  The abundance of social media data allows researchers to construct large
digital cohorts to study the interplay between human behavior and medical
treatment. Identifying the users most relevant to a specific health problem is,
however, a challenge in that social media sites vary in the generality of their
discourse. While X (formerly Twitter), Instagram, and Facebook cater to wide
ranging topics, Reddit subgroups and dedicated patient advocacy forums trade in
much more specific, biomedically-relevant discourse. To hone in on relevant
users anywhere, we have developed a general framework and applied it to
epilepsy discourse in social media as a test case. We analyzed the text from
posts by users who mention epilepsy drugs in the general-purpose social media
sites X and Instagram, the epilepsy-focused Reddit subgroup (r/Epilepsy), and
the Epilepsy Foundation of America (EFA) forums. We curated a medical terms
dictionary and used it to generate a knowledge graph (KG) for each online
community. For each KG, we computed the metric backbone--the smallest subgraph
that preserves all shortest paths in the network. By comparing the subset of
users who contribute to the backbone to the subset who do not, we found that
epilepsy-focused social media users contribute to the KG backbone in much
higher proportion than do general-purpose social media users. Furthermore,
using human annotation of Instagram posts, we demonstrated that users who do
not contribute to the backbone are more than twice as likely to use dictionary
terms in a manner inconsistent with their biomedical meaning. For biomedical
research applications, our backbone-based approach thus has several benefits
over simple engagement-based approaches: It can retain low-engagement users who
nonetheless contribute meaningful biomedical insights. It can filter out very
vocal users who contribute no relevant content.
