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Article overview
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Identifying Influential Brokers on Social Media from Social Network Structure | | Sho Tsugawa
; Kohei Watabe
; | | Date: |
1 Aug 2022 | | Abstract: | Identifying influencers in a given social network has become an important
research problem for various applications, including accelerating the spread of
information in viral marketing and preventing the spread of fake news and
rumors. The literature contains a rich body of studies on identifying
influential source spreaders who can spread their own messages to many other
nodes. In contrast, the identification of influential brokers who can spread
other nodes’ messages to many nodes has not been fully explored. Theoretical
and empirical studies suggest that involvement of both influential source
spreaders and brokers is a key to facilitating large-scale information
diffusion cascades. Therefore, this paper explores ways to identify influential
brokers from a given social network. By using three social media datasets, we
investigate the characteristics of influential brokers by comparing them with
influential source spreaders and central nodes obtained from centrality
measures. Our results show that (i) most of the influential source spreaders
are not influential brokers (and vice versa) and (ii) the overlap between
central nodes and influential brokers is small (less than 15%) in Twitter
datasets. We also tackle the problem of identifying influential brokers from
centrality measures and node embeddings, and we examine the effectiveness of
social network features in the broker identification task. Our results show
that (iii) although a single centrality measure cannot characterize influential
brokers well, prediction models using node embedding features achieve F$_1$
scores of 0.35--0.68, suggesting the effectiveness of social network features
for identifying influential brokers. | | Source: | arXiv, 2208.00630 | | Services: | Forum | Review | PDF | Favorites | |
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