Extracting Topics with Focused Communities for Social Content Recommendation
Extracting Topics with Focused Communities for Social Content Recommendation
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DOI：
10.1145/2998181.2998259
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发表时间：
2017-02
期刊：
影响因子：
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通讯作者：
T. Georgiou;A. E. Abbadi;Xifeng Yan
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文献类型：
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作者：
T. Georgiou;A. E. Abbadi;Xifeng Yan
深入了解社交媒体讨论以及参与这些讨论的用户的人口统计数据对于商业或政治分析等许多应用程序至关重要。这种理解及其对真实的世界的影响可以通过社交媒体的自动摘要来实现。趋势主题被提供作为高级内容推荐系统，其中如果用户认为所显示的主题有趣，则建议用户查看相关内容。然而，识别关注每个主题的用户的特征可以提高重要性，即使对于可能不受欢迎或突发的主题。我们定义了一种方法来描述用户群集中在这些主题，并提出了一个有效和准确的算法来提取这样的社区。通过定性和定量的实验，我们观察到，具有强烈的社区焦点的主题是有趣的，更有可能抓住用户的注意力。
A thorough understanding of social media discussions and the demographics of the users involved in these discussions has become critical for many applications like business or political analysis. Such an understanding and its ramifications on the real world can be enabled through the automatic summarization of Social Media. Trending topics are offered as a high level content recommendation system where users are suggested to view related content if they deem the displayed topics interesting. However, identifying the characteristics of the users focused on each topic can boost the importance even for topics that might not be popular or bursty. We define a way to characterize groups of users that are focused in such topics and propose an efficient and accurate algorithm to extract such communities. Through qualitative and quantitative experimentation we observe that topics with a strong community focus are interesting and more likely to catch the attention of users.