Observational Data for: Polarized information ecosystems can reorganize social networks via information cascades

General Information This contains the observational data for the publication: Tokita, Guess, and Tarnita (2021). Polarized information ecosystems can reorganize social networks via information cascades. Proceedings of the National Academies of Science of the United States. Please see the above peer-reviewed article that resulted from this data for more details. Please see the observational/ directory in the Github repository https://github.com/christokita/information-cascades for the code that analyzes the Twitter data and generates derived data. Data directly pertaining to the four news sources of interest are in the data/ directory, while data that is related to the 4,000 monitored news followers are in the data_derived/ directory. The main data file is in a zipped file. We also included a read me with a description of each directory and subdirectory of the data. Methods For each of our four news sources of interest (CBS News, USA Today, Vox, and the Washington Examiner), we used the Twitter API to sample 3,000 random Twitter users that followed that news source's account. We then used the tweetscores R package to estimate the ideology of each of the 12,000 sampled users. From the original pool of approximately 12,000 sampled users who follow a news outlet of interest, we monitored the follower networks of 1,000 liberal followers of CBS News, 1,000 conservative followers of USA Today, 1,000 liberal followers of Vox, and 1,000 conservative followers of the Washington Examiner. When selecting the 1,000 random users to monitor for a given news outlet, we first conducted filters based on self-reported geolocation and other Twitter profile attributes to reasonably filter down to US-based users who primarily tweet in English. We then pulled the complete follower network of each of our 4,000 monitored users at the beginning and end of a 6-week period from August to September 2020, allowing us to assess who unfollowed these users over this period of time. Finally, using the initial follower networks of each monitored user, we estimated the ideology of up to 50 random followers to create a baseline for the ideological composition of each user’s follower network. Using the initial and final follower networks of each individual, we calculated the rate of unfollows by opposite-ideology users in their follower network. To determine whether an unfollow event was breaking a cross-ideology social tie, users and their unfollowers were classified simply as either liberal (ideology score 0). We excluded from analysis unfollowers for whom we could not estimate ideology, either because the account had been suspended, deleted, or made private, or because they did not follow any of the political, news, or cultural accounts needed to do the estimation. We then compared the proportion of unfollowers that were of the opposite ideology (i.e., conservative unfollowers if the focal user is liberal) against the estimated proportion of opposite-ideology followers in the focal user’s initial follower network. This allowed us to account for the fact that many user’s follower networks were not ideologically balanced and set the baseline expectation that if unfollows were random then the proportion of unfollows by opposite-ideology users should match the proportion of followers that were of the opposite ideology. Abstract (for main paper) The precise mechanisms by which the information ecosystem polarizes society remain elusive. Focusing on political sorting in networks, we develop a computational model that examines how social network structure changes when individuals participate in information cascades, evaluate their behavior, and potentially rewire their connections to others as a result. Individuals follow proattitudinal information sources but are more likely to first hear and react to news shared by their social ties and only later evaluate these reactions by direct reference to the coverage of their preferred source. Reactions to news spread through the network via a complex contagion. Following a cascade, individuals who determine that their participation was driven by a subjectively “unimportant” story adjust their social ties to avoid being misled in the future. In our model, this dynamic leads social networks to politically sort when news outlets differentially report on the same topic, even when individuals do not know others’ political identities. Observational follow network data collected on Twitter support this prediction:We find that individuals in more polarized information ecosystems lose cross-ideology social ties at a rate that is higher than predicted by chance. Importantly, our model reveals that these emergent polarized networks are less efficient at diffusing information: Individuals avoid what they believe to be “unimportant” news at the expense of missing out on subjectively “important” news far more frequently. This suggests that “echo chambers”—to the extent that they exist—may not echo so much as silence.This work was supported by the NSF Graduate Research Fellowship under Grant DGE1656466 (to C.K.T.) and a research grant from the Princeton Data-Driven Social Science Initiative