Empathy is the tendency to understand and share others' thoughts and feelings. Literature in psychology has shown through surveys potential beneficial implications of empathy. Prior psychology literature showed that a particular type of empathy called "situational empathy"- - an immediate empathic response to a triggering situation (e.g., a distressing situation) - - is reflected in the language people use in response to the situation. However, this has not so far been properly measured at scale. In this work, we collected 4k textual reactions (and corresponding situational empathy labels) to different stories. Driven by theoretical concepts, we developed computational models to predict situational empathy from text and, in so doing, we built and made available a list of empathy-related words. When applied to Reddit posts and movie transcripts, our models produced results that matched prior theoretical findings, offering evidence of external validity and suggesting its applicability to unstructured data. The capability of measuring proxies for empathy at scale might benefit a variety of areas such as social media, digital healthcare, and workplace well-being.

The Language of Situational Empathy / Zhou, Ke; Aiello, Luca Maria; Scepanovic, Sanja; Quercia, Daniele; Konrath, Sara. - In: PROCEEDINGS OF THE ACM ON HUMAN-COMPUTER INTERACTION. - ISSN 2573-0142. - 5:(2021), pp. 1-19. (Intervento presentato al convegno ACM CSCW) [10.1145/3449087].

The Language of Situational Empathy

Quercia, Daniele;
2021

Abstract

Empathy is the tendency to understand and share others' thoughts and feelings. Literature in psychology has shown through surveys potential beneficial implications of empathy. Prior psychology literature showed that a particular type of empathy called "situational empathy"- - an immediate empathic response to a triggering situation (e.g., a distressing situation) - - is reflected in the language people use in response to the situation. However, this has not so far been properly measured at scale. In this work, we collected 4k textual reactions (and corresponding situational empathy labels) to different stories. Driven by theoretical concepts, we developed computational models to predict situational empathy from text and, in so doing, we built and made available a list of empathy-related words. When applied to Reddit posts and movie transcripts, our models produced results that matched prior theoretical findings, offering evidence of external validity and suggesting its applicability to unstructured data. The capability of measuring proxies for empathy at scale might benefit a variety of areas such as social media, digital healthcare, and workplace well-being.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2996097
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