Predicting personality with social media

By Jennifer Golbeck, Cristina Robles, Karen Turner. CHI Extended Abstracts, 2011

619 citations recorded by the source corpus.

Subfields: Clinical Psychology, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/3f1442472a674968d6a6f40bb9bcdae019606e49

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Citations in the corpus

User-generated-content versus marketing-generated-content: personality and content influence on traveler’s behavior (2018, background): “…recent years considerable empirical support and is now the most widely accepted taxonomy of human personality and the most wellresearched, well-regarded and used model of measuring personality dimensions (Booker, Kitchens, & Rebman, 2007; Golbeck et al., 2011; Wehrli, 2008; Whelan & Davies, 2006).”
Predicting Personality from Twitter (2011, background): “Personality type is linked to whom users choose to friend on Facebook.”
Artificial intelligence in the creative industries: a review (2020, background): “This normally involves gathering and analysing personal data in order to predict preferences [248].”
A Survey of Personality Computing (2014, neutral): “The main reason is that these tend to focus on personal issues and experiences, therefore they are likely to show traces of their author’s personalities [51].”
Automatic personality assessment through social media language. (2015, methodology): “When applied to out-of-sample users, these models predicted users’ personality traits better than chance, and the authors concluded that “users’ Big Five personality traits can be predicted from the public information they share on Face-book” (Golbeck et al., 2011, p. 260).”
Understanding User Profiles on Social Media for Fake News Detection (2018, methodology): “Personality inference on social media is first studied in [13] by using linguistic, structure and LIWC features and to predict the Big Five Personality Inventory [14] that shaping users’ personalities.”
Predicting the Big 5 personality traits from digital footprints on social media: A meta-analysis (2018, background): “…best possible user experiences (Farnadi et al., 2016), and recommendation systems may also capitalize on this information by including personality dimensions to their current user models and present information in ways that will be most attractive to users (Golbeck et al., 2011; Nass & Lee, 2000).”
Personality and patterns of Facebook usage (2012, background): “We continue and expand the work of [2, 11, 15, 21] attempting to overcome some of their limitations, and most of all their rather small (at most a few hundred participants) and biased (mostly student) samples.”
You are what you tweet: Personality expression and perception on Twitter (2012, background): “…based on social media may require not only the linguistic markers but also other behavioral cues, such as the engagement in the media and interactions with other users (Golbeck, Robles, Edmondson, & Turner, 2011; Golbeck et al., 2011; Yee, Duchenaut, Nelson, & Likarish, 2011; Yee et al., 2011).”
Our Twitter Profiles, Our Selves: Predicting Personality with Twitter (2011, methodology): “…is reasonable since Extraversion and Neuroticism are predictors for number of friends in the real world and number of social contacts on Facebook [10], [27], [39] -in both real life and Facebook, sociometrically popular individuals are extroverts (high in Extraversion) and emotionally stable…”
Inspectability and control in social recommenders (2012, background): “Several recent studies have extended these ideas to prediction of personality, and by derivation, behavior of a user within the system [1] in terms of both trust and distrust [15].”
Private traits and attributes are predictable from digital records of human behavior (2013, background): “Similarly, it has been shown that personality can be predicted based on the contents of personal Web sites (16), music collections (17), properties of Facebook or Twitter pro ﬁ les such as the number of friends or the density of friendship networks (18 – 21), or language used by their users (22).”
Attribute Inference Attacks in Online Social Networks (2018, methodology): “Golbeck and her collaborators [1, 21] used a user’s tweets to infer her personality through participant study.”
Overview of the 3rd Author Profiling Task at PAN 2015 (2015, background): “But, researchers have also focused on personality prediction using corpora of social network data, such as Twitter and Facebook, exploiting either linguistic features in status updates, social features such as friends count, and daily activity [19, 60, 13].”