Abstract

In recent times, social media sites such as Twitter have been extensively used for debating politics and public policies. These debates span millions of tweets and numerous topics of public importance. Thus, it is imperative that this vast trove of data is tapped in order to gain insights into public opinion especially on hotly contested issues such as abortion, gun reforms etc. Thus, in our work, we aim to gauge users' stance on such topics in Twitter. We propose ReLP, a semi-supervised framework using a retweet-based label propagation algorithm coupled with a supervised classifier to identify users with differing opinions. In particular, our framework is designed such that it can be easily adopted to different domains with little human supervision while still producing excellent accuracy.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages153-160
Number of pages8
Volume8393 LNCS
ISBN (Print)9783319055787
DOIs
StatePublished - 2014
Event7th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, SBP 2014 - Washington, DC, United States
Duration: Apr 1 2014Apr 4 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8393 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other7th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, SBP 2014
CountryUnited States
CityWashington, DC
Period4/1/144/4/14

Fingerprint

Gages
Labels
Classifiers
Public Policy
Social Media
Gauge
Classifier
Propagation
Framework
Human

Keywords

  • label propagation
  • opinion mining
  • polarity detection
  • semi-supervised

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Rajadesingan, A., & Liu, H. (2014). Identifying users with opposing opinions in Twitter debates. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8393 LNCS, pp. 153-160). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8393 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-05579-4_19

Identifying users with opposing opinions in Twitter debates. / Rajadesingan, Ashwin; Liu, Huan.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8393 LNCS Springer Verlag, 2014. p. 153-160 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8393 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Rajadesingan, A & Liu, H 2014, Identifying users with opposing opinions in Twitter debates. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 8393 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8393 LNCS, Springer Verlag, pp. 153-160, 7th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, SBP 2014, Washington, DC, United States, 4/1/14. https://doi.org/10.1007/978-3-319-05579-4_19
Rajadesingan A, Liu H. Identifying users with opposing opinions in Twitter debates. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8393 LNCS. Springer Verlag. 2014. p. 153-160. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-05579-4_19
Rajadesingan, Ashwin ; Liu, Huan. / Identifying users with opposing opinions in Twitter debates. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8393 LNCS Springer Verlag, 2014. pp. 153-160 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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