Prominent Features of Rumor Propagation in Online Social Media

• Published in: Proceedings (IEEE International Conference on Data Mining) pp. 1103 - 1108
• Main Authors: Kwon, Sejeong, Cha, Meeyoung, Jung, Kyomin, Chen, Wei, Wang, Yajun
• Format: Conference Proceeding Journal Article
• Language: English
• Published: IEEE 01.12.2013
• Subjects: Adaptation models; Classification; Conferences; Data mining; Diffusion; Diffusion Network; Electric shock; Linguistics; Mathematical model; Online; Pragmatics; Psychology; Rumor; Sentiment Analysis; Social Media; Social networks; Temporal logic; Time Series; Time series analysis; Twitter
• DOI: 10.1109/ICDM.2013.61
• ISSN: 1550-4786

The problem of identifying rumors is of practical importance especially in online social networks, since information can diffuse more rapidly and widely than the offline counterpart. In this paper, we identify characteristics of rumors by examining the following three aspects of diffusion: temporal, structural, and linguistic. For the temporal characteristics, we propose a new periodic time series model that considers daily and external shock cycles, where the model demonstrates that rumor likely have fluctuations over time. We also identify key structural and linguistic differences in the spread of rumors and non-rumors. Our selected features classify rumors with high precision and recall in the range of 87% to 92%, that is higher than other states of the arts on rumor classification.