KeySee:支持关键字搜索不断发展的事件在社会流

Pei Lee, L. Lakshmanan, E. Milios
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引用次数: 17

摘要

Twitter/Facebook时间轴和论坛讨论等在线社交流已成为信息传播的普遍渠道。随着这些社交流的迅速激增,信息过载已经成为一个巨大的问题。像Twitter搜索这样的社交流上现有的关键字搜索引擎并没有成功地克服这个问题,因为它们只是返回一个压倒性的帖子列表,几乎没有聚合或语义。在这个演示中,我们提供了一个名为\keysee的新解决方案,它将帖子分组为事件,并在新帖子涌入和旧帖子淡出时跟踪事件的演变模式。我们的系统有效地解决了噪音和冗余问题。我们的演示通过允许用户指定时间跨度和指定的演化模式,支持对演化事件进行精细的关键字查询。对于每个事件结果,我们提供了各种分析视图,如频率曲线,词云和GPS分布。我们在真实的Twitter流上部署\keysee,结果表明我们的演示在质量和可用性方面都优于现有的关键字搜索引擎。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
KeySee: supporting keyword search on evolving events in social streams
Online social streams such as Twitter/Facebook timelines and forum discussions have emerged as prevalent channels for information dissemination. As these social streams surge quickly, information overload has become a huge problem. Existing keyword search engines on social streams like Twitter Search are not successful in overcoming the problem, because they merely return an overwhelming list of posts, with little aggregation or semantics. In this demo, we provide a new solution called \keysee by grouping posts into events, and track the evolution patterns of events as new posts stream in and old posts fade out. Noise and redundancy problems are effectively addressed in our system. Our demo supports refined keyword query on evolving events by allowing users to specify the time span and designated evolution pattern. For each event result, we provide various analytic views such as frequency curves, word clouds and GPS distributions. We deploy \keysee on real Twitter streams and the results show that our demo outperforms existing keyword search engines on both quality and usability.
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