CrowdTiles:呈现基于人群的信息以满足事件驱动的信息需求

S. Whiting, K. Zhou, J. Jose, Omar Alonso, Teerapong Leelanupab
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引用次数: 11

摘要

时间在许多与近期事件相关的网络搜索信息需求中起着中心作用。对于最需要新鲜信息的近期查询,可能会有大量由世界各地的人群最近创建的高度相关的信息,特别是在维基百科和Twitter等平台上。有了这么多的用户,主流事件通常会很快反映在这些资源中。英文维基百科全书包含大量用户编辑的文章,涵盖了一系列主题。在事件期间,用户可以近乎实时地协作创建和编辑现有文章。同时,Twitter上的用户传播和讨论事件细节,少数用户对该话题具有影响力。在这个演示中,我们提出了一种新颖的方法来显示与用户搜索主题相关的最近或正在发生的事件相关的新信息和用户的摘要,从而帮助发现最新的信息。我们概述了检测由事件驱动的搜索主题的方法,识别和提取不断变化的维基百科文章段落,并找到有影响力的Twitter用户。使用这些,我们提供了一个系统,该系统在搜索结果中显示熟悉的磁贴,以显示与事件相关的维基百科文章的最新变化,以及最近发布了有关事件主题的相关信息的Twitter用户。
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CrowdTiles: presenting crowd-based information for event-driven information needs
Time plays a central role in many web search information needs relating to recent events. For recency queries where fresh information is most desirable, there is likely to be a great deal of highly-relevant information created very recently by crowds of people across the world, particularly on platforms such as Wikipedia and Twitter. With so many users, mainstream events are often very quickly reflected in these sources. The English Wikipedia encyclopedia consists of a vast collection of user-edited articles covering a range of topics. During events, users collaboratively create and edit existing articles in near real-time. Simultaneously, users on Twitter disseminate and discuss event details, with a small number of users becoming influential for the topic. In this demo, we propose a novel approach to presenting a summary of new information and users related to recent or ongoing events associated with the user's search topic, therefore aiding most recent information discovery. We outline methods to detect search topics which are driven by events, identify and extract changing Wikipedia article passages and find influential Twitter users. Using these, we provide a system which displays familiar tiles in search results to present recent changes in the event-related Wikipedia articles, as well as Twitter users who have tweeted recent relevant information about the event topics.
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Predicting web search success with fine-grained interaction data User activity profiling with multi-layer analysis Search result presentation based on faceted clustering Domain dependent query reformulation for web search CrowdTiles: presenting crowd-based information for event-driven information needs
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