DOI: 10.5061/dryad.7sqv9s50w
title: Incentivizing news consumption on social media platforms using large language models and realistic bot accounts

description: This project examines how to enhance users' exposure to and
 engagement with verified and ideologically balanced news in an
 ecologically valid setting. We rely on a large-scale two-week long field
 experiment on 28,457 Twitter users. We created 28 bots utilizing GPT-2
 that replied to users tweeting about sports, entertainment, or lifestyle
 with a contextual reply containing two hardcoded elements: a URL to the
 topic-relevant section of quality news organization and an encouragement
 to follow its Twitter account. Treated users were randomly assigned to
 receive responses by bots presented as female or male. We examine whether
 our intervention enhances the following of news media organization, the
 sharing/liking of news content and the tweeting/liking of political
 content. We find that the treated users followed more news accounts and
 the users in the female bot treatment were more likely to like news
 content than the control.
description: Collected via Twitter API and the Python Tweepy library. Contains
 raw files from our pre and post metrics and also contains our final
 metrics after all of the classifications (politics and news). 
description: # Incentivizing News Consumption on Social Media Platforms Using Large
 Language Models and Realistic Bot Accounts
 [https://doi.org/10.5061/dryad.7sqv9s50w](https://doi.org/10.5061/dryad.7sqv9s50w) ## Description of the data and file structure Dataset contains the following CSV's: **UserInfo.csv** - user account-level information **UserAnalysis_Data.csv** - user behavioral data, with corresponding treatment labels **UserFollowingChange.csv** - change in followed accounts, per user, pre and post experiment, for filtering **Final Coef Estimates.csv** - Exported main model coefficients, for plotting figure 4 **Final Coef Estimates PolInterest.csv** - Exported political interest model coefficients, for plotting figure 5 **Final Coef Estimates Class.csv** - Exported users topic classes model coefficients, for plotting appendix models **Final_topic_classification.csv** - user topic classifications, for merging in topic class models. The individual files contain the following columns: **UserInfo.csv:** user_id - Twitter ID of user     created_at  - date of account creation listed_count  - number of lists account features on     favourites_count  - number of likes from account statuses_count  - number of posts from account   friends_count - number of accounts followed by user    followers_count - numbers of accounts following user **UserFollowingChange.csv:** original_user_id  - Twitter ID of user following  - number of accounts followed pre-treatment  followingpost - number of accounts followed post-treatment    postdiff - difference between pre and post accounts followed    postdiffpct - difference in pct between pre and post accounts followed **UserAnalysis_Data.csv** UserIDs  - Twitter ID of user    treatment - treatment group   treated - where user was treated (not control     followees_diff - post-treatment difference in number of media accounts followed    tweets_media_pct_diff  - post-treatment difference in pct. retweets of media accounts likes_media_pct_diff  - post-treatment difference in pct. likes of media accounts   tweets_pol_pct_diff  - post-treatment difference in pct. of political tweets     likes_pol_pct_diff  - post-treatment difference in pct. of political likes followees_pre  - number of pre-treatment media accounts followed    tweets_media_pct_pre - pct. of pre-treatment retweets of media accounts    likes_media_pct_pre  - pct. of pre-treatment likes of media accounts    tweets_pol_pct_pre  - pct. of pre-treatment tweets political     likes_pol_pct_pre - pct. of pre-treatment likes political followees_difftrim - post-treatment difference in number of media accounts followed (suggested accounts only) **Final_topic_classification.csv:** UserIDs - Twitter ID of user  Class - Topical classification of user (Class of tweets most often sent by user) **All the coef csv contain these variables:** Treatment - Treatment Group Variable - Variable modelled as dependent variable coef - coefficient estimate se - standard error upper - upper bound (95% CI) lower - lower bound (95% CI) Model - ITT or Treated Models UserType -  One of Sports, Entertainment or Lifestyle in the 'Final Coef Estimates Class.csv' file and between 'High Interest' (Political) or 'Low Interest' (Political) in the 'Final Coef Estimates PolInterest.csv' file.  Function- This refers to the "Score Test" being performed in the Regression Models. Some cells in the files contain "N/A", which indicates that those cells were not applicable for that user or that information was missing for that user.  ## Sharing/Access information Data was derived from the following sources: * Twitter API and Tweepy ## Code/Software * Tweepy docs: [https://docs.tweepy.org/en/stable/](https://docs.tweepy.org/en/stable/) * Our bot's github: [https://github.com/ercexpo/EXPO-TwitterBot](https://github.com/ercexpo/EXPO-TwitterBot) * Other important resources used for our experiment: [https://github.com/HadiAskari/TwitterBot-Resources](https://github.com/HadiAskari/TwitterBot-Resources)