Am I who I say I am? Unobtrusive self-representation and personality recognition on Facebook

Abstract

Across social media platforms users (sub)consciously represent themselves in a way which is appropriate for their intended audience. This has unknown impacts on studies with unobtrusive designs based on digital (social) platforms, and studies of contemporary social phenomena in online settings. A lack of appropriate methods to identify, control for, and mitigate the effects of self-representation, the propensity to express socially responding characteristics or self-censorship in digital settings, hinders the ability of researchers to confidently interpret and generalize their findings. This article proposes applying boosted regression modelling to fill this research gap. A case study of paid Amazon Mechanical Turk workers (n = 509) is presented where workers completed psychometric surveys and provided anonymized access to their Facebook timelines. Our research finds indicators of self-representation on Facebook, facilitating suggestions for its mitigation. We validate the use of LIWC for Facebook personality studies, as well as find discrepancies with extant literature about the use of LIWC-only approaches in unobtrusive designs. Using survey data and LIWC sentiment categories as predictors, the boosted regression model classified the Five Factor personality model with an average accuracy of 74.6%. The contribution of this work is an accurate prediction of psychometric information based on short, informal text.

Validity Evidence for Off-the-Shelf Language-Based Personality Assessment Using Video Interviews: Convergent and Discriminant Relationships with Self and Observer Ratings

“…Such approaches hold potential to reduce appearance, gender, and race biases that influence selection decisions. Other researchers claim to have outperformed IBM Watson PI, Schwartz et al (2013), andPark et al (2015) in predicting personality from social media posts, but higher accuracy has only been achieved when language features were combined with self-reports of attitudes and behavior as predictors of self-reported personality (Hall & Caton, 2017). To our knowledge, this study is the first to examine the convergent and discriminant validity evidence of language-based personality assessment with self and observer ratings of personality in the context of a video interview.…”

Modeling Personality vs. Modeling Personalidad

“…Similarly, we examined the impact of age on the personality models by performing the analysis for a balanced population of the three different age ranges. We conducted this procedure by randomly sub-sampling the population of [26][27][28][29][30][31][32][33][34] year old individuals to match the population size of the group with the lowest number of individuals (i.e. [35][36][37][38][39][40][41][42][43][44] year old group) and repeated it 10 times.…”

«Много Здесь, Кто В Стриме…» Распространение Социальных Медиа И Девальвация Основных Средств Массовой Информации В Венгрии

“…Наравне с описательными исследованиями появились и те, которые пользуются методами лингвистического и стилистического анализа для обеспечения безопасности в интернет-пространстве и противодействия компьютерным преступлениям. Холл и Катон [Hall, Caton, 2017] рассматривают метод LIWC (Linguistic Inquiry and Word Count) для выявления самопрезентации и цифровой идентичности, а А. Воробьева [Воробьева, 2017]…”