U.S. Election Hardens Hate Universe

TL;DR

The paper addresses how local political events trigger rapid, global online hate dynamics by constructing a cross-platform 'hate universe'—a network-of-networks of hate communities across platforms around the 2020 U.S. election. It combines cross-platform data collection with NLP-based classification of hate flavors to analyze changes in network topology and narratives around election day and January 6, revealing a cohesive, hardening structure and shifts toward immigration, ethnicity, and antisemitism content, with Telegram playing a central coordinating role. Key findings show significant network hardening (e.g., clustering coefficient up by ~164.8%, assortativity up by ~27%), a reduction in the number of communities with growth of the largest community, and corresponding surges in specific hate flavors. The study implies that anti-hate policies must adopt multi-platform, multi-flavor strategies and leverage global-scale hate-universe mappings to effectively anticipate and counter evolving online hate dynamics surrounding elections and other major events.

Abstract

Local or national politics can trigger potentially dangerous hate in someone. But with a third of the world's population eligible to vote in elections in 2024 alone, we lack understanding of how individual-level hate multiplies up to hate behavior at the collective global scale. Here we show, based on the most recent U.S. election, that offline events are associated with a rapid adaptation of the global online hate universe that hardens (strengthens) both its network-of-networks structure and the 'flavors' of hate content that it collectively produces. Approximately 50 million potential voters in hate communities are drawn closer to each other and to the broad mainstream of approximately 2 billion others. It triggers new hate content at scale around immigration, ethnicity, and antisemitism that aligns with conspiracy theories about Jewish-led replacement before blending in hate around gender identity/sexual orientation, and religion. Telegram acts as a key hardening agent - yet is overlooked by U.S. Congressional hearings and new E.U. legislation. Because the hate universe has remained robust since 2020, anti-hate messaging surrounding not only upcoming elections but also other events like the war in Gaza, should pivot to blending multiple hate 'flavors' while targeting previously untouched social media structures.

Figures (4)

• Figure 1: Hate universe. A: Schematic of how a hate community (node) on a given social media platform (given color) establishes a link (by sharing a URL for a piece of content) to another community (node) at a given time. A link from node A to node B means that members of community A are alerted to B's existence, and can visit community B to share hate. Each community is a platform-provided community: a Telegram Channel, a Gab Group, a YouTube Channel, etc. Top dotted box is the subset of communities that are identified as hate communities. These link together over time to form hate networks-of-networks. Bottom box represents communities that are directly linked to by hate communities but which are not themselves in the list of hate communities; hence, we label these as vulnerable mainstream. B: Empirically determined composition of the hate universe. Boxes show number of hate communities (nodes) in a given platform and the average number of members of each community. Edges shown are aggregated over time. Schematic dynamic structures in the online hate network, as well as an actual visualization of the connections and quantities in this network
• Figure 2: Changes in key network metrics surrounding events in the 2020 U.S. election period, specifically the actual date of the election (A) and the day of Congress' confirmation which is when the attack took place on the U.S. Capitol (B) Plots and tables showing network metric changes before and after Nov. 3 (left) and Jan. 6 (right)
• Figure 3: Hardening of hate universe's network-of-networks. A: Gephi visualization of the hate universe before and after January 6, 2021. The partial rings comprise dense clouds of vulnerable mainstream communities (nodes) with successively increasing numbers of links from the hate networks, hence creating orbital-like rings. B: Subset of Telegram-connected networks before and after election day, show its key role as a binding agent. Social media networks colored by platform
• Figure 4: Hardening of hate universe's content around particular types ('flavors') of hate narratives. Results shown for the periods around the declaration of Biden as president-elect (A) and around January 6 (B). Bar graphs showing percent changes in hate type following significant events