Real-World Popularity Estimation from Community Structure of Followers on SNS
by Shuhei Kobayashi, Keishi Tajima
Abstract
In this paper, we propose methods of estimating the offline real-world
popularity of users of online social network services (SNSs). Because
their followers on an SNS are biased sampling from their offline
real-world fans, we cannot estimate their real-world popularity simply
by the number of their online followers. Our methods are based on the
following hypothesis: SNS users with followers more distributed over
many communities are likely to have more real-world popularity.
We developed four methods, three of which use variations of the
clustering coefficients of the followers to measure how much they are
distributed, and one of which uses a metric we newly designed.
Through the evaluation of our methods on the data from nine Ms/Mr
university competitions, we validated our hypothesis.
offline popularity;
social network;
Twitter;
Instagram;
clustering coefficient
Published in Proc. of IEEE/WIC/ACM WI-IAT, pp.328-334, Niagara Falls, Canada, 2022
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