Abstract
Twitter enables users to browse and access the latest news-related content. However, given user’s interest in a particular news-related tweet, searching for related content may be a tedious process. Formulating an effective search query is not a trivial task. And due to the often small size of smart phone screens, instead of typing, users always prefer click-based operations to retrieve related content. To address these issues, we introduce a new paradigm for news-related Twitter search called Search by Tweet(SbT). In this paradigm, a user submits a particular tweet which triggers a search task to retrieve further related tweets. In this paper, we formalize the SbT problem and propose an effective and efficient framework implementing such a functionality. At the core, we model the public Twitter stream as a dynamic graph-of-words, reflecting the importance of both words and word correlations. Given an input tweet, our framework utilizes the graph model to generate an implicit query. Our techniques demonstrate high efficiency and effectiveness as evaluated using a large-scale Twitter dataset and a user study.
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Notes
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As pre-processing steps, we remove reply-tweets, user names and stopwords. All hashtags are retained and no stemming is applied.
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Features: burst_sum, graphAvgCorr_min, clustDeg_min, clustRelDeg_sum, clustSumCorr_max.
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Thus, 96.4% of tweets have at least one ‘relevant’ query. Among these tweets, on average 4.1 out of 12.5 queries are ‘relevant’.
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Hao, X., Cheng, J., Vosecky, J., Ng, W. (2017). Towards a Query-Less News Search Framework on Twitter. In: Candan, S., Chen, L., Pedersen, T., Chang, L., Hua, W. (eds) Database Systems for Advanced Applications. DASFAA 2017. Lecture Notes in Computer Science(), vol 10178. Springer, Cham. https://doi.org/10.1007/978-3-319-55699-4_9
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