TY - JOUR
T1 - The Language of Situational Empathy
AU - Zhou, Ke
AU - Aiello, Luca Maria
AU - Scepanovic, Sanja
AU - Quercia, Daniele
AU - Konrath, Sara
N1 - Publisher Copyright:
© 2021 ACM.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/4/13
Y1 - 2021/4/13
N2 - 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.
AB - 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.
KW - computational model
KW - empathy
KW - lexicon
UR - http://www.scopus.com/inward/record.url?scp=85102061894&partnerID=8YFLogxK
U2 - 10.1145/3449087
DO - 10.1145/3449087
M3 - Article
AN - SCOPUS:85102061894
SN - 2573-0142
VL - 5
JO - Proceedings of the ACM on Human-Computer Interaction
JF - Proceedings of the ACM on Human-Computer Interaction
IS - CSCW1
M1 - 3449087
ER -