• DocumentCode
    3166377
  • 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
  • fYear
    2013
  • fDate
    7-10 Dec. 2013
  • Firstpage
    1103
  • Lastpage
    1108
  • 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;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Data Mining (ICDM), 2013 IEEE 13th International Conference on
  • Conference_Location
    Dallas, TX
  • ISSN
    1550-4786
  • Type

    conf

  • DOI
    10.1109/ICDM.2013.61
  • Filename
    6729605