TY - GEN
T1 - Collective smile
T2 - 18th ACM International Conference on Computer-Supported Cooperative Work and Social Computing, CSCW 2015
AU - Abdullah, Saeed
AU - Murnane, Elizabeth L.
AU - Costa, Jean M.R.
AU - Choudhury, Tanzeem
N1 - Publisher Copyright:
© 2015 ACM.
PY - 2015/2/28
Y1 - 2015/2/28
N2 - The increasing adoption of social media provides unprecedented opportunities to gain insight into human nature at vastly broader scales. Regarding the study of populationwide sentiment, prior research commonly focuses on textbased analyses and ignores a treasure trove of sentimentladen content: images. In this paper, we make methodological and computational contributions by introducing the Smile Index as a formalized measure of societal happiness. Detecting smiles in 9 million geo-located tweets over 16 months, we validate our Smile Index against both text-based techniques and self-reported happiness. We further make observational contributions by applying our metric to explore temporal trends in sentiment, relate public mood to societal events, and predict economic indicators. Reflecting upon the innate, language-independent aspects of facial expressions, we recommend future improvements and applications to enable robust, global-level analyses. We conclude with implications for researchers studying and facilitating the expression of collective emotion through socio-Technical systems.
AB - The increasing adoption of social media provides unprecedented opportunities to gain insight into human nature at vastly broader scales. Regarding the study of populationwide sentiment, prior research commonly focuses on textbased analyses and ignores a treasure trove of sentimentladen content: images. In this paper, we make methodological and computational contributions by introducing the Smile Index as a formalized measure of societal happiness. Detecting smiles in 9 million geo-located tweets over 16 months, we validate our Smile Index against both text-based techniques and self-reported happiness. We further make observational contributions by applying our metric to explore temporal trends in sentiment, relate public mood to societal events, and predict economic indicators. Reflecting upon the innate, language-independent aspects of facial expressions, we recommend future improvements and applications to enable robust, global-level analyses. We conclude with implications for researchers studying and facilitating the expression of collective emotion through socio-Technical systems.
UR - http://www.scopus.com/inward/record.url?scp=84968755010&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84968755010&partnerID=8YFLogxK
U2 - 10.1145/2675133.2675186
DO - 10.1145/2675133.2675186
M3 - Conference contribution
AN - SCOPUS:84968755010
T3 - CSCW 2015 - Proceedings of the 2015 ACM International Conference on Computer-Supported Cooperative Work and Social Computing
SP - 361
EP - 374
BT - CSCW 2015 - Proceedings of the 2015 ACM International Conference on Computer-Supported Cooperative Work and Social Computing
PB - Association for Computing Machinery, Inc
Y2 - 14 March 2015 through 18 March 2015
ER -