How toxicity travels through online conversations depends on context

Toxic online conversations are often moderated one comment at a time. A study in the IEEE Journal of Social Computing finds that harmful language develops through reply chains and that the most influential comment may depend on the broader conversational context.

A team led by the Maulden-Entergy Chair and Donaghey Distinguished Professor Nitin Agarwal of the COSMOS Research Center at the University of Arkansas at Little Rock and the International Computer Science Institute at the University of California, Berkeley, developed a generational analysis framework that represents online conversations as trees. It examines a comment’s parent, grandparent, and great-grandparent to characterize whether the conversation ends in toxicity.

The researchers analyzed 1.2 million Reddit posts from r/NoNewNormal, a community active during the COVID-19 pandemic. The posts formed 156,847 conversation trees, including 23,000 with at least four hierarchical levels. Toxicity was measured using Detoxify, a RoBERTa-based model, and nine machine-learning algorithms were evaluated.

The study identified a context-dependent “generational toxicity gradient.” In highly toxic, moderately toxic, and high-engagement conversations, the immediate parent carried the strongest analytical signal, with characterization accuracy ranging from 69% to 74%. In less-toxic conversations, the great-grandparent was most informative, reaching 75% accuracy.

“The most important message is that toxicity does not simply weaken as conversational distance increases,” said Agarwal, the study’s lead and corresponding author and founding director of COSMOS. “In a hostile thread, the latest toxic remark can provoke a rapid response. In a less-toxic discussion, an earlier comment may continue setting the tone several replies later.”

“Our methodology reconstructs Reddit discussions as hierarchical conversation trees and measures toxicity across parent, grandparent, and great-grandparent comments using Detoxify, a RoBERTa-based model,” said Tope Falade, a doctoral candidate at COSMOS. “We then evaluate nine machine-learning algorithms to identify which generational features best characterize how each conversation ends.”

Toxic conversations also had distinctive structures. Compared with non-toxic conversations, they branched more widely, extended deeper, and involved more participants, yet lasted less time, i.e., 18.3 hours on average versus 34.6 hours. Highly toxic trees ended with a toxic comment 51.5% of the time, compared with 18.2% for high-engagement trees.

Toxicity can harm well-being, weaken communities, and drive constructive participants away. The findings suggest that moderation timing and conversational context may be as important as detecting offensive words. Structural signals such as rapid branching or increasing depth could help moderators recognize deteriorating discussions before explicit toxicity becomes widespread.

“The findings point toward moderation strategies that respond to the condition of the conversation,” Agarwal said. “Already-hostile threads may require rapid review of the newest replies, while less-toxic discussions may benefit from protecting the quality of the opening exchange.”

The authors emphasize that the models characterize patterns rather than constitute a deployment-ready moderation system. Because the data came from one English-language Reddit community shaped by pandemic-era and anti-establishment discourse, using the framework elsewhere will require ethical review, bias auditing, and validation across platforms, languages, cultures, and community norms.

The researchers next plan to test whether the generational toxicity gradient appears on X, Facebook, and YouTube. They also propose controlled moderation studies, integration of structural signals with large language models, and evaluation of counterspeech. Their ultimate goal is to support transparent early-warning and human-review tools that reduce harmful escalation without suppressing legitimate disagreement.

This work was supported in part by the U.S. National Science Foundation (OIA-1946391, OIA-1920920); U.S. Army Research Office (W911NF-23-1-0011, W911NF-24-1-0078, W911NF-25-1-0147, W911NF-26-1-A163); U.S. Office of Naval Research (N00014-21-1-2121, N00014-21-1-2765, N00014-22-1-2318); U.S. Air Force Office of Scientific Research (FA9550-22-1-0332); U.S. Air Force Research Laboratory; U.S. Defense Advanced Research Projects Agency; Australian Department of Defense Strategic Policy Grants Program; Arkansas Research Alliance; Jerry L. Maulden/Entergy Endowment; and Donaghey Foundation. The views expressed are those of the authors and not necessarily those of the funding organizations.

DOI Link:

https://doi.org/10.23919/JSC.2026.0017

 

Article Information

Nitin Agarwal and Tope C. Falade, “Context-Dependent Toxicity Propagation in Online Discourse: A Generational Analysis Framework for Characterizing Conversation Dynamics in Reddit,” Journal of Social Computing, vol. 7, no. 3, pp. 269-296, September 2026. DOI: 10.23919/JSC.2026.0017.

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