In this summary, we discuss our approach to the CLPsych Shared Task and its initial results. For our predictions in each task, we used a recursive partitioning algorithm (decision trees) to select from our set of features, which were primarily dictionary scores and counts of individual words. We focused primarily on Task A, which aimed to predict suicide risk, as rated by a team of expert clinicians (Shing et al., 2018), based on language used in SuicideWatch posts on Reddit. Category-level findings highlight the potential importance of social and moral language categories. Word-level correlates of risk levels underline the value of fine-grained data-driven approaches, revealing both theory-consistent and potentially novel correlates of suicide risk that may motivate future research.

People tend to like stimuli-ranging from human faces to text-that are prototypical, and thus easily processed. However, recent research has suggested that less typical stimuli may be preferred in creative contexts, such as fine art or music lyrics. In an archival sample of movie scripts, we tested whether genre-typicality predicted film ratings as a function of rater role (novice audience member or expert film critic). Genre-typicality was operationalized as the profile correlations between linguistic arcs (across five segments, or acts) for each script and within-genre averages. We predicted (1) that critics would prefer more disfluent (genreatypical) films and general audiences would prefer fluent (genre-typical) films, and (2) that these differences would be most pronounced for genres expected to be more entertaining (e.g., action/adventure) than challenging (e.g., tragedy). Partly consistent with our hypotheses, the results showed that critics gave higher ratings to action/adventure films with less typical positive emotion arcs. However, regardless of audience-member or professional-critic status, higher ratings were attributed to films that were more genre-atypical (or disfluent), in terms of analytic thinking, narrative action, and emotional tone, across all genres except family/kids films. Such findings support the growing literature on the appeal of disfluency in the arts and have relevance for researchers in psychology and computer science who are interested in computational linguistic approaches to attitudes, film, and literature.

Depression is characterized by a selffocused negative attentional bias, which is often reflected in everyday language use. In a prospective writing study, we explored whether the association between depressive symptoms and negative, selffocused language varies across social contexts. College students (N = 243) wrote about a recent interaction with a person they care deeply about. Depression symptoms positively correlated with negative emotion words and first-person singular pronouns (or negative self-focus) when writing about a recent interaction with romantic partners or, to a lesser extent, friends, but not family members. The pattern of results was more pronounced when participants perceived greater self-other overlap (i.e., interpersonal closeness) with their romantic partner. Findings regarding how the linguistic profile of depression differs by type of relationship may inform more effective methods of clinical diagnosis and treatment.

Reading-response research has shown that students respond to a text by engaging various cognitive and emotional processes. The aim of the current study was to examine students’ written reactions to an assigned reading as a way to determine (1) whether students connect with the reading and (2) the differing cognitive styles they may utilize in their reactions. The methods applied two text-analytic procedures to 238 student reactions to an ethics case study. The procedures were language style matching, which is a metric of engagement, and the categorical-dynamic index, which is a metric of analytic and experiential processing. We predicted that students who more strongly connected—or engaged—with the text would also demonstrate greater analytic thinking in their written response and, conversely, those who weakly connected with the text would express a more informal response based on experience. The data were analyzed using correlation statistics. The results showed that students whose writing more closely matched with the linguistic style of the case study were more likely to use an analytical style of writing, and students whose writing weakly matched the linguistic style of the case study were more likely to use an informal narrative style of writing. Future research should examine the extent to which language style matching and an associated analytic cognitive style are emergent skills that develop over the course of a college experience. References Baddeley, J.L. (2012). E-mail communications among people with and without major depressive disorder (Unpublished doctoral dissertation). University of Texas at Austin, Austin, TX Blackburn, K.G. (2015). The narrative arc: Exploring the linguistic structure of the narrative (Unpublished doctoral dissertation). University of Texas at Austin, Austin, Texas. Chung, C., & Pennebaker, J. W. (2007). The psychological functions of function words. Social Communication, 1, 343-359. Ireland, M. E., & Pennebaker, J. W. (2010). Language style matching in writing: Synchrony in essays, correspondence, and poetry. Journal of Personality and Social Psychology, 99(3), 549. https://doi.org/10.1037/a0020386 Ireland, M.E., Slatcher, R.B., Eastwick, P.W., Scissors, L.E., Finkel, E.J., & Pennebaker, J.W. (2011). Language style matching predicts relationship initiation and stability. Psychological Science, 22(1), 39-44. https://doi.org/10.1177/0956797610392928 Inbar, Y., Cone, J., & Gilovich, T. (2010). People’s intuitions about intuitive insight and intuitive choice. Journal of Personality and Social Psychology, 99, 232–247. https://doi.org/10.1037/a0020215 Jordan, K. N., & Pennebaker, J. W. (2017). The exception or the rule: Using words to assess analytic thinking, Donald Trump, and the American presidency. Translational Issues in Psychological Science, 3(3), 312-316. https://doi.org/10.1037/tps0000125 Jordan, K. N., Sterling, J., Pennebaker, J. W., & 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(9), 3476-3481. https://doi.org/10.1073/pnas.1811987116 Kacewicz, E., Pennebaker, J. W., Davis, M., Jeon, M., & Graesser, A. C. (2014). Pronoun use reflects standings in social hierarchies. Journal of Language and Social Psychology, 33(2), 125-143. https://doi.org/10.1177/0261927X13502654 Lance G.N., Williams W.T. (1967): Mixed-data classificatory programs, I.) Agglomerative systems. Australian Computer Journal, 1, 15-20. Leaper, C. (2014). Gender similarities and differences in language. In T. M. Holtgraves (Ed.), The Oxford handbook of language and social psychology. (pp. 62-81). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199838639.013.002 Ludwig, S., de Ruyter, K., Mahr, D., Wetzels, M., Brüggen, E. and De Ruyck, T. (2014). Take their word for it: The symbolic role of linguistic style matches in user communities. MIS Quarterly: Management Information Systems, 38(4), 1201-1217. Mart, C. T. (2019). Reader-response theory and literature discussions: A Springboard for exploring literary texts. The New Educational Review, 56, 78-87. https://doi.org/10.15804/tner.2019.56.2.06 Niederhoffer, K. G., & Pennebaker, J. W. (2002). Linguistic style matching in social interaction. Journal of Language and Social Psychology, 21, 337-360. https://doi.org/10.1177/026192702237953 Pennebaker, J. W. (2011). The secret life of pronouns: How our words reflect who we are. New York, NY: Bloomsbury. Pennebaker, J.W., Booth, R.J., Boyd, R.L., & Francis, M.E. (2015). Linguistic Inquiry and Word Count: LIWC2015. Austin, TX: Pennebaker Conglomerates. Pennebaker, J.W., Chung, C.K., Frazee, J., Lavergne, G.M., & Beaver, D.I. (2014). When small words foretell academic success: The case of college admissions essays. PLoS ONE, 9. https://doi.org/10.1371/journal.pone.0115844 Pulvermüller, F., Shtyrov, Y., Hasting, A. S., & Carlyon, R. P. (2008). Syntax as a reflex: Neurophysiological evidence for early automaticity of grammatical processing. Brain and Language, 104, 244-253. https://doi.org/10.1016/j.bandl.2007.05.002 Richardson, B. H., Taylor, P. J., Snook, B., Conchie, S. M., & Bennell, C. (2014). Language style matching and police interrogation outcomes. Law and Human Behavior, 38(4), 357-366.  https://doi.org/10.1037/lhb0000077 Rosenblatt, L. M. (2016). Literature as exploration. Modern Language Association. Segalowitz, S. J., & Lane, K. C. (2000). Lexical access of function versus content words. Brain and Language, 75, 376-389. https://doi.org/10.1006/brln.2000.2361 Segrin, C. (2000). 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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.