Abstract
Twitter has become a key social media for sharing information, not only for casual conversations but also for business and technologies. As the Twitter community continues to grow, an intriguing question is to determine how to obtain most valuable information the earliest by following fewest Tweeters or Tweets. This multi-criteria optimization problem exhibits similar features as in the information cascade problem for blogs. This work revises an information cascade outbreak detection algorithm to find critical Twitter accounts that disseminate the most cyber vulnerabilities the earliest. Three award functions are defined to evaluate every account’s contribution per topic from three aspects: timeliness, originality and influence. Critical users are selected according to their total contribution on a specific security category. Experiments were conducted using Tweets containing CVE information over a five-week period, to compare the proposed algorithm with account selections based on the number of followers and based on the PageRank algorithm. The results show that with the same number of users and tweets, our algorithm outperforms in both information coverage and timeliness.
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Cui, B., Moskal, S., Du, H., Yang, S.J. (2013). Who Shall We Follow in Twitter for Cyber Vulnerability?. In: Greenberg, A.M., Kennedy, W.G., Bos, N.D. (eds) Social Computing, Behavioral-Cultural Modeling and Prediction. SBP 2013. Lecture Notes in Computer Science, vol 7812. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37210-0_43
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DOI: https://doi.org/10.1007/978-3-642-37210-0_43
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