Graphs on Channel, User and Chat data on Brazilian Telegram Channels of Information Disorder

This dataset contains three distinct graph models - Channel, User, and Chat networks - derived from the "Brazilian Social Media Anti-vaccine Information Disorder Dataset - Telegram (2020-2025)" (doi: 10.25824/redu/5JIVDT). The primary research goals, to which the present dataset is the basis of study, are to identify viral and popular misinformation topics, their spread through time, the relationship between channels and the existence of user bubbles. The original messages were grouped and structured through channels, users, and temporal message clusters (chats), forming three distinct graphs. The Channel Graph models directed interactions between the monitored channels via message forwarding; the User Graph identifies relationships based on forwards and replies between users; and the Chat Graph represents temporal "sessions" of messages to track how specific topics spread across the network, with forwards and replies between chats showing how a certain idea propagates.

As the graphml graphs were exported from Neo4J, they all have by default orientation, but that is how Neo4J works. In order to analyze the User Graph as undirected, it is sufficient to run any Cypher query or processing disregarding the orientation.
To import them on Neo4J, paste them in the "import" folder and, with the APOC import permission, run 'CALL apoc.import.graphml("?_graph.graphml", {readLabels: true})', replacing ? with users, channels or chats.