Abstract
- In recent years, the automated, efficient and sensitive monitoring of social networks has become increasingly important for the criminal investigation process and crime prevention. Previously, we have shown that the detection of opinion leaders is of great interest in forensic applications to gather important information. In the current work, it is argued that state of the art methods, determining the relative degree to which an opinion leader exerts influence over the network, have weaknesses if networks exhibit a star-like social graph topology, whereas these topologies result from the interaction of users with similar interests. This is typically the case in networks of political organizations. In these cases, the underlying topologies are highly focused on one (or only a few) central actor(s) and lead to less meaningful results by classic measures of node centrality commonly used to ascertain the degree of leadership. With the help of data collected from the Facebook and Twitter network of a German political party, these aspects are examined and a quantitative indicator for describing star-like network topologies is introduced and discussed. This measure can be of great value in assessing the applicability of established leader detection methods. Finally, two variations of a new measure– the CompetenceRank – which is based on the LeaderRAnk score and aims to address the discussed problems in cases with and without additional network data such as likes and shares, are proposed.