Replication Data for: Cross-Partisan Discussions on YouTube: Conservatives Talk to Liberals but Liberals Don't Talk to Conservatives

The dataset is first introduced in the following paper: Siqi Wu and Paul Resnick. Cross-Partisan Discussions on YouTube: Conservatives Talk to Liberals but Liberals Don't Talk to Conservatives. In AAAI International Conference on Weblogs and Social Media (ICWSM), 2021. us_partisan.csv Metadata for 1,267 US partisan media on YouTube. The first row is header. Fields include "title, url, channel_title, channel_id, leaning, type, source, channel_description" video_meta.csv Metadata for 274241 YouTube political videos from US partisan media. The first row is header. Fields include "video_id, channel_id, media_leaning, media_type, num_view, num_comment, num_cmt_from_liberal, num_cmt_from_conservative, num_cmt_from_unknown" user_comment_meta.csv.bz2 Metadata for 9,304,653 YouTube users who have commented on YouTube political videos. The first row is header. Fields include "hashed_user_id, predicted_user_leaning, num_comment, num_cmt_on_left, num_cmt_on_right" user_comment_trace.tsv.bz2 Comment trace for 9,304,653 YouTube users who have commented on YouTube political videos. The first row is header. Fields include "hashed_user_id predicted_user_leaning comment_trace" (split by \t) "comment_trace" consists of "channel_id1,num_comment_on_this_channel1;channel_id2,num_comment_on_this_channel2;..." (split by ;) trained_HAN_models.tar.bz2 Five trained HAN models for predicting user political leanings. Each model consists a ".h5" model file and ".tokenizer" tokenizer file. See this for how to use our pre-trained HAN models. See more details in this data description.