Abstract
This paper describes the LiveTweet application, a system for automatically analysing and predicting the interestingness of microblog posts. Based on a stream of recent microblog posts the system tracks user interactions on Twitter that indicate interesting content. An incremental Naive Bayes model is trained to learn the characteristics of tweets which are considered interesting by the users. Finally, the probability of a microblog post to be retweeted is used as metric for its interestingness.
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References
Gottron, T., Lipka, N.: A Comparison of Language Identification Approaches on Short, Query-Style Texts. In: Gurrin, C., He, Y., Kazai, G., Kruschwitz, U., Little, S., Roelleke, T., Rüger, S., van Rijsbergen, K. (eds.) ECIR 2010. LNCS, vol. 5993, pp. 611–614. Springer, Heidelberg (2010)
Naveed, N., Gottron, T., Kunegis, J., Che Alhadi, A.: Bad news travel fast: A content-based analysis of interestingness on twitter. In: Proc. 3rd International Conf. on Web Science (2011)
Naveed, N., Gottron, T., Kunegis, J., Che Alhadi, A.: Searching microblogs: Coping with sparsity and document quality. In: Proc. 20th ACM Conf. on Information and Knowledge Management (2011)
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Alhadi, A.C., Gottron, T., Kunegis, J., Naveed, N. (2012). LiveTweet: Monitoring and Predicting Interesting Microblog Posts. In: Baeza-Yates, R., et al. Advances in Information Retrieval. ECIR 2012. Lecture Notes in Computer Science, vol 7224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28997-2_66
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DOI: https://doi.org/10.1007/978-3-642-28997-2_66
Publisher Name: Springer, Berlin, Heidelberg
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