Inferring Perceived Demographics from User Emotional Tone and User-Environment Emotional Contrast

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pages 1567–1578, Berlin, Germany, August 7-12, 2016. c⃝2016 Association for Computational Linguistics Inferring Perceived Demographics from User Emotional Tone and User-Environment Emotional Contrast Svitlana Volkova Johns Hopkins University (now at Pacific Northwest National Laboratory) Baltimore, MD, 21218, USA svitlana@jhu.edu Yoram Bachrach Microsoft Research Cambridge, UK CB1 2FB yobach@microsoft.com Abstract We examine communications in a social network to study user emotional contrast – the propensity of users to express dif- ferent emotions than those expressed by their neighbors. Our analysis is based on a large Twitter dataset, consisting of the tweets of 123,513 users from the USA and Canada. Focusing on Ekman’s basic emo- tions, we analyze differences between the emotional tone expressed by these users and their neighbors of different types, and correlate these differences with perceived user demographics. We demonstrate that many perceived demographic traits corre- late with the emotional contrast between users and their neighbors. Unlike other ap- proaches on inferring user attributes that rely solely on user communications, we explore the network structure and show that it is possible to accurately predict a range of perceived demographic traits based solely on the emotions emanating from users and their neighbors. 1 Introduction The explosion of social media services like Twit- ter, Google+ and Facebook have led to a grow- ing application potential for personalization in human computer systems such as personalized intelligent user interfaces, recommendation sys- tems, and targeted advertising. Researchers have started mining these massive volumes of person- alized and diverse data produced in public social media with the goal of learning about their de- mographics (Burger et al., 2011; Zamal et al., 2012; Volkova et al., 2015) and personality (Gol- beck et al., 2011; Kosinski et al., 2013),1 lan- 1https://apps.facebook.com/snpredictionapp/ guage variation (Eisenstein et al., 2014; Kern et al., 2014; Bamman et al., 2014),2 likes and in- terests (Bachrach et al., 2012; Lewenberg et al., 2015), emotions and opinions they express (Bollen et al., 2011b; Volkova and Bachrach, 2015), their well-being (Schwartz et al., 2013) and their inter- actions with online environment (Bachrach, 2015; Kalaitzis et al., 2016). The recent study has shown that the environment in a social network has a huge influence on user behavior and the tone of the mes- sages users generate (Coviello et al., 2014; Ferrara and Yang, 2015a). People vary in the ways they respond to the emotional tone of their environment in a social network. Some people tend to send out messages with a positive emotional tone, while others tend to express more negative emotions such as sad- ness or fear. Some of us are likely to share peer messages that are angry, whereas others filter out such messages. In this work we focus on the prob- lem of predicting user perceived demographics by examining the emotions expressed by users and their immediate neighbors. We first define the user emotional tone, the environment emotional tone, and the user-environment emotional contrast. Definition 1 Environment emotional tone is the …

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