Exploring Stylistic Variation with Age and Income on Twitter

Abstract

Writing style allows NLP tools to adjust to the traits of an author. In this paper, we explore the relation between stylistic and syntactic features and authors' age and income. We confirm our hypothesis that for numerous feature types writing style is predictive of income even beyond age. We analyze the predictive power of writing style features in a regression task on two data sets of around 5,000 Twitter users each. Additionally, we use our validated features to study daily variations in writing style of users from distinct income groups. Temporal stylistic patterns not only provide novel psychological insight into user behavior, but are useful for future research and applications in social media.

You Write like You Eat: Stylistic Variation as a Predictor of Social Stratification

“…By contrast, there has been relatively little work on socio-economic status. Flekova et al (2016) show that textual features can predict income, demonstrating a relationship between this and age. Lampos et al (2016) also report good results on inferring the socio-economic status of social media users from text.…”

Beyond Binary Labels: Political Ideology Prediction of Twitter Users

“…* Work carried out during a research visit at the University of Pennsylvania User trait prediction from text is based on the assumption that language use reflects a user's demographics, psychological states or preferences. Applications include prediction of age (Rao et al, 2010;Flekova et al, 2016b), gender (Burger et al, 2011;Sap et al, 2014), personality (Schwartz et al, 2013;, socioeconomic status (Preoţiuc-Pietro et al, 2015a,b;Liu et al, 2016c), popularity (Lampos et al, 2014) or location (Cheng et al, 2010).…”

Listening between the Lines: Learning Personal Attributes from Conversations

“…Social Media User Profiling: The rapid growth of social media has led to a massive volume of user-generated informal text, which sometimes mimics conversational utterances. A great deal of work has been dedicated to automatically identify latent demographic features of online users, including age and gender [3,4,8,9,17,[34][35][36]41], political orientation and ethnicity [26,[32][33][34]41], regional origin [8,34], personality [14,36], as well as occupational class that can be mapped to income [10,31]. Most of these works focus on user-generated content from Twitter, with a few exceptions that explore Facebook [35,36] or Reddit [8,14] posts.…”