Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-22T03:01:37.011Z Has data issue: false hasContentIssue false

14 - Language Research in Social Personality Psychology

from Part III - Deep Dives on Methods and Tools for Testing Your Question of Interest

Published online by Cambridge University Press:  12 December 2024

By
Molly E. Ireland  and
James W. Pennebaker
Edited by
Harry T. Reis ,
Tessa West  and
Charles M. Judd
Harry T. Reis
Affiliation:
University of Rochester, New York
Tessa West
Affiliation:
New York University
Charles M. Judd
Affiliation:
University of Colorado Boulder
Get access

Summary

Language is the natural currency of most social communication. Until the emergence of more powerful computational methods, it simply was not feasible to measure its use in mainline social psychology. We now know that language can reveal behavioral evidence of mental states and personality traits, as well as clues to the future behavior of individuals and groups. In this chapter, we first review the history of language research in social personality psychology. We then survey the main methods for deriving psychological insights from language (ranging from data-driven to theory-driven, naturalistic to experimental, qualitative to quantitative, holistic to granular, and transparent to opaque) and describe illustrative examples of findings from each approach. Finally, we present our view of the new capabilities, real-world applications, and ethical and psychometric quagmires on the horizon as language research continues to evolve in the future.

Keywords

languagecomputational linguisticshistory of psychologyfunction wordscontent wordsmental healthmental statesgroup processespersonality traitsAI
Type
Chapter
Information
Handbook of Research Methods in Social and Personality Psychology , pp. 322 - 348
Publisher: Cambridge University Press
Print publication year: 2024

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F.L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S. and Avila, R. (2023). GPT-4 technical report. arXiv preprint arXiv:2303.08774.Google Scholar
Acker, A., and Kreisberg, A. (2020). Social media data archives in an API-driven world. Archival Science, 20, 105–123.CrossRefGoogle Scholar
Adams, K. (2022). SALLEE documentation. Online Receptiviti Inc. documentation, at https://docs.receptiviti.com/frameworks/emotions.Google Scholar
Agnew, C. R., van Lange, P. A., Rusbult, C. E., and Langston, C. A. (1998). Cognitive interdependence: Commitment and the mental representation of close relationships. Journal of Personality and Social Psychology, 74(4), 939–954.CrossRefGoogle Scholar
Allport, G. W. (1953). The psychological nature of personality. The Personalist, 34(4), 347–357.Google Scholar
Allport, G. W., and Odbert, H. S. (1936). Trait-names: A psycho-lexical study. Psychological Monographs, 47(1), i–171.CrossRefGoogle Scholar
Ashokkumar, A., and Pennebaker, J. W. (2022). Social media conversations reveal large psychological shifts caused by COVID-19’s onset across US cities. Science Advances, 7(39), eabg7843, https://www.science.org/doi/10.1126/sciadv.abg7843.CrossRefGoogle Scholar
Back, M. D., Küfner, A. C., and Egloff, B. (2010). The emotional timeline of September 11, 2001. Psychological Science, 21, 1417–1419.CrossRefGoogle ScholarPubMed
Back, M. D., Küfner, A. C., and Egloff, B. (2011). “Automatic or the people?” Anger on September 11, 2001, and lessons learned for the analysis of large digital data sets. Psychological Science, 22, 837–838.CrossRefGoogle Scholar
Baddeley, J. L., Pennebaker, J. W., and Beevers, C. G. (2013). Everyday social behavior during a major depressive episode. Social Psychological and Personality Science, 4, 445–452.CrossRefGoogle Scholar
Badr, H., Milbury, K., Majeed, N., Carmack, C. L., Ahmad, Z., and Gritz, E. R. (2016). Natural language use and couples’ adjustment to head and neck cancer. Health Psychology, 35(10), 1069–1080.CrossRefGoogle ScholarPubMed
Bauer, J. J., and McAdams, D. P. (2010). Eudaimonic growth: Narrative growth goals predict increases in ego development and subjective well-being 3 years later. Developmental Psychology, 46(4), 761–772.CrossRefGoogle ScholarPubMed
Bell, A., Brenier, J. M., Gregory, M., Girand, C., and Jurafsky, D. (2009). Predictability effects on durations of content and function words in conversational English. Journal of Memory and Language, 60(1), 92–111.CrossRefGoogle Scholar
Benjamini, Y., and Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B, 57, 289–300.CrossRefGoogle Scholar
Berger, J., and Packard, G. (2018). Are atypical things more popular? Psychological Science, 29(7), 1178–1184.CrossRefGoogle ScholarPubMed
Bhatia, S. (2017). The semantic representation of prejudice and stereotypes. Cognition, 164, 46–60.CrossRefGoogle ScholarPubMed
Bhatt, A. M., Goldberg, A., and Srivastava, S. B. (2021). A language-based method for assessing symbolic boundary maintenance between social groups. Sociological Methods & Research, 51(4), 00491241221099555.Google Scholar
Biderman, M. D., Nguyen, N. T., Cunningham, C. J., and Ghorbani, N. (2011). The ubiquity of common method variance: The case of the Big Five. Journal of Research in Personality, 45, 417–429.CrossRefGoogle Scholar
Blei, D. M., Ng, A. Y., and Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.Google Scholar
Bock, J. K. (1986). Syntactic persistence in language production. Cognitive Psychology, 18, 355–387.CrossRefGoogle Scholar
Bowen, J. D., Winczewski, L. A., and Collins, N. L. (2017). Language style matching in romantic partners’ conflict and support interactions. Journal of Language and Social Psychology, 36, 263–286.CrossRefGoogle Scholar
Boyd, R. L., Ashokkumar, A., Seraj, S., and Pennebaker, J. W. (2022). The Development and Psychometric Properties of LIWC-22. University of Texas at Austin.Google Scholar
Boyd, R. L., Blackburn, K. G., and Pennebaker, J. W. (2020). The narrative arc: Revealing core narrative structures through text analysis. Science Advances, 6, eaba2196.CrossRefGoogle ScholarPubMed
Boyd, R. L., and Pennebaker, J. W. (2015). A way with words: Using language for psychological science in the modern era. In Dimofte, C. V., Haugtvedt, C. P., and Yalch, R. F. (eds.) Consumer Psychology in a Social Media World. Routledge.Google Scholar
Boyd, R. L., and Pennebaker, J. W. (2017). Language-based personality: A new approach to personality in a digital world. Current Opinion in Behavioral Sciences, 18, 63–68.CrossRefGoogle Scholar
Boyd, R. L., and Schwartz, H. A. (2021). Natural language analysis and the psychology of verbal behavior: The past, present, and future states of the field. Journal of Language and Social Psychology, 40, 21–41.CrossRefGoogle ScholarPubMed
Brinberg, M., and Ram, N. (2021). Do new romantic couples use more similar language over time? Evidence from intensive longitudinal text messages. Journal of Communication, 71, 454–477.CrossRefGoogle ScholarPubMed
Brown, P., and Levinson, S. C. (1987). Politeness: Some Universals in Language Usage. Cambridge University Press.CrossRefGoogle Scholar
Bruno, J. H., Jarvis, E. D., Liberman, M., and Tchernichovski, O. (2021). Birdsong learning and culture: analogies with human spoken language. Annual Review of Linguistics, 7(1), 449–472.CrossRefGoogle Scholar
Byrne, M. L., Lind, M. N., Horn, S. R., Mills, K. L., Nelson, B. W., Barnes, M. L., … Allen, N. B. (2021). Using mobile sensing data to assess stress: Associations with perceived and lifetime stress, mental health, sleep, and inflammation. Digital Health, 7, 20552076211037227.CrossRefGoogle ScholarPubMed
Campbell, D. T., and Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait–multimethod matrix. Psychological Bulletin, 56, 81–105.CrossRefGoogle ScholarPubMed
Charlesworth, T. E., Caliskan, A., and Banaji, M. R. (2022). Historical representations of social groups across 200 years of word embeddings from Google Books. Proceedings of the National Academy of Sciences, 119, e2121798119.CrossRefGoogle ScholarPubMed
Chung, C. K., and 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, 96–132.CrossRefGoogle Scholar
Chung, C. K., Rentfrow, P. J., and Pennebaker, J. W. (2014). Finding values in words: Using natural language to detect regional variations in personal concerns. In Rentfrow, P. J. (ed.) Geographical Psychology: Exploring the Interaction of Environment and Behavior. American Psychological Association.Google Scholar
Christie, A. (1936). The ABC Murders. Collins Crime Club.Google Scholar
Coppersmith, G., Fine, A., Crutchley, P., and Carroll, J. (2021). Individual differences in the movement–mood relationship in digital life data. In Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology, 25–31.CrossRefGoogle Scholar
Cutler, A., and Condon, D. M. (2022). Deep lexical hypothesis: Identifying personality structure in natural language. Journal of Personality and Social Psychology. Advance online publication, https://doi.org/10.1037/pspp0000443.Google Scholar
Danescu-Niculescu-Mizil, C., West, R., Jurafsky, D., Leskovec, J., and Potts, C. (2013, May). No country for old members: User lifecycle and linguistic change in online communities. In Proceedings of the 22nd international conference on World Wide Web, 307–318.CrossRefGoogle Scholar
Davies, M. (2008). Word frequency data (www.wordfrequency.info). From the Corpus of Contemporary American English (COCA), www.english-corpora.org/coca/.Google Scholar
Davies, M. (2022). Google Books (BYU/Advanced): American English, www.english-corpora.org/googlebooks/#Google Scholar
Davis, T., and Goldwater, M. (2021). Using model-based neuroimaging to adjudicate structured and continuous representational accounts in same–different categorization and beyond. Current Opinion in Behavioral Sciences, 37, 103–108.CrossRefGoogle Scholar
DeFranza, D., Mishra, H., and Mishra, A. (2020). How language shapes prejudice against women: An examination across 45 world languages. Journal of Personality and Social Psychology, 119, 7–22.CrossRefGoogle ScholarPubMed
Deters, F. G., and Mehl, M. R. (2013). Does posting Facebook status updates increase or decrease loneliness? An online social networking experiment. Social Psychological and Personality Science, 4, 579–586.CrossRefGoogle ScholarPubMed
Devlin, J., Chang, M. W., Lee, K., and Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.Google Scholar
Dolcos, S., and Albarracin, D. (2014). The inner speech of behavioral regulation: Intentions and task performance strengthen when you talk to yourself as a You. European Journal of Social Psychology, 44, 636–642.CrossRefGoogle Scholar
Doré, B. P., and Morris, R. R. (2018). Linguistic synchrony predicts the immediate and lasting impact of text-based emotional support. Psychological Science, 29, 1716–1723.CrossRefGoogle ScholarPubMed
Durrheim, K., Schuld, M., Mafunda, M., and Mazibuko, S. (2022). Using word embeddings to investigate cultural biases. British Journal of Social Psychology, 62(4), DOI:10.1111/bjso.12560.Google ScholarPubMed
Eichstaedt, J. C., Kern, M. L., Yaden, D. B., Schwartz, H. A., Giorgi, S., Park, G., Hagan, C. A., Tobolsky, V. A., Smith, L. K., Buffone, A., Iwry, J., Seligman, M. E. P., and Ungar, L. H. (2021). Closed- and open-vocabulary approaches to text analysis: A review, quantitative comparison, and recommendations. Psychological Methods, 26, 398–427.CrossRefGoogle ScholarPubMed
Eichstaedt, J. C., Smith, R. J., Merchant, R. M., Ungar, L. H., Crutchley, P., Preoţiuc-Pietro, D., Asch, D. A., and Schwartz, H. A. (2018). Facebook language predicts depression in medical records. Proceedings of the National Academy of Sciences, 115, 11203–11208.CrossRefGoogle ScholarPubMed
Esper, E. (1935). Language. In Murchison, C. (ed.) A Handbook of Social Psychology. Clark University Press.Google Scholar
Evangelopoulos, N. E. (2013). Latent semantic analysis. Wiley Interdisciplinary Reviews: Cognitive Science, 4, 683–692.Google ScholarPubMed
Fast, E., Chen, B., and Bernstein, M. S. (2016, May). Empath: Understanding topic signals in large-scale text. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, 4647–4657.CrossRefGoogle Scholar
Finkel, E. J., and Eastwick, P. W. (2008). Speed-dating. Current Directions in Psychological Science, 17, 193–197.CrossRefGoogle Scholar
Foltz, P. W. (1996). Latent semantic analysis for text-based research. Behavior Research Methods, Instruments, & Computers, 28, 197–202.CrossRefGoogle Scholar
Francis, M. E., and Pennebaker, J. W. (1992). Putting stress into words: The impact of writing on physiological, absentee, and self-reported emotional well-being measures. American Journal of Health Promotion, 6(4), 280–287.CrossRefGoogle ScholarPubMed
Freud, S. (1960). The Psychopathology of Everyday Life: Forgetting, Slips of the Tongue, Bungled Actions, Superstitions and Errors (1901). In The Standard Edition of the Complete Psychological Works of Sigmund Freud, vol. 6, trans. and ed. James Strachey. The Hogarth Press.Google Scholar
Frimer, J., Haidt, J., Graham, J. Dehgani, M., and Boghrati, R. (2017). Moral foundations dictionaries for linguistic analyses, 2.0. Unpublished manuscript, www.jeremyfrimer.com/uploads/2/1/2/7/21278832/summary.pdf (accessed October 24, 2022).Google Scholar
Frost, D. M. (2013). The narrative construction of intimacy and affect in relationship stories: Implications for relationship quality, stability, and mental health. Journal of Social and Personal Relationships, 30(3), 247–269.CrossRefGoogle Scholar
Furnham, A. (1990). Language and personality. In Giles, H. and Robinson, W. P. (eds.) Handbook of Language and Social Psychology. John Wiley & Sons.Google Scholar
Garten, J., Hoover, J., Johnson, K. M., Boghrati, R., Iskiwitch, C., and Dehghani, M. (2018). Dictionaries and distributions: Combining expert knowledge and large scale textual data content analysis. Behavior Research Methods, 50, 344–361.CrossRefGoogle ScholarPubMed
Gelfand, M. J., Severance, L., Lee, T., Bruss, C. B., Lun, J., Abdel‐Latif, A. H., … Moustafa Ahmed, S. (2015). Culture and getting to yes: The linguistic signature of creative agreements in the United States and Egypt. Journal of Organizational Behavior, 36, 967–989.CrossRefGoogle Scholar
Gendron, M., Lindquist, K. A., Barsalou, L., and Barrett, L. F. (2012). Emotion words shape emotion percepts. Emotion, 12, 314–325.CrossRefGoogle ScholarPubMed
Gerlach, M., and Font-Clos, F. (2020). A standardized Project Gutenberg corpus for statistical analysis of natural language and quantitative linguistics. Entropy, 22, 126.CrossRefGoogle Scholar
Ghai, B., Hoque, M. N., and Mueller, K. (2021, May). WordBias: An interactive visual tool for discovering intersectional biases encoded in word embeddings. In Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, 1–7.CrossRefGoogle Scholar
Giles, H. (1971). Teacher’s attitudes towards accent usage and change. Educational Review, 24, 11–25.CrossRefGoogle Scholar
Gottman, J. M., and Levenson, R. W. (2000). The timing of divorce: Predicting when a couple will divorce over a 14-year period. Journal of Marriage and Family, 62, 737–745.CrossRefGoogle Scholar
Gottschalk, L. A. (1974). The application of a method of content analysis to psychotherapy research. American Journal of Psychotherapy, 28, 488–499.CrossRefGoogle ScholarPubMed
Graesser, A. C., Fiore, S. M., Greiff, S., Andrews-Todd, J., Foltz, P. W., and Hesse, F. W. (2018). Advancing the science of collaborative problem solving. Psychological Science in the Public Interest, 19, 59–92.CrossRefGoogle ScholarPubMed
Graham, J., Haidt, J., and Nosek, B. A. (2009). Liberals and conservatives rely on different sets of moral foundations. Journal of Personality and Social Psychology, 96, 1029–1046.CrossRefGoogle ScholarPubMed
Hart, R. P. (1984). Systematic analysis of political discourse: The development of DICTION. Political Communication Yearbook, 1, 97–134.Google Scholar
Hewstone, M. (1983). The role of language in attribution processes. In Jaspars, J., Fincham, F. D., and Hewstone, M. (eds.), Attribution Theory and Research: Conceptual, Developmental and Social Dimensions. Academic Press.Google Scholar
Hirsh, J. B., and Peterson, J. B. (2009). Personality and language use in self-narratives. Journal of Research in Personality, 43, 524–527.CrossRefGoogle Scholar
Ho, S. M., and Hancock, J. T. (2019). Context in a bottle: Language-action cues in spontaneous computer-mediated deception. Computers in Human Behavior, 91, 33–41.CrossRefGoogle Scholar
Hobson, R. P., Lee, A., and Hobson, J. A. (2010). Personal pronouns and communicative engagement in autism. Journal of Autism and Developmental Disorders, 40, 653–664.CrossRefGoogle ScholarPubMed
Holtzman, N. S., Tackman, A. M., Carey, A. L., Brucks, M. S., Küfner, A. C., Deters, F. G., … Mehl, M. R. (2019). Linguistic markers of grandiose narcissism: A LIWC analysis of 15 samples. Journal of Language and Social Psychology, 38, 773–786.CrossRefGoogle Scholar
Hopp, F. R., Fisher, J. T., Cornell, D., Huskey, R., and Weber, R. (2021). The extended Moral Foundations Dictionary (eMFD): Development and applications of a crowd-sourced approach to extracting moral intuitions from text. Behavior Research Methods, 53, 232–246.CrossRefGoogle Scholar
Hovy, D., and Prabhumoye, S. (2021). Five sources of bias in natural language processing. Language and Linguistics Compass, 15, e12432.CrossRefGoogle ScholarPubMed
Hutto, C., and Gilbert, E. (2014, May). Vader: A parsimonious rule-based model for sentiment analysis of social media text. In Proceedings of the International AAAI Conference on Web and Social Media, 216–225.CrossRefGoogle Scholar
Iliev, R., Hoover, J., Dehghani, M., and Axelrod, R. (2016). Linguistic positivity in historical texts reflects dynamic environmental and psychological factors. Proceedings of the National Academy of Sciences, 113(49), E7871–E7879.CrossRefGoogle ScholarPubMed
Ireland, M. E., and Henderson, M. D. (2014). Language style matching, engagement, and impasse in negotiations. Negotiation and conflict management research, 7, 1–16.CrossRefGoogle Scholar
Ireland, M. E., and Mehl, M. R. (2014). Natural language use as a marker of personality. In Holtgraves, T. M. (ed.), The Oxford Handbook of Language and Social Psychology. Oxford University Press.Google Scholar
Ireland, M. E., and Nalabandian, T. (2022). Language coordination in writing and conversation. In Boyd, R. and Dehghani, M. (eds.) Handbook of Language Analysis in Psychology. Guilford Press.Google Scholar
Ireland, M. E., and Pennebaker, J. W. (2010). Language style matching in writing: synchrony in essays, correspondence, and poetry. Journal of Personality and Social Psychology, 99, 549–571.CrossRefGoogle ScholarPubMed
Ireland, M. E., Slatcher, R. B., Eastwick, P. W., Scissors, L. E., Finkel, E. J., and Pennebaker, J. W. (2011). Language style matching predicts relationship initiation and stability. Psychological science, 22, 39–44.CrossRefGoogle ScholarPubMed
Iserman, M. (2022). lingmatch: Linguistic matching and accommodation. R package version 1.0.4, https://CRAN.R-project.org/package=lingmatch.Google Scholar
Jaidka, K., Giorgi, S., Schwartz, H. A., Kern, M. L., Ungar, L. H., and Eichstaedt, J. C. (2020). Estimating geographic subjective well-being from Twitter: A comparison of dictionary and data-driven language methods. Proceedings of the National Academy of Sciences, 117, 10165–10171.CrossRefGoogle ScholarPubMed
Jeblick, K., Schachtner, B., Dexl, J., Mittermeier, A., Stüber, A. T., Topalis, J., Weber, T., Wesp, P., Sabel, B., Ricke, J., and Ingrisch, M. (2022). ChatGPT makes medicine easy to swallow: An exploratory case study on simplified radiology reports. arXiv preprint arXiv:2212.14882.Google Scholar
Jordan, K. N., Sterling, J., Pennebaker, J. W., and Boyd, R. L. (2019). Examining long-term trends in politics and culture through language of political leaders and cultural institutions. Proceedings of the National Academy of Sciences, 116, 3476–3481.CrossRefGoogle ScholarPubMed
Kacewicz, E., Pennebaker, J. W., Davis, M., Jeon, M., and Graesser, A. C. (2014). Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology, 33, 125–143.CrossRefGoogle Scholar
Karadeniz, T., and Dogdu, E. (2018, December). Improvement of general inquirer features with quantity analysis. In 2018 IEEE International Conference on Big Data (Big Data). IEEE.Google Scholar
Kashy, D. A., and Kenny, D. A. (2000). The analysis of data from dyads and groups. In Reis, H. T. and Judd, C. M. (eds.) Handbook of Research Methods in Social and Personality Psychology (pp. 451–477). Cambridge University Press.Google Scholar
Kintsch, W., and Mangalath, P. (2011). The construction of meaning. Topics in Cognitive Science, 3, 346–370.CrossRefGoogle ScholarPubMed
Kjell, O. N., Sikström, S., Kjell, K., and Schwartz, H. A. (2022). Natural language analyzed with AI-based transformers predict traditional subjective well-being measures approaching the theoretical upper limits in accuracy. Scientific Reports, 12, 1–9.CrossRefGoogle ScholarPubMed
Koul, A., Becchio, C., and Cavallo, A. (2018). Cross-validation approaches for replicability in psychology. Frontiers in Psychology, 9, 1117, https://doi.org/10.3389/fpsyg.2018.01117.CrossRefGoogle ScholarPubMed
Kosinski, M., and Stillwell, D. J. (2011). myPersonality Research Wiki. myPersonality Project, http://mypersonality.org/wiki.Google Scholar
Kross, E., and Ayduk, O. (2017). Self-distancing: Theory, research, and current directions. Advances in Experimental Social Psychology, 55,81–136.CrossRefGoogle Scholar
Landauer, T. K., and Dumais, S. T. (1997). A solution to Plato’s problem: The latent semantic analysis theory of the acquisition, induction, and representation of knowledge. Psychological Review, 104, 211–240.CrossRefGoogle Scholar
Lanning, K., Pauletti, R. E., King, L. A., and McAdams, D. P. (2018). Personality development through natural language. Nature Human Behaviour, 2, 327–334.CrossRefGoogle ScholarPubMed
Linde, C., and Labov, W. (1975). Spatial networks as a site for the study of language and thought. Language, 51, 924–939.CrossRefGoogle Scholar
Lindquist, K. A. (2017). The role of language in emotion: Existing evidence and future directions. Current Opinion in Psychology, 17, 135–139.CrossRefGoogle ScholarPubMed
Lindquist, K. A., Jackson, J. C., Leshin, J., Satpute, A. B., and Gendron, M. (2022). The cultural evolution of emotion. Nature Reviews Psychology, 1(11), 669–681.CrossRefGoogle Scholar
Liu, T., Giorgi, S., Yadeta, K., Schwartz, H. A., Ungar, L. H., and Curtis, B. (2022a). Linguistic predictors from Facebook postings of substance use disorder treatment retention versus discontinuation. American Journal of Drug and Alcohol Abuse, 48, 573–585.CrossRefGoogle ScholarPubMed
Liu, Y., Mittal, A., Yang, D., and Bruckman, A. (2022b). Will AI console me when I lose my pet? Understanding perceptions of AI-mediated email writing. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems ,1–13.CrossRefGoogle Scholar
McAdams, D. P. (2008). Personal narratives and the life story. In John, O. P., Robins, R. W., and Pervin, L. A. (eds.), Handbook of Personality: Theory and Research, 3rd ed. The Guilford Press.Google Scholar
McClelland, D. C., Koestner, R., and Weinberger, J. (1989). How do self-attributed and implicit motives differ? Psychological Review, 96, 690–702.CrossRefGoogle Scholar
McNeilly, E. A., Mills, K., Kahn, L., Crowley, R., Pfeifer, J., and Allen, N. (2023). Adolescent social communication through smartphones: Linguistic features of internalizing symptoms and daily mood. Clinical Psychological Science, 11(6), 1090–1107.CrossRefGoogle ScholarPubMed
Mairesse, F., and Walker, M. (2007). PERSONAGE: Personality generation for dialogue. In Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics (ACL), 496–503.Google Scholar
Manczak, E. M., Zapata-Gietl, C., and McAdams, D. P. (2014). Regulatory focus in the life story: Prevention and promotion as expressed in three layers of personality. Journal of Personality and Social Psychology, 106, 169–181.CrossRefGoogle ScholarPubMed
Mehl, M. R. (2017). The electronically activated recorder (EAR): A method for the naturalistic observation of daily social behavior. Current Directions in Psychological Science, 26, 184–190.CrossRefGoogle Scholar
Mehl, M. R., Gosling, S. D., and Pennebaker, J. W. (2006). Personality in its natural habitat: manifestations and implicit folk theories of personality in daily life. Journal of Personality and Social Psychology, 90, 862–877.CrossRefGoogle ScholarPubMed
Mehl, M. R., Pennebaker, J. W., Crow, D. M., Dabbs, J., and Price, J. H. (2001). The electronically activated recorder (EAR): A device for sampling naturalistic daily activities and conversations. Behavior Research Methods, Instruments, & Computers, 33, 517–523.CrossRefGoogle Scholar
Mehl, M. R., Robbins, M. L., and Große Deters, F. (2012). Naturalistic observation of health-relevant social processes: The Electronically Activated Recorder (EAR) methodology in psychosomatics. Psychosomatic Medicine, 74, 410–417.CrossRefGoogle ScholarPubMed
Mehl, M. R., Robbins, M. L., and Holleran, S. E. (2012). How taking a word for a word can be problematic: Context-dependent linguistic markers of extraversion and neuroticism. Journal of Methods and Measurement in the Social Sciences, 3, 30–50.CrossRefGoogle Scholar
Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013). Efficient estimation of word representations in vector space. Preprint, arXiv, 1301.3781.Google Scholar
Mohammad, S. M. (2016). Sentiment analysis: Detecting valence, emotions, and other affectual states from text. In Meiselman, H. L. (ed.), Emotion Measurement. Woodhead Publishing.Google Scholar
Morgan, C. D., and Murray, H. A. (1935). A method for investigating fantasies: The thematic apperception test. Archives of Neurology & Psychiatry, 34, 289–306.CrossRefGoogle Scholar
Murchison, C. (ed.) (1935). A Handbook of Social Psychology. Clark University Press.Google Scholar
Nalabandian, T., and Ireland, M. E. (2019). Genre-typical narrative arcs in films are less appealing to lay audiences and professional film critics. Behavior Research Methods, 51, 1636–1650.CrossRefGoogle ScholarPubMed
Neff, G. (2016). Talking to bots: Symbiotic agency and the case of Tay. International Journal of Communication, 10, 4915–4931.Google Scholar
Newman, M. L., Pennebaker, J. W., Berry, D. S., and Richards, J. M. (2003). Lying words: Predicting deception from linguistic styles. Personality and Social Psychology Bulletin, 29, 665–675.CrossRefGoogle ScholarPubMed
Niederhoffer, K. G., and Pennebaker, J. W. (2002). Linguistic style matching in social interaction. Journal of Language and Social Psychology, 21, 337–360.CrossRefGoogle Scholar
Norman, W. T. (1967). 2800 Personality Trait Descriptors: Normative Operating Characteristics for a University Population. University of Michigan, Dept. of Psychology.Google Scholar
Novogrodsky, R. (2013). Subject pronoun use by children with autism spectrum disorders (ASD). Clinical Linguistics & Phonetics, 27, 85–93.CrossRefGoogle ScholarPubMed
Ott, M., Choi, Y., Cardie, C., and Hancock, J. T. (2013). Finding deceptive opinion spam by any stretch of the imagination. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol 1., 309–319.Google Scholar
Pang, J. S., and Schultheiss, O. C. (2005). Assessing implicit motives in US college students: Effects of picture type and position, gender and ethnicity, and cross-cultural comparisons. Journal of Personality Assessment, 85, 280–294.CrossRefGoogle ScholarPubMed
Park, G., Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Kosinski, M., Stillwell, D. J., … Seligman, M. E. (2015). Automatic personality assessment through social media language. Journal of personality and social psychology, 108, 934–952.CrossRefGoogle ScholarPubMed
Park, G., Yaden, D. B., Schwartz, H. A., Kern, M. L., Eichstaedt, J. C., Kosinski, M., … Seligman, M. E. (2016). Women are warmer but no less assertive than men: Gender and language on Facebook. PLOS ONE, 11(5), e0155885.CrossRefGoogle ScholarPubMed
Pashler, H., and Harris, C. R. (2012). Is the replicability crisis overblown? Three arguments examined. Perspectives on Psychological Science, http://dx.doi.org/10.1371/journal, pmed.0020124.CrossRefGoogle Scholar
Peabody, D. and Goldberg, L. R. (1989). Some determinants of factor structures from personality-trait descriptors. Journal of Personality and Social Psychology, 57, 552–567.CrossRefGoogle ScholarPubMed
Pennebaker, J. W. (2011). The Secret Life of Pronouns: What Our Words Say about Us. Bloomsbury.CrossRefGoogle Scholar
Pennebaker, J. W. (2021). Computer-based language analysis as a paradigm shift. In Dehghani, M. and Boyd, R. L. (eds.) Handbook of Language Analysis in Psychology. Guilford.Google Scholar
Pennebaker, J. W., and Beall, S. K. (1986). Confronting a traumatic event: toward an understanding of inhibition and disease. Journal of Abnormal Psychology, 95, 274–281.CrossRefGoogle ScholarPubMed
Pennebaker, J. W., Boyd, R. L., Booth, R. J., Ashokkumar, A., and Francis, M. E. (2022). Linguistic inquiry and word count: LIWC-22. Pennebaker Conglomerates, http://www.liwc.app.Google Scholar
Pennebaker, J. W., Chung, C. K., Frazee, J., Lavergne, G. M., and Beaver, D. I. (2014). When small words foretell academic success: The case of college admissions essays. PLOS ONE, 9, e115844.CrossRefGoogle ScholarPubMed
Pennebaker, J. W., and Francis, M. E. (1996). LIWC Windows Application. LEA Software and Alternative Media.Google Scholar
Pennebaker, J. W., and King, L. A. (1999). Linguistic styles: language use as an individual difference. Journal of Personality and Social Psychology, 77, 1296–1312.CrossRefGoogle ScholarPubMed
Pennington, J., Socher, R., and Manning, C. D. (2014). Glove: Global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, 1532–1543.CrossRefGoogle Scholar
Peters, H., Marrero, Z., and Gosling, S. D. (2022). The Big Data toolkit for psychologists: Data sources and methodologies. In Matz, S. C. (ed.), The Psychology of Technology: Social Science Research in the Age of Big Data. American Psychological Association.Google Scholar
Preoţiuc-Pietro, D., Schwartz, H. A., Park, G., Eichstaedt, J., Kern, M., Ungar, L., and Shulman, E. (2016, June). Modelling valence and arousal in Facebook posts. In Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, 9–15.CrossRefGoogle Scholar
Pury, C. L. (2011). Automation can lead to confounds in text analysis: Back, Küfner, and Egloff (2010) and the not-so-angry Americans. Psychological Science, 22, 835–836.CrossRefGoogle ScholarPubMed
Rasmussen, H. F., Borelli, J. L., Smiley, P. A., Cohen, C., Cheung, R. C. M., Fox, S., Marvin, M., and Blackard, B. (2017). Mother–child language style matching predicts children’s and mothers’ emotion reactivity. Behavioural Brain Research, 325, 203–213.CrossRefGoogle ScholarPubMed
Rauthmann, J. F., Gallardo-Pujol, D., Guillaume, E. M., Todd, E., Nave, C. S., Sherman, R. A., Ziegler, M., Jones, A., B., and Funder, D. C. (2014). The Situational Eight DIAMONDS: a taxonomy of major dimensions of situation characteristics. Journal of Personality and Social Psychology, 107, 677–718.CrossRefGoogle ScholarPubMed
Rayner, K. (1977). Visual attention in reading: Eye movements reflect cognitive processes. Memory & Cognition, 5, 443–448.CrossRefGoogle ScholarPubMed
Reece, A., Cooney, G., Bull, P., Chung, C., Dawson, B., Fitzpatrick, C., … Marin, S. (2022). Advancing an interdisciplinary science of conversation: Insights from a large multimodal corpus of human speech. Preprint, arXiv, 2203.00674.Google Scholar
Rentscher, K. E., Soriano, E. C., Rohrbaugh, M. J., Shoham, V., and Mehl, M. R. (2017). Partner pronoun use, communal coping, and abstinence during couple‐focused intervention for problematic alcohol use. Family Process, 56, 348–363.CrossRefGoogle ScholarPubMed
Richardson, B. H., Taylor, P. J., Snook, B., Conchie, S. M., and Bennell, C. (2014). Language style matching and police interrogation outcomes. Law and Human Behavior, 38(4), 357–366.CrossRefGoogle ScholarPubMed
Robinson, M. D., Persich, M. R., Sjoblom-Schmidt, S., and Penzel, I. B. (2020). Love stories: How language use patterns vary by relationship quality. Discourse Processes, 57, 81–98.CrossRefGoogle Scholar
Rohrbaugh, M. J., Shoham, V., Skoyen, J. A., Jensen, M., and Mehl, M. R. (2012). We‐talk, communal coping, and cessation success in a couple‐focused intervention for health‐compromised smokers. Family Process, 51, 107–121.CrossRefGoogle Scholar
Rorschach, H. (1921). Psychodiagnostik. Bircher.Google Scholar
Sagi, E., and Dehghani, M. (2014). Measuring moral rhetoric in text. Social Science Computer Review, 32, 132–144.CrossRefGoogle Scholar
Sap, M., Jafarpour, A., Choi, Y., Smith, N. A., Pennebaker, J. W. and Horvitz, E. (2022, in press). Quantifying the narrative flow of imagined versus autobiographical stories. PNAS.CrossRefGoogle Scholar
Schaper, R., Nowotny, C., Michalek, S., Schmidt, U., and Brockmeyer, T. (2022). Language style matching and treatment outcome in anorexia nervosa. European Eating Disorders Review, 31(1), DOI:10.1002/erv.2943.Google Scholar
Schultheiss, O. C., Patalakh, M., Rawolle, M., Liening, S., and MacInnes, J. J. (2011). Referential competence is associated with motivational congruence. Journal of Research in Personality, 45, 59–70.CrossRefGoogle Scholar
Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Dziurzynski, L., Ramones, S. M., Agrawal, M., Shah, A., Kosinski, M., Stillwell, D., Seligman, M. E., and Ungar, L. H. (2013). Personality, gender, and age in the language of social media: The open-vocabulary approach. PLOS ONE, 8, p.e73791.CrossRefGoogle ScholarPubMed
Schwartz, H. A., Eichstaedt, J., Kern, M., Park, G., Sap, M., Stillwell, D., … Ungar, L. (2014). Towards assessing changes in degree of depression through Facebook. In Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, 118–125.CrossRefGoogle Scholar
Schwartz, H. A., Park, G., Sap, M., Weingarten, E., Eichstaedt, J., Kern, M., Stillwell, D., Kosinski, M., Berger, J., Seligman, M., and Ungar, L. (2015). Extracting human temporal orientation from Facebook language. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 409–419.CrossRefGoogle Scholar
Sedoc, J., Buechel, S., Nachmany, Y., Buffone, A., and Ungar, L. (2020, May). Learning word ratings for empathy and distress from document-level user responses. In Proceedings of the 12th Language Resources and Evaluation Conference, 1664–1673.Google Scholar
Seraj, S., Blackburn, K. G., and Pennebaker, J. W. (2021). Language left behind on social media exposes the emotional and cognitive costs of a romantic breakup. Proceedings of the National Academy of Sciences, 118, e2017154118.CrossRefGoogle ScholarPubMed
Sharma, A., Lin, I. W., Miner, A. S., Atkins, D. C., and Althoff, T. (2023). Human–AI collaboration enables more empathic conversations in text-based peer-to-peer mental health support. Nature Machine Intelligence, 5(1), 1–12.CrossRefGoogle Scholar
Simmons, R. A., Gordon, P. C., and Chambless, D. L. (2005). Pronouns in marital interaction: What do “you” and “I” say about marital health? Psychological science, 16, 932–936.CrossRefGoogle Scholar
Slatcher, R. B., Vazire, S., and Pennebaker, J. W. (2008). Am “I” more important than “we”? Couples’ word use in instant messages. Personal Relationships, 15, 407–424.CrossRefGoogle Scholar
Smith, C. P. (2000). Content analysis and narrative analysis. In Reis, H. T. and Judd, C. M. (eds.), Handbook of Research Methods in Social and Personality Psychology. Cambridge University Press.Google Scholar
Srivastava, S. B., Goldberg, A., Manian, V. G., and Potts, C. (2018). Enculturation trajectories: Language, cultural adaptation, and individual outcomes in organizations. Management Science, 64, 1348–1364.CrossRefGoogle Scholar
Stewart, A. E., Vrzakova, H., Sun, C., Yonehiro, J., Stone, C. A., Duran, N. D., Shute, V. and D’Mello, S. K. (2019). I say, you say, we say: Using spoken language to model socio-cognitive processes during computer-supported collaborative problem solving. Proceedings of the ACM on Human-Computer Interaction, 3, 1–19.CrossRefGoogle Scholar
Stone, P. J., Bales, R. F., Namenwirth, J. Z., and Ogilvie, D. M. (1962). The General Inquirer: A computer system for content analysis and retrieval based on the sentence as a unit of information. Behavioral Science, 7, 484–498.CrossRefGoogle Scholar
Sun, J., Schwartz, H. A., Son, Y., Kern, M. L., and Vazire, S. (2020). The language of well-being: Tracking fluctuations in emotion experience through everyday speech. Journal of Personality and Social Psychology, 118, 364–387.CrossRefGoogle ScholarPubMed
Tackman, A. M., Baranski, E. N., Danvers, A. F., Sbarra, D. A., Raison, C. L., Moseley, S. A., … Mehl, M. R. (2020). “Personality in its natural habitat” revisited: A pooled, multi–sample examination of the relationships between the Big Five personality traits and daily behaviour and language use. European Journal of Personality, 34, 753–776.CrossRefGoogle Scholar
Tackman, A. M., Sbarra, D. A., Carey, A. L., Donnellan, M. B., Horn, A. B., Holtzman, N. S., Edwards, T. M. S., Pennebaker, J. W., and Mehl, M. R. (2019). Depression, negative emotionality, and self-referential language: A multi-lab, multi-measure, and multi-language-task research synthesis. Journal of Personality and Social Psychology, 116, 817–834.CrossRefGoogle Scholar
Tausczik, Y., Chung, C., and Pennebaker, J. (2016). Tracking secret-keeping in emails. In Proceedings of the International AAAI Conference on Web and Social Media, 388–397.Google Scholar
Tausczik, Y. R., and Pennebaker, J. W. (2010). The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology, 29, 24–54.CrossRefGoogle Scholar
Taylor, P. J., Dando, C. J., Ormerod, T. C., Ball, L. J., Jenkins, M. C., Sandham, A., and Menacere, T. (2013). Detecting insider threats through language change. Law and Human Behavior, 37, 267–275.CrossRefGoogle ScholarPubMed
Taylor, P. J., and Thomas, S. (2008). Linguistic style matching and negotiation outcome. Negotiation and Conflict Management Research, 1, 263–281.CrossRefGoogle Scholar
Thorp, H. H. (2023). ChatGPT is fun, but not an author. Science, 379, 313.CrossRefGoogle Scholar
Timmons, A. C., Han, S. C., Kim, Y., Pettit, C., Perrone, L., Power, K., Vitale, L., and Margolin, G. (2021). Fluctuations in pronoun use in everyday life: Understanding couple aggression in context. Journal of Family Psychology, 35, 149–159.CrossRefGoogle ScholarPubMed
Toma, C. L., and Hancock, J. T. (2012). What lies beneath: The linguistic traces of deception in online dating profiles. Journal of Communication, 62, 78–97.CrossRefGoogle Scholar
van Loon, A., Giorgi, S., Willer, R., and Eichstaedt, J. (2022, May). Negative associations in word embeddings predict anti-black bias across regions – but only via name frequency. In Proceedings of the International AAAI Conference on Web and Social Media, 1419–1424.CrossRefGoogle Scholar
Vine, V., Boyd, R. L., and Pennebaker, J. W. (2020). Natural emotion vocabularies as windows on distress and well-being. Nature Communications, 11, 1–9.CrossRefGoogle ScholarPubMed
Wei, J., Finn, K., Templeton, E., Wheatley, T., and Vosoughi, S. (2021). Linguistic complexity loss in text-based therapy. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 4450–4459.CrossRefGoogle Scholar
Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P. S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., and Kenton, Z. (2021). Ethical and social risks of harm from language models. Preprint, arXiv, 2112.04359.Google Scholar
Weidman, A. C., Sun, J., Vazire, S., Quoidbach, J., Ungar, L. H., and Dunn, E. W. (2020). (Not) hearing happiness: Predicting fluctuations in happy mood from acoustic cues using machine learning. Emotion, 20, 642–658.CrossRefGoogle ScholarPubMed
Weintraub, W. (1981). Verbal Behavior: Adaptation and Psychopathology. Springer Publishing Company.Google Scholar
Weintraub, W. (1989). Verbal Behavior in Everyday Life. Springer Publishing Co.Google Scholar
Winter, D. G., and McClelland, D. C. (1978). Thematic analysis: An empirically derived measure of the effects of liberal arts education. Journal of Educational Psychology, 70, 8–16.CrossRefGoogle Scholar
Whorf, B. L. (1940). Science and Linguistics. Bobbs-Merrill.Google Scholar
Wierzbicka, A. (1997). Understanding Cultures through Their Key Words: English, Russian, Polish, German, and Japanese. Oxford University Press.CrossRefGoogle Scholar
Wierzbicka, A. (2016). Two levels of verbal communication, universal and culture-specific. In Rocci, A. and de Saussure, L. (eds.), Verbal Communication. De Gruyter Mouton.Google Scholar
Wittgenstein, L. (1953). Philosophical Investigations. Basil Blackwell.Google Scholar
Wood, J. M., Lilienfeld, S. O., Garb, H. N., and Nezworski, M. T. (2000). The Rorschach test in clinical diagnosis: A critical review, with a backward look at Garfield (1947). Journal of Clinical Psychology, 56, 395–430.3.0.CO;2-O>CrossRefGoogle Scholar
Yarkoni, T. (2010). Personality in 100,000 words: A large-scale analysis of personality and word use among bloggers. Journal of Research in Personality, 44, 363–373.CrossRefGoogle ScholarPubMed
Yeats, W. B. (1921). The Second Coming. In Yeats, W. B., Michael Robartes and the Dancer: And Other Poems. Cuala Press.Google Scholar
Youyou, W., Stillwell, D., Schwartz, H. A., and Kosinski, M. (2017). Birds of a feather do flock together: Behavior-based personality-assessment method reveals personality similarity among couples and friends. Psychological Science, 28, 276–284.CrossRefGoogle Scholar

Save book to Kindle

To save this book to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×