Selecting focused digital cohorts from social media using the metric backbone of biomedical knowledge graphs
<|reference_start|>Selecting focused digital cohorts from social media using the metric backbone of biomedical knowledge graphs: 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.<|reference_end|>
@article{guo2024selecting,
    title={Selecting focused digital cohorts from social media using the metric
  backbone of biomedical knowledge graphs},
    author={Ziqi Guo, Jack Felag, Jordan C. Rozum, Rion Brattig Correia, Luis M.
  Rocha},
    journal={arXiv preprint arXiv:2405.07072},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.07072},
    primaryClass={cs.SI}
}