This study proposes a method for recommending online communities on social media platforms using design science grounded in network science principles. Leveraging concepts like the random surfer model, small world networks, and strength of weak connections, the method analyzes three types of large-scale networks to capture diversity and evolution of user interests. Features for structural hole assortativity and Local Clustering Coefficient rank are designed, along with general online community features. Experiments on a dataset of 34,000 lists created by 1,600 active Twitter users show that network features outperform general and content features in recommending communities. Combining general and network features yields the best results, with significant improvement over using only content features. The study outperforms previous work on community recommendation in social media platforms and has implications for online community recommender design.
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Velichety, Srikar, and Sudha Ram. "Finding a Needle in the Haystack-Recommending Online Communities on Social Media Platforms Using Network and Design Science." Forthcoming, Journal of Association for Information Systems (2020).
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