Identifying cross-platform user relationships in 2020 U.S. election fraud and protest discussions

Abstract

Understanding how social media users interact with each other and spread information across multiple platforms is critical for developing effective methods for promoting truthful information and disrupting misinformation, as well as accurately simulating multi-platform information diffusion. This work explores five approaches for identifying relationships between users involved in cross-platform information spread. We use a combination of user attributes and URL posting behaviors to find users who appear to purposely spread the same information over multiple platforms or transfer information to new platforms. To evaluate the outlined approaches, we apply them to a dataset of over 24M social media posts from Twitter, Facebook, Reddit, and Instagram relating to the 2020 U.S. presidential election. We then characterize and validate our results using null model analysis and the component structure of the user networks returned by each approach. We subsequently examine the political bias, fact ratings, and performance of the content posted by the identified sets of users. We find that the different approaches yield largely distinct sets of users with different biases and content preferences.