Title :
Prominent Features of Rumor Propagation in Online Social Media
Author :
Sejeong Kwon ; Meeyoung Cha ; Kyomin Jung ; Wei Chen ; Yajun Wang
Author_Institution :
Korea Adv. Institue of Sci. & Technol., Daejeon, South Korea
Abstract :
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.
Keywords :
social networking (online); time series; daily shock cycles; external shock cycles; linguistic differences; online social media; online social networks; periodic time series model; rumor classification; rumor propagation; structural differences; temporal characteristics; Adaptation models; Electric shock; Mathematical model; Pragmatics; Psychology; Time series analysis; Twitter; Diffusion Network; Rumor; Sentiment Analysis; Social Media; Time Series;
Conference_Titel :
Data Mining (ICDM), 2013 IEEE 13th International Conference on
Conference_Location :
Dallas, TX
DOI :
10.1109/ICDM.2013.61