A Centrality for Social Media Users Focusing on Information-Gathering Ability
by Mamoru Yamakawa, Keishi Tajima
Abstract
In this paper, we propose a centrality metric for social media users
that focuses on their information-gathering ability. Existing methods
of rating users in social graphs focus on various aspects of users,
such as popularity, influential power, and informational quality, but
these aspects are related to information-transmitting ability of
users. On social media, information-gathering ability is also an
important ability, which varies widely from user to user. There have
been two well-known metrics related to it: the hub score in the HITS
algorithm and Katz centrality. These two methods are, however, not
designed for today's social media, and do not take important aspects
of social media into consideration. HITS does not consider multi-hop
information propagation, and Katz centrality assumes that all nodes in
the graph are equally important as information sources and also as
information propagation mediators. In the proposed method, we extend
Katz centrality by introducing two properties of users: importance as
information source and information forwarding probability. The result
of our experiment on two Twitter follow graphs shows that our metric
produces a ranking different from the existing metrics, and also
suggests that it captures some useful aspect of users that are not
captured by existing metrics.
social network;
social media;
Twitter;
Katz centrality;
hub score;
retweet;
information propagation;
graph node ranking;
Published in Proc. of ACM Hypertext, pp.32:1-32:9, Rome, Italy, 2023
/ Fax: +81(Japan) 75-753-5978 / Office: Research Bldg. #7, room 404