcissism is unrelated to using first-person singular pronouns. Whether narcissism is linked to other language use remains unclear. We aimed to identify linguistic markers of narcissism. We applied the Linguistic Inquiry and Word Count to texts ( k = 15; N = 4,941). The strongest positive correlates were using words related to sports, second-person pronouns, and swear words. The strongest negative correlates were using anxiety/fear words, tentative words, and words related to sensory/perceptual processes. Effects were small (each | r| < .10).

The Stuff That Verbal Person-Centered Support Is Made of: Identifying Linguistic Markers of More and Less Supportive Conversations

2018 ◽  
Vol 37 (6) ◽  
pp. 656-679 ◽  
Author(s):  
Kaitlin E. Cannava ◽  
Andrew C. High ◽  
Susanne M. Jones ◽  
Graham D. Bodie
Keyword(s):  
Cognitive Process ◽  
Social Process ◽  
Word Count ◽  
Support Providers ◽  
Linguistic Markers ◽  
Computerized Analysis ◽  
Linguistic Inquiry ◽  
Different Levels ◽  
Linguistic Content

Although the functions of messages varying in verbal person centeredness (PC) are well-established, we know less about the linguistic content that differentiates messages with distinct levels of PC. This study examines the lexicon of different levels of PC comfort and seeks to ascertain whether computerized analysis can complement human coders when coding supportive conversations. Transcripts from support providers trained to enact low, moderate, or high levels of PC were subjected to the Linguistic Inquiry and Word Count (LIWC) dictionary. Results reveal that several categories in the LIWC dictionary vary systematically as a function of conversational PC level. LIWC categories, particularly pronouns, social process, cognitive process, anxiety, and anger words, reliably predict which level of the PC hierarchy an interaction represents based on whether a conversation was designed to be high, moderate, or low in PC. The implications are discussed in the context of the lexicon of conversations that vary in PC.

scholarly journals Linguistic Markers of Narcissism: An Exploratory LIWC Analysis of 15 Samples

2018 ◽  
Author(s):  
Nicholas S. Holtzman ◽  
Allison Mary Tackman ◽  
Albrecht Kuefner ◽  
Fenne große Deters ◽  
Mitja Back ◽  
...  
Keyword(s):  
Language Use ◽  
First Person ◽  
Word Count ◽  
Second Person ◽  
Linguistic Markers ◽  
Linguistic Inquiry

Narcissism is unrelated to using first-person singular pronouns. Whether narcissism is linked to other language use remains unclear. We aimed to identify linguistic markers of narcissism. We applied the Linguistic Inquiry and Word Count to texts (k = 15; N = 4,941). The strongest positive correlates were: using words related to sports, second-person pronouns, and swear words. The strongest negative correlates were: using anxiety/fear words, tentative words, and words related to sensory/perceptual processes. Effects were small (each |r| &lt; .10).

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