Status: completed
Description
Emotions are social information (Van Kleef, 2009), and serve important social functions (Niedenthal & Brauer, 2012), that help people cope with a given situation (Van Kleef, Homan, & Cheshin, 2012). Emotion is increasingly expressed in writing, through social media such as chat, Twitter and Facebook, and prevails in written interactions between service organizations and customers (e.g., Fisher, Rupová, & Bittnerová, 2014; Fisher, 2013; Ofcom, 2013). Yet, the understanding of emotion effects in written interactions between customers and service organizations is still extremely limited. This will be the focus of the current work. We propose to build on available tools for automated linguistic analyses to study emotion in written text (Bak, Kim, & Oh, 2012; Garas, Garcia, Skowron, & Schweitzer, 2012; Kanavos et al., 2014; Kim, Bak, & Oh, 2012). Although these tools exist, researchers have barely studied the identification and effects of emotion in spontaneous interpersonal written communication. Our study is unique in providing a non-obtrusive assessment of emotions of multiple interactions, and in being able to track emotions and investigate corresponding effects continuously over time in multiple interactions, using big data.
Our study will address three gaps in available research on emotion: (a) Beyond a Restricted Range of Individual Episodes. Available research primarily studied emotion of one person (Barsade, 2002; Lelieveld, Van Dijk, Van Beest & Van Kleef, 2013; Eastwood, Smilek, & Merikle, 2003; Jia, Tong, & Lee, 2014) or a limited set of emotion episodes (e.g., Deng, Sang, & Luan, 2013; Gruber, Kogan, Mennin, & Murray, 2013; Pychyl, Lee, Thibodeau, & Blunt, 2000); we will study full sequences of interactions that carry emotions, thus enabling the identification of cumulative effects of emotion over time. (b) Beyond One Individual: Available research has paid limited attention to emotion of multiple partners in a social interaction (Hareli & Rafaeli, 2008); we will look at emotion dynamics of individuals interacting with a number of partners simultaneously. (c) Beyond Traditional Data and Tools: Available research relied heavily on self-report measures of emotion, which has multiple limitations (Donaldson & Grant-Vallone, 2002; Paulhus & Vazire, 2005). We introduce the merit of automated emotion detection in text. The tools we will use build on previous research in psychology (Pennebaker, Francis, & Booth, 2001; Tausczik & Pennebaker, 2010; Thelwall, 2013; Thelwall, Buckley, Paltoglou, Cai, & Kappas, 2010) and in computer science (Liu, 2012; Pang & Lee, 2008). Using these new tools, we will conduct empirical studies of a rich archive of live, online communication chats. Thus, our project will show the merit of a new platform for research and afford findings on the effects of emotion in interpersonal communication.
Involved Persons
