TY - GEN
T1 - Identifying users with opposing opinions in Twitter debates
AU - Rajadesingan, Ashwin
AU - Liu, Huan
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - label propagation
KW - opinion mining
KW - polarity detection
KW - semi-supervised
UR - http://www.scopus.com/inward/record.url?scp=84958523837&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84958523837&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-05579-4_19
DO - 10.1007/978-3-319-05579-4_19
M3 - Conference contribution
SN - 9783319055787
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 153
EP - 160
BT - Social Computing, Behavioral-Cultural Modeling, and Prediction - 7th International Conference, SBP 2014, Proceedings
PB - Springer Verlag
T2 - 7th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, SBP 2014
Y2 - 1 April 2014 through 4 April 2014
ER -