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Area of Science:

  • Computational Social Science
  • Political Science
  • Data Science

Background:

  • Social bots are automated accounts influencing public opinion on social media platforms.
  • Understanding bot behavior is crucial for analyzing political discourse and election integrity.
  • Existing datasets may lack the depth or specific features needed for comprehensive bot analysis.

Purpose of the Study:

  • To present a comprehensive, anonymized Twitter dataset from the 2019 Spanish general election.
  • To facilitate research on social bot detection, analysis, and classification.
  • To support the development and testing of machine learning models for identifying and understanding automated political influence.

Main Methods:

  • Collected 5.8 million tweets from nearly 800,000 users discussing politics, identified via 46 hashtags.
  • Anonymized user data to ensure privacy.
  • Enriched the dataset with features including topic mentions, keywords (political bag-of-words), sentiment scores, bot likelihood, political affinity, and follower/following lists.

Main Results:

  • A large-scale, feature-rich dataset specifically tailored for social bot research in a political context.
  • The dataset includes user-level features indicating bot probability and political alignment.
  • Tweets are annotated with topic information and sentiment, enabling nuanced analysis.

Conclusions:

  • The dataset provides a valuable resource for researchers studying automated influence in political campaigns.
  • Enables advancements in machine learning techniques for social bot detection and characterization.
  • Facilitates a deeper understanding of the role of social bots in shaping public opinion during elections.