对比2016与2024年大选，发现生成式AI让认知作战从复制转发转向自主造谣。
Amplification to Synthesis: A Comparative Analysis of Cognitive Operations Before and After Generative AI
- 用语言模式和时间同步分析社交媒体数据，识别出内容生成方式的根本转变。
- 原创内容占比从59%升至93%，词句重叠率从0.99暴跌至0.27，表达趋异。
- 适合关注网络认知战、信息安全及生成式AI风险的从业者阅读。
认知操作正成为地缘政治领域的重要议题，一场无声却严谨的公众认知与决策争夺战。尽管机器人驱动的内容放大已受广泛研究，但生成式AI的出现可能彻底改变了此类操作的设计与执行方式。为应对这一新威胁，本研究对比了2016年与2024年美国大选期间在X（原推特）平台上的行为与语言协调模式。基于超过13.3万条帖子的合并语料库，采用帖子类型分布、语义聚类、时间同步分析及基于Jaccard系数的词汇重叠度测量。结果显示：2024年语料中原创内容占比由59%升至93%，转发几乎消失；词汇重叠率从均值0.99骤降至0.27，相同主题以显著不同的语言表达；时间协调性也从跨语义广泛同步转为叙事集中共现。这些特征表明，操作逻辑已转向主动内容生成与叙事精准投放，与生成式AI应用高度一致。研究为未来探究生成式AI在认知作战链中的角色提供了实证基准，也为安全从业者构建适配后生成式AI时代的检测框架提供实践参考。
原文摘要 · Abstract (English)
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.
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