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Title: Extracting Topics with Focused Communities for Social Content Recommendation
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.  more » « less
Award ID(s):
1649469
NSF-PAR ID:
10033870
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing
Page Range / eLocation ID:
1432 to 1443
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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