Twitter summarization with social-temporal context

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• 作者：Ruifang He ; Yang Liu ; Guangchuan Yu ; Jiliang Tang ; Qinghua Hu…
• 关键词：Twitter summarization ; Social media ; Time point detection ; Wavelet denoising ; Social ; temporal context
• 刊名：World Wide Web
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：20
• 期：2
• 页码：267-290
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Information Systems Applications (incl.Internet); Database Management; Operating Systems;
• 出版者：Springer US
• ISSN：1573-1413
• 卷排序：20

文摘

Twitter is one of the most popular social media platforms for online users to create and share information. Tweets are short, informal, and large-scale, which makes it difficult for online users to find reliable and useful information, arising the problem of Twitter summarization. On the one hand, tweets are short and highly unstructured, which makes traditional document summarization methods difficult to handle Twitter data. On the other hand, Twitter provides rich social-temporal context beyond texts, bringing about new opportunities. In this paper, we investigate how to exploit social-temporal context for Twitter summarization. In particular, we provide a methodology to model temporal context globally and locally, and propose a novel unsupervised summarization framework with social-temporal context for Twitter data. To assess the proposed framework, we manually label a real-world Twitter dataset. Experimental results from the dataset demonstrate the importance of social-temporal context in Twitter summarization.