Multi-Topic Reddit Political User Interactions

The data comes from a selection of political subreddits for the 2007-2022 period. For each week in this period we have user networks. The nodes are Reddit users, connected by an undirected edge if they have exchanged a statistically significant number of messages. For each user we have a stance value which is their average opinion on that week for seven political topics of interest: abortion, climate, gender, guns, health, unauthorized immigration, and racial issues. We also have a PCA value reducing the 7-dimensional stance to their main component. Finally, we also have the network community to which the user belongs. For each edge we have the following attributes: the weight (number of messages the two users exchanged in that week), a significance score (the higher the more significant the message), the average level of toxicity of the messages the two users exchanged, and the disagreement between the users (the absolute value of the difference of their PCA scores). Additionally, we have a database with all Reddit messages in our study. It is a single tab-separated file with the following columns: user id of the author of the message the detected topic of the message (using BERTopic) the toxicity of the message (using detoxify) the stance of the message on the topic (using Llama 3) the week in which the message was sent the subreddit in which the message was sent the rolling opinion of the user (averaging the last 4 messages the user sent on that topic) the weekly opinion of the user (averaging all messages the user sent on that topic in that week, even if they are more than 4) Note that the user ids in the networks and in the message database are not consistent. This is because the networks are preprocessed to be quickly loaded in torch_geometric, which require reindexing so that nodes have ids between 0 and n-1, without gaps. To use this dataset, we refer to our Github repository: https://github.com/mikk-c/mdim_polarization/