Amplification to Synthesis: A Comparative Analysis of Cognitive Operations Before and After Generative AI
2026-05-13 • Computers and Society
Computers and SocietyArtificial Intelligence
AI summaryⓘ
The authors studied how online influence campaigns around U.S. elections have changed from 2016 to 2024, especially with new AI tools. They found that in 2024, there was much more original content rather than simple retweets, and posts about the same topics used very different wording. Timing patterns also shifted from broad coordination to focused, story-based bursts. These changes suggest that generative AI is helping create more diverse and targeted messages in these operations. This work sets a baseline for future studies and security efforts against AI-driven influence tactics.
Cognitive operationsGenerative AIBehavioral coordinationLinguistic coordinationSemantic clusteringTemporal synchronyJaccard similaritySocial media influenceElection interferenceContent generation
Authors
Liz Cho, Dongwook Yoon
Abstract
Cognitive operations are a rising concern in the geopolitical sphere, a quiet yet rigorous fight for public perception and decision making. While such operations have been extensively studied in the context of bot-driven amplification, the emergence of generative AI introduces a new set of capabilities that may have fundamentally altered how these operations are designed and executed. The possible evolution of cognitive operation via generative AI puts nation states vulnerable without proper mitigation strategies. To address this, we compared behavioral and linguistic coordination patterns in X (formerly Twitter) datasets from the 2016 and 2024 U.S. presidential elections. Utilizing a combined corpus of over 133,000 posts, we applied post-type distribution, semantic clustering, temporal synchrony analysis, and Jaccard-based lexical overlap measures. Findings suggest that the 2024 corpus exhibits a distinct pattern from 2016. Original content rose from 59% to 93% with retweets virtually disappeared; lexical overlap collapsed from a mean Jaccard score of 0.99 to 0.27, with posts converging on the same subject matter expressed in markedly different words; and temporal coordination shifted from pervasive cross-semantic synchrony to narratively concentrated co-occurrence. Taken together, these patterns point toward an operational logic organized around active content generation and narrative-specific targeting - characteristics consistent with generative AI involvement. These findings offer an empirical baseline for future research investigating generative AI's role in the cognitive operation pipeline, and as a practical reference point for security practitioners developing detection frameworks calibrated to the post-generative AI threat environment.