Enthusiasm and support: Alternative sentiment classification for social movements on social media

Shubhanshu Mishra; Kirstin Phelps; Sneha Agarwal; Johna Picco; Jinlong Guo; Jana Diesner

doi:10.1145/2615569.2615667

Enthusiasm and support: Alternative sentiment classification for social movements on social media

Shubhanshu Mishra, Kirstin Phelps, Sneha Agarwal, Johna Picco, Jinlong Guo, Jana Diesner

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

We present a novel sentiment classifier particularly designed for modeling and analyzing social movements; capturing levels of support (supportive versus non-supportive) and degrees of enthusiasm (enthusiastic versus passive). The resulting computational solution can help organizations involved with social causes to disseminate messages in a more informed and effective fashion; potentially leading to greater impact. Our findings suggest that enthusiastic and supportive tweets are more prevalent in tweets about social causes than other types of tweets on Twitter.

Original language	English (US)
Title of host publication	WebSci 2014 - Proceedings of the 2014 ACM Web Science Conference
Publisher	Association for Computing Machinery
Pages	261-262
Number of pages	2
ISBN (Print)	9781450326223
DOIs	https://doi.org/10.1145/2615569.2615667
State	Published - 2014
Event	6th ACM Web Science Conference, WebSci 2014 - Bloomington, IN, United States Duration: Jun 23 2014 → Jun 26 2014

Publication series

Name	WebSci 2014 - Proceedings of the 2014 ACM Web Science Conference

Other

Other	6th ACM Web Science Conference, WebSci 2014
Country/Territory	United States
City	Bloomington, IN
Period	6/23/14 → 6/26/14

Keywords

Data classification
Data corpus
Human factors
Social causes
Social network analysis

ASJC Scopus subject areas

Computer Networks and Communications

Online availability

10.1145/2615569.2615667

Library availability

Discover UIUC Full Text

Cite this

Mishra, S., Phelps, K., Agarwal, S., Picco, J., Guo, J., & Diesner, J. (2014). Enthusiasm and support: Alternative sentiment classification for social movements on social media. In WebSci 2014 - Proceedings of the 2014 ACM Web Science Conference (pp. 261-262). (WebSci 2014 - Proceedings of the 2014 ACM Web Science Conference). Association for Computing Machinery. https://doi.org/10.1145/2615569.2615667

Enthusiasm and support: Alternative sentiment classification for social movements on social media. / Mishra, Shubhanshu; Phelps, Kirstin; Agarwal, Sneha et al.
WebSci 2014 - Proceedings of the 2014 ACM Web Science Conference. Association for Computing Machinery, 2014. p. 261-262 (WebSci 2014 - Proceedings of the 2014 ACM Web Science Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Mishra, S, Phelps, K, Agarwal, S, Picco, J, Guo, J & Diesner, J 2014, Enthusiasm and support: Alternative sentiment classification for social movements on social media. in WebSci 2014 - Proceedings of the 2014 ACM Web Science Conference. WebSci 2014 - Proceedings of the 2014 ACM Web Science Conference, Association for Computing Machinery, pp. 261-262, 6th ACM Web Science Conference, WebSci 2014, Bloomington, IN, United States, 6/23/14. https://doi.org/10.1145/2615569.2615667

Mishra S, Phelps K, Agarwal S, Picco J, Guo J, Diesner J. Enthusiasm and support: Alternative sentiment classification for social movements on social media. In WebSci 2014 - Proceedings of the 2014 ACM Web Science Conference. Association for Computing Machinery. 2014. p. 261-262. (WebSci 2014 - Proceedings of the 2014 ACM Web Science Conference). doi: 10.1145/2615569.2615667

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abstract = "We present a novel sentiment classifier particularly designed for modeling and analyzing social movements; capturing levels of support (supportive versus non-supportive) and degrees of enthusiasm (enthusiastic versus passive). The resulting computational solution can help organizations involved with social causes to disseminate messages in a more informed and effective fashion; potentially leading to greater impact. Our findings suggest that enthusiastic and supportive tweets are more prevalent in tweets about social causes than other types of tweets on Twitter.",

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AB - We present a novel sentiment classifier particularly designed for modeling and analyzing social movements; capturing levels of support (supportive versus non-supportive) and degrees of enthusiasm (enthusiastic versus passive). The resulting computational solution can help organizations involved with social causes to disseminate messages in a more informed and effective fashion; potentially leading to greater impact. Our findings suggest that enthusiastic and supportive tweets are more prevalent in tweets about social causes than other types of tweets on Twitter.

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Enthusiasm and support: Alternative sentiment classification for social movements on social media

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Online availability

Library availability

Related links

Fingerprint

Tweet IDs annotated for enthusiasm and support towards social causes: CTE, cyberbullying, and LGBT

Cite this

Enthusiasm and support: Alternative sentiment classification for social movements on social media

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Online availability

Library availability

Related links

Fingerprint

Datasets

Tweet IDs annotated for enthusiasm and support towards social causes: CTE, cyberbullying, and LGBT

Cite this