Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers

By Tal Yarkoni. Journal of Research in Personality, 2010

608 citations recorded by the source corpus.

Previous studies have found systematic associations between personality and individual differences in word use. Such studies have typically focused on broad associations between major personality domains and aggregate word categories, potentially masking more specific associations. Here I report the results of a large-scale analysis of personality and word use in a large sample of blogs (N=694). The size of the dataset enabled pervasive correlations with personality to be identified for a broad range of lexical variables, including both aggregate word categories and individual English words. The results replicated category-level findings from previous offline studies, identified numerous novel associations at both a categorical and single-word level, and underscored the value of complementary approaches to the study of personality and word use.

Subfields: Artificial Intelligence, Clinical Psychology, Social Psychology

View Paper: https://www.semanticscholar.org/paper/9354fbeb34aa0ee318266a12f43a1e987b595ffe

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Citations in the corpus

Choosing prediction over explanation in psychology: Lessons from machine learning (2017, background): “For example, suppose one is interested in the relationship between personality and language use (Fast & Funder, 2008; Pennebaker & King, 1999; Yarkoni, 2010).”
Using Hashtags to Capture Fine Emotion Categories from Tweets (2015, background): “More recently, there has also been work on personality detection from blogs (Yarkoni, 2010), collections of Facebook posts (Kosinski et al. , 2013), and collections of Twitter posts and follower network (Qiu et al. , 2012).”
User-generated-content versus marketing-generated-content: personality and content influence on traveler’s behavior (2018, background): “, 2008), as well as peoples intention in writing online (Yarkoni, 2010).”
A meta-analysis of correlations between depression and first person singular pronoun use (2017, background): “…et al., 2010; Buffardi & Campbell, 2008; Kern et al., 2014; Weidman et al., 2012; Youyou, Kosinski, & Stillwell, 2015) and Twitter (Coppersmith, Dredze, & Harman, 2014), and language is one important kind of footprint people leave behind (Kern et al., 2014; Schwartz et al., 2013; Yarkoni, 2010).”
New Talent Signals: Shiny New Objects or a Brave New World? (2016, unknown): “For example, the words that neurotics use in blogs include “awful,” “horrible,” and “depressing,” whereas extraverts talk about “bars,” “drinks,” and “Miami” (Schwartz et al., 2013; Yarkoni, 2010).”
People With Dark Personalities Tend to Create a Physically Attractive Veneer (2013, methodology): “Self-reports of the Big Five personality traits were obtained using the Analogue for Multiple Broadband Inventories (AMBI; Yarkoni, 2010).”
Facing a psychopath: Detecting the dark triad from emotionally-neutral faces, using prototypes from the Personality Faceaurus (2011, background): “I plan to include prototypes for other traits (e.g., AMBI personality facets; Yarkoni, 2010) in the future.”
You are what you tweet: Personality expression and perception on Twitter (2012, background): “Extraverts have also been found to use more positive emotion words in blogs (Yarkoni, 2010) and stream of consciousness essays (Mairesse, Walker, Mehl, & Moore, 2007; Pennebaker & King, 1999).”
Automatic personality assessment through social media language. (2015, methodology): “In contrast, techniques from computational linguistics offer finer-grained, open-vocabulary methods for language analysis (e.g., Grimmer & Stewart, 2013; O’Connor, Bamman, & Smith, 2011; Schwartz et al., 2013b; Yarkoni, 2010).”
Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach (2013, methodology): “conditions of other studies (for examples, see [25,27,65]), and its component traits are most loosely related [85].”
Psychological targeting as an effective approach to digital mass persuasion (2017, background): “For example, people’s personality profiles have been predicted from personal websites (11), blogs (12), Twitter messages (13), Facebook profiles (10, 14–16), and Instagram pictures (17).”