Using targeted betweenness centrality to identify bridges to neglected users in the Twitter conversation on veteran suicide

There are many real-world contexts in which it would be invaluable to identify intermediaries who serve as bridges for specified dyads. For instance, with respect to suicide prevention among military veterans, mental health organizations play an important role in providing information, social support, and other resources to veterans, particularly on social media, where anonymous and pseudonymous interactions may help to counteract the stigma associated with suicide and mental health. However, many at-risk individuals are overlooked in the large-scale conversations on social media and are thus less likely to benefit from those interactions. Intermediaries who can integrate those peripheral actors into the conversation, or at least deliver essential information to them, could help to resolve this issue. The critical question, then, is how to identify those potentially useful intermediaries who are well positioned to act as bridges to reach peripheral actors. To address this challenge, we propose a new network measure, targeted betweenness centrality, to identify vertices that represent potentially useful intermediaries between specified dyads, then use that measure to identify social media users who can act as opinion leaders on behalf of the US Department of Veterans Affairs to reach vertices on the periphery of the Twitter conversation about veteran suicide. The results of this study provide useful insights related to opinion leadership, the military veteran community, and suicide prevention, and more broadly, they demonstrate the practical utility of targeted betweenness centrality for real-world research across a variety of contexts.