Identifying and characterizing ideologically homogeneous clusters on Twitter and Parler during the 2020 election - Daniel Verdear; Ashley Hemm; Zuoyu Tian; Sara El Oud; Sandra Kübler; John Funchion; Michelle Seelig; Amanda Diekman; Manohar Murthi; Kamal Premaratne; Neil F. Johnson; Stefan Wuchty (PLoS ONE, 2025)
During the 2020 U.S. presidential election cycle, a combination of public statements and social media posts cast doubt on the legitimacy of the election. These sentiments flowed through various social networks and eventually sparked the January 6th insurrection at the Capitol. Here, we analyze both the network-level and content-level data that made the #StopTheSteal movement so effective online. We use Louvain clustering and a novel homogeneity metric to identify the most ideologically homogeneous groups within the discussion on the mainstream social network Twitter and alternative social network Parler. We show that these ideologically homogeneous groups spread messages further than their ideologically diverse counterparts. Our results also differentiate between ideologically homogeneous left- and right-leaning groups by measuring the characteristics of their texts, finding that right-leaning texts are stylistically similar to worldbuilding language that can be found in conspiracy theory texts.