TY - GEN
T1 - Recognizing pathogenic empathy in social media
AU - Abdul-Mageed, Muhammad
AU - Buffone, Anneke
AU - Peng, Hao
AU - Giorgi, Salvatore
AU - Eichstaedt, Johannes
AU - Ungar, Lyle
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2017
Y1 - 2017
N2 - Empathy is an integral part of human social life, as people care about and for others who experience adversity. However, a specific "pathogenic" form of empathy, marked by automatic contagion of negative emotions, can lead to stress and burnout. This is particularly detrimental for individuals in caregiving professions who experience empathic states more frequently, because it can result in illness and high costs for health systems. Automatically recognizing pathogenic empathy from text is potentially valuable to identify at-risk individuals and monitor burnout risk in caregiving populations. We build a model to predict this type of empathy from social media language on a data set we collected of users' Facebook posts and their answers to a new questionnaire measuring empathy. We obtain promising results in identifying individuals' empathetic states from their social media (Pearson r = 0.252, p < 0.003). © Copyright 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
AB - Empathy is an integral part of human social life, as people care about and for others who experience adversity. However, a specific "pathogenic" form of empathy, marked by automatic contagion of negative emotions, can lead to stress and burnout. This is particularly detrimental for individuals in caregiving professions who experience empathic states more frequently, because it can result in illness and high costs for health systems. Automatically recognizing pathogenic empathy from text is potentially valuable to identify at-risk individuals and monitor burnout risk in caregiving populations. We build a model to predict this type of empathy from social media language on a data set we collected of users' Facebook posts and their answers to a new questionnaire measuring empathy. We obtain promising results in identifying individuals' empathetic states from their social media (Pearson r = 0.252, p < 0.003). © Copyright 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
UR - http://www.scopus.com/inward/record.url?scp=85029453209&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85029453209&origin=recordpage
U2 - 10.1609/icwsm.v11i1.14942
DO - 10.1609/icwsm.v11i1.14942
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781577357889
T3 - Proceedings of the 11th International Conference on Web and Social Media, ICWSM 2017
SP - 448
EP - 451
BT - Proceedings of the 11th International Conference on Web and Social Media, ICWSM 2017
PB - AAAI Press
T2 - 11th International Conference on Web and Social Media, ICWSM 2017
Y2 - 15 May 2017 through 18 May 2017
ER -