Fig 1.
User bias distributions on Twitter and Parler.
(A) Each bin represents a 0.25-point interval of average user bias, and the height is the probability density of users in that bin. Twitter contains similar proportions of left- and right-leaning users, while Parler is predominantly right-leaning. (B) Contour plots of user average bias with respect to the average bias of their one-hop neighborhood. In both plots, –2 refers to the most left-leaning, and 2 to the most right-leaning users. Additionally, darker shades represent a higher density of interactions. Two homophilic clusters characterize Twitter users, while most users’ neighborhoods are right-leaning on Parler.
Fig 2.
Characteristics of ideologically homogeneous groups.
(A) The probability distribution of cluster sizes on both social networks exhibit exponential decay, indicative of the prevalence of smaller clusters in our results. (B) Percentage of clusters that are ideologically homogeneous (IHC) with respect to cluster size. We define IHCs as the top 20% most homogeneous clusters (dotted line) and observe that Twitter IHCs are concentrated in smaller clusters, while larger groups are more likely to be homogeneous on Parler (inset).
Fig 3.
Retweet characteristics of IHCs.
(A) Retweet spread ratio as a function of cluster size on Parler and Twitter (inset). Retweet spread ratio is defined as the ratio of retweets per user in IHCs and retweets per user in all other clusters. For all cluster sizes, the IHCs have significantly higher retweet spread than similarly sized but less homogeneous clusters. On Twitter, the IHCs are much more effective at gathering reposts when compared to Parler. (B) Inter-cluster spread ratio as a function of cluster size for Parler groups and Twitter groups (inset). Inter-cluster spread ratio was calculated as the ratio of the number of edges from a cluster to another unique cluster and the number of users in that cluster. The results show that users on Parler tend to refrain from interacting outside of their primary clusters, regardless of the ideological homogeneity value. On Twitter, IHC groups are more effective at gathering out of cluster retweets, a differential that grows for larger clusters.
Table 1.
Genres categorized by theme.
Fig 4.
Fraction of all IHCs that are at least one standard deviation below the average value with respect to writing genre.
(A) On Twitter, left-leaning groups post content that have high similarity to the Alternative History genre and the Activism Non-Speculative genre, while the right leaning groups posts are most similar to both Speculative Fiction and Hybrid genres. Conversely, on Parler (B), the right-leaning groups have significant similarity to the Hybrid genres as well as the Alternative History and Activism genres indicative of the election context. The dotted line indicates the expected, random distribution of genres.