The Secret Life of Pronouns. What Our Words Say About Us

By John Nerbonne. Literary and Linguistic Computing, 2014

516 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/7c771743d0e247ce56c9b4670d751abbb439e1fa

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Citations in the corpus

The shortest path to happiness: recommending beautiful, quiet, and happy routes in the city (2014, methodology): “. We also get hold of the actual tags, clean them (i.e., convert them to lowercase and remove those that are stop-words), and process them using a dictionary called\Linguistic Inquiry Word Count"[21]. LIWC is a standard dictionary of 2,300 English words that capture 80% of the words used in eve”
Deep Learning for Depression Detection of Twitter Users (2018, methodology): “From previous research, we identiﬁed that individuals susceptible to depression more regularly use ﬁrst-person singular pronouns compared to the use of other pronouns (Pennebaker, 2011).”
Changes in Pronoun Use in American Books and the Rise of Individualism, 1960-2008 (2013, background): “However, new technologies such as word coding computer programs (for a review, see Pennebaker, 2011) have made it possible to analyze language use in cultural products such as song lyrics (DeWall, Pond, Campbell, & Twenge, 2011).”
(I'm) Happy to Help (You): The Impact of Personal Pronoun Use in Customer–Firm Interactions (2018, background): “Although “I” pronouns have been linked to a speaker’s egotistical self-focus (Pennebaker 2011) or selfinterest (e.g., Ickes, Reidhead, and Patterson 1986), the participation framework of a firm agent serving a customer renders such negative perceptions of “I” pronoun use unlikely.”
Gender Profiling for Slovene Twitter communication: the Influence of Gender Marking, Content and Style (2017, background): “Function words and POS-tags are the best features in this setting, as they are not under conscious con-trol (Pennebaker, 2011).”
Overview of the 3rd Author Profiling Task at PAN 2015 (2015, result): “Furthermore, contrary to most of the existing research in computational linguistics [3] and social psychology [53], which focuses on the English language, we broaden the analysis in terms of languages: in addition to English, we provide data also in Spanish, Italian, and Dutch.”
The Pen Is Mightier Than the Keyboard (2014, methodology): “Linguistic Inquiry and Word Count (LIWC) software was also used to analyze the notes on categories identified by Pennebaker (2011) as correlating with improved college grades.”
Self-Concealment: Integrative Review and Working Model (2015, unknown): “…Caughlin & Vangelisti, 2009; Farber, 2006; Finkenauer, Kubacka, Engels, & Kerkhof, 2009; Imber-Black, 1998; Kelly, 2002; Kelly & Macready, 2009; Pennebaker, 1990, 2011; Petronio, 2000; Smyth, Pennebaker, & Arigo, 2012; Wismeijer, 2011a), draws on work conducted in conceptuallyrelated areas,…”
Emphasizing personal information for Author Profiling: New approaches for term selection and weighting (2018, background): “Furthermore, they have found that patterns on the use of personal pronouns are very useful features to distinguish among different groups of people [12] .”
Progressive Disclosure: Designing for Effective Transparency (2018, background): “However, extant literature confirms that system feedback is correct as usage of first person singular pronouns are correlated with depression and negative mood [63,70].”