ArticleFrontiers in artificial intelligence2026
Corpus-level behavioral intensity in human-AI conversations.
Article in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
4 authors.
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Abstract
Conversational generative models are increasingly used to produce, transform, and revise information through multi-turn exchanges. However, large human-AI datasets are still commonly analyzed through performance, preference, or content lenses, leaving conversation-level behavioral structure less operationalized. This study proposes a transparent rule-based framework for estimating persistence, delegation-related lexical patterns, and refinement-related lexical markers without inferring psychological dependence. LMSYS-Chat-1M and WildChat were analyzed as primary conversational corpora, while Chatbot Arena was included as a structurally conditional A/B comparator. Across 1,879,085 valid analytical records, we calculated IPF, CDR, SRS, and the Conversational Behavioral Intensity Index (CBII), together with an equal-weight control. WildChat showed the highest mean CBII (0.2463), followed by LMSYS-Chat-1M (0.2123) and Chatbot Arena (0.1762). This ordering was stable under language controls, IPF thresholds of 5, 10, and 20 user turns, and question-level analysis of Chatbot Arena. Pairwise effect sizes were small to moderate, with the largest contrast between WildChat and Chatbot Arena (Cohen's
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