Analyzing patterns of information cascades based on users' influence and posting behaviors

  • Authors:

  • Geerajit Rattanaritnont;Masashi Toyoda;Masaru Kitsuregawa

  • Affiliations:

  • The University of Tokyo, Komaba, Meguro-ku, Tokyo, Japan;The University of Tokyo, Komaba, Meguro-ku, Tokyo, Japan;The University of Tokyo, Komaba, Meguro-ku, Tokyo, Japan

  • Venue:
  • Proceedings of the 2nd Temporal Web Analytics Workshop
  • Year:
  • 2012

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Abstract

Nowadays people can share useful information on social networking sites such as Facebook and Twitter. The information is spread over the networks when it is forwarded or copied repeatedly from friends to friends. This phenomenon is so called "information cascade", and has been studied long time since it sometimes has an impact on the real world. Various social activities tends to have different ways of cascade on the social networks. Our focus in this study is on characterizing the cascade patterns according to users' influence and posting behaviors in various topics. The cascade patterns could be useful for various organizations to consider the strategy of public relations activities. We explore four measures which are cascade ratio, tweet ratio, time of tweet, and exposure curve. Our results show that hashtags in different topics have different cascade patterns in term of these measures. However, some hashtags even in the same topic have different cascade patterns. We discover that such kind of hidden relationship between topics can be surprisingly revealed by using only our four measures rather than considering tweet contents. Finally, our results also show that cascade ratio and time of tweet are the most effective measures to distinguish cascade patterns in different topics.