Hang Zhang
Vinay Setty
ABSTRACT
Use of social media platforms to express opinion and discuss various topics has been increasingly popular. Consequently, huge volume of social media data is generated by users across all these platforms, e.g. users comment on a variety of content items such as news articles, videos, images on social media. These comments are often noisy and sparse, therefore, identifying sub-topics within them to explore social media is a challenge. In this paper, we develop an effective way to distill sub-topics from all the comments related to a textual query and apply two different diversification techniques to select comments. We conduct experiments to validate our idea using seven years of Reddit comments and news events from Wikipedia Current Events Portal as queries.
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Index Terms
- Finding diverse needles in a haystack of comments: social media exploration for news
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