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

Published: 17 April 2012 Publication History

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.

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      cover image ACM Other conferences
      TempWeb '12: Proceedings of the 2nd Temporal Web Analytics Workshop
      April 2012
      55 pages
      ISBN:9781450311885
      DOI:10.1145/2169095
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Published: 17 April 2012

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      Author Tags

      1. Twitter
      2. information cascade
      3. information diffusion
      4. microblog
      5. social network

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      • (2023)Dynamic evolution of information diffusion networks of news agencies in emergencies: a case study of microblogs of urban fire disasters on Sina WeiboMultimedia Tools and Applications10.1007/s11042-023-16498-083:9(25287-25319)Online publication date: 19-Aug-2023
      • (2018)Efficient Discovery of Weighted Frequent Itemsets in Very Large Transactional Databases: A Re-visit2018 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2018.8622642(723-732)Online publication date: Dec-2018
      • (2017)Review on towards efficient mining of recurrent patterns in time series data2017 International Conference on Innovative Mechanisms for Industry Applications (ICIMIA)10.1109/ICIMIA.2017.7975525(572-575)Online publication date: Feb-2017

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