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Generating timeline summaries with social media attention

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Abstract

Timeline generation is an important research task which can help users to have a quick understanding of the overall evolution of one given topic. Previous methods simply split the time span into fixed, equal time intervals without studying the role of the evolutionary patterns of the underlying topic in timeline generation. In addition, few of these methods take users’ collective interests into considerations to generate timelines.

We consider utilizing social media attention to address these two problems due to the facts: 1) social media is an important pool of real users’ collective interests; 2) the information cascades generated in it might be good indicators for boundaries of topic phases. Employing Twitter as a basis, we propose to incorporate topic phases and user’s collective interests which are learnt from social media into a unified timeline generation algorithm.We construct both one informativeness-oriented and three interestingness-oriented evaluation sets over five topics.We demonstrate that it is very effective to generate both informative and interesting timelines. In addition, our idea naturally leads to a novel presentation of timelines, i.e., phase based timelines, which can potentially improve user experience.

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Author information

Authors and Affiliations

  1. School of Information, Renmin University of China, Beijing, 100872, China

    Wayne Xin Zhao & Ji-Rong Wen

  2. Beijing Key Laboratory of Big Data Management and Analysis Methods, Renmin University of China, Beijing, 100872, China

    Ji-Rong Wen

  3. School of Electronics Engineering and Computer Science, Peking University, Beijing, 100871, China

    Xiaoming Li

  4. Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data, Beijing, 100144, China

    Wayne Xin Zhao

Authors
  1. Wayne Xin Zhao
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  2. Ji-Rong Wen
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  3. Xiaoming Li
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Corresponding author

Correspondence to Wayne Xin Zhao.

Additional information

Wayne Xin Zhao is currently an assistant professor at the School of Information, Renmin University of China, China. He received the PhD degree from Peking University, China in 2014. His research interests are web text mining and natural language processing. He has published several referred papers in international conferences and journals such as ACL, EMNLP, COLING, ECIR, CIKM, SIGIR, SIGKDD, ACM TOIS, ACM TIST and IEEE TKDE.

Ji-Rong Wen is a professor at the School of Information, Renmin University of China, China. Before that, he was a senior researcher and group manager of the Web Search and Mining Group at MicroSesearch Asia (MSRA), China since 2008. He has published extensively on prestigious international conferences/journals and served as program committee members or chairs in many international conferences. He was the chair of theWWWin China track of the 17thWorldWideWeb conference. He is currently the associate editor of ACM Transactions on Information Systems (TOIS).

Xiaoming Li is a professor at the School of Electronic Engineering and Computer Science and the director of Institute of Network Computing and Information Systems in Peking University, China. He is a senior member of IEEE and currently served as vice president of China Computer Federation. His research interests include search engine and web mining, and web technology enabled social sciences.

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Zhao, W.X., Wen, JR. & Li, X. Generating timeline summaries with social media attention. Front. Comput. Sci. 10, 702–716 (2016). https://doi.org/10.1007/s11704-015-5145-3

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  • DOI: https://doi.org/10.1007/s11704-015-5145-3

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