In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics. Sharifi, B., Hutton, M.-A., Kalita, J.: Summarizing microblogs automatically. Russell, M.A.: Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More. Association for Computational Linguistics (2011) In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Ritter, A., Clark, S., Etzioni, O.: Named entity recognition in tweets: An experimental study. Journal of the American Society for Information Science and Technology 62(5), 902–918 (2011) Naaman, M., Becker, H., Gravano, L.: Hip and trendy: Characterizing emerging trends on Twitter. In: 2012 IEEE 28th International Conference on Data Engineering (ICDE). Li, R., et al.: Tedas: A twitter-based event detection and analysis system. In: 2011 IEEE 11th International Conference on Data Mining Workshops (ICDMW). Lee, K., et al.: Twitter trending topic classification. Kwak, H., et al.: What is Twitter, a social network or a news media? In: Proceedings of the 19th International Conference on World Wide Web. In: 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third International Conference on Social Computing (SocialCom). Inouye, D., Kalita, J.K.: Comparing twitter summarization algorithms for multiple post summaries. Association for Computational Linguistics (2005) In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics. International Journal of Web Based Communities 9(1), 122–139 (2013)įinkel, J.R., Grenager, T., Manning, C.: Incorporating non-local information into information extraction systems by gibbs sampling. Keywordsīenhardus, J., Kalita, J.: Streaming trend detection in twitter. We conducted human experiments with 20 postgraduate students. Then, we applied four types of information retrieval approaches (key factor extraction, named entity recognition, topic modelling, and automatic summarization) for extracting the representative contents of trending topics. We first collected the trending topics and tweets related to them. The goal of this paper is finding the most successful method that uses to retrieve the representative contents of trending topics in order to disambiguate the meaning of topics. It is almost impossible to identify what a trending topic is about unless you read all related tweets. Trending Topics on Twitter shows the list of top 10 trending topics but each topic consists of short phrase or keyword, which does not contain any explanation of those meanings. Then, they publish these topics on the list, called ‘Trending Topics’. Twitter monitors their users’ postings and detects the most discussed topics of the moment. Twitter is one of the most popular social media services that allow users to share and spread information.
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