Classification of rumors and counter-rumors

TY - GEN

T1 - Classification of rumors and counter-rumors

AU - Pal, Anjan

AU - Chua, Alton Y.K.

N1 - Funding Information: ACKNOWLEDGMENT This work was supported by the Ministry of Education Research Grant AcRF Tier 2 (MOE2014-T2-2-020). Publisher Copyright: © 2018 IEEE.

PY - 2018/6/21

Y1 - 2018/6/21

N2 - Rumors are posing a serious threat in this digital era. They spread fast and wide on social media to become viral. While some users are deceived by rumors, others explicitly seek to debunk them. In consequence, two opposite forces of claims, namely, rumors and counter-rumors emerge. Given that rumors and counter-rumors are antithetical and could exhibit contrary patterns, the objective of this paper is to classify the two. For this purpose, messages on a rumoring phenomenon were collected from Twitter, and a set of content- based as well as user-based features were identified to distinguish between rumors and counter-rumors. To ensure methodological rigor, a set of classification algorithms including voting was employed using five-fold cross validation. Furthermore, feature selection techniques were used to identify the top features in classifying rumors and counter-rumors. The implications of the results are discussed.

AB - Rumors are posing a serious threat in this digital era. They spread fast and wide on social media to become viral. While some users are deceived by rumors, others explicitly seek to debunk them. In consequence, two opposite forces of claims, namely, rumors and counter-rumors emerge. Given that rumors and counter-rumors are antithetical and could exhibit contrary patterns, the objective of this paper is to classify the two. For this purpose, messages on a rumoring phenomenon were collected from Twitter, and a set of content- based as well as user-based features were identified to distinguish between rumors and counter-rumors. To ensure methodological rigor, a set of classification algorithms including voting was employed using five-fold cross validation. Furthermore, feature selection techniques were used to identify the top features in classifying rumors and counter-rumors. The implications of the results are discussed.

KW - classification

KW - counter-rumor

KW - machine learning

KW - rumor

KW - social media

KW - voting

UR - http://www.scopus.com/inward/record.url?scp=85050161093&partnerID=8YFLogxK

U2 - 10.1109/INFOMAN.2018.8392814

DO - 10.1109/INFOMAN.2018.8392814

M3 - Conference contribution

AN - SCOPUS:85050161093

T3 - 2018 4th International Conference on Information Management, ICIM 2018

SP - 81

EP - 85

BT - 2018 4th International Conference on Information Management, ICIM 2018

PB - IEEE

T2 - 4th International Conference on Information Management, ICIM 2018

Y2 - 25 May 2018 through 27 May 2018

ER -