# Description These files contain the data employed in the experiments described in Bruno Ordozgoiti and Aristides Gionis. 2019. Reconciliation k-median: Clustering with Non-Polarized Representatives. In Proceedings of the 2019 World Wide Web Conference (WWW’19), May 13–17, 2019, San Francisco, CA, USA. Twitter ID's have been anonymized. # Contents domain_mentions.txt: Each line contains a domain name, a user ID and the number of times this user has mentioned this domain name in a tweet. format: domain_name <TAB> user_id <TAB> mention_count domains_ideology_score.txt: Domain names and their ideology score, estimated as described in (Lahoti et al. WSDM 2018). Note: missing scores can be retrieved from supplementary data in https://doi.org/10.1093/poq/nfw006 format: domain_name <TAB> ideology_score follow_graph.txt: The Twitter follower graph. Each line contains a user id and the user id of one of its followers. format: user_id <TAB> follower_user_id representatives.txt: US Congress representatives, each with Twitter handle and polarity score computed using Barbera's method (Barbera, 2015). format: rep_name <TAB> website_url <TAB> district <TAB> twitter_handle <TAB> party <TAB> barbera_polarity_score user_polarity.txt: User ID's and polarity score computed using Barbera's method (Barbera, 2015). format: user_id <TAB> barbera_polarity_score

{"references": ["Lahoti, Preethi, Kiran Garimella, and Aristides Gionis. \"Joint non-negative matrix factorization for learning ideological leaning on twitter.\" Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. ACM, 2018.", "Barber\u00e1, Pablo. \"Birds of the same feather tweet together: Bayesian ideal point estimation using Twitter data.\" Political Analysis 23.1 (2015): 76-91."]}