Focused digital cohort selection from social media using the metric backbone of biomedical knowledge graphs

TL;DR

This paper presents a novel method using the metric backbone of biomedical knowledge graphs to identify and filter social media users relevant to specific health issues, demonstrated on epilepsy discourse across various platforms.

Contribution

The study introduces a general approach for social media cohort selection based on the metric backbone of biomedical knowledge graphs, validated on epilepsy-related data.

Findings

Users contributing to the KG backbone are more relevant to the health topic.

Backbone-based filtering improves the precision of cohort selection.

Non-backbone users often misuse biomedical terms, indicating lower relevance.

Abstract

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. To filter relevant users on any social media, we have developed a general method and tested it on epilepsy discourse. We analyzed the text from posts by users who mention epilepsy drugs at least once 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 used a curated medical terminology dictionary to generate a knowledge graph (KG) from each social media site, whereby nodes represent terms, and edge weights denote the strength of association between pairs of terms in the collected text. Our method is based on computing the metric backbone of each KG, which yields the subgraph of edges that participate in shortest paths. By comparing the subset of users who contribute to the backbone to the subset who do not, we show 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 demonstrate that users who do not contribute to the backbone are much more likely to use dictionary terms in a manner inconsistent with their biomedical meaning and are rightly excluded from the cohort of interest.