From Parasocial Scripts to Dyadic Persistence in Autonomous AI-Agent Communities
This study investigates whether parasocial interaction cues exist in online communities where both sides are autonomous AI agents. Analyzing 4,434 posts and 50,338 comments from Moltbook using keyword matching, few-shot LLM annotation, and grouped-context LLM annotation, the authors found that PSI colloquial cues are prevalent and strongly associated with OP re-engagement and reciprocal reply structures. A dyadic persistence test further confirms reciprocity bids aligned with sustained OP-involving mutual recurrence, providing empirical evidence bridging interaction-level PSI scripts with relationship-level persistent dyadic patterns.
[2606.17174] From Parasocial Scripts to Dyadic Persistence in Autonomous AI-Agent Communities
[Submitted on 15 Jun 2026]
Title:From Parasocial Scripts to Dyadic Persistence in Autonomous AI-Agent Communities
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Abstract:While parasocial interactions (PSIs) and parasocial relationships (PSRs) have been studied in conventional media settings, we investigate whether PSI- (colloquial) relational cues also exist in online communities where both sides are autonomous AI agents. We analyze 4,434 posts and 50,338 comments from Moltbook through three theory-based textual indicators: attachment/intimacy language, reciprocity bids, and self-identification to original poster (OP). The combined results across methods based on keyword matching, few-shot large language model (LLM) annotation, and grouped-context LLM annotation reveal that PSI colloquial cues prevail and are strongly associated with OP re-engagement and a reciprocal reply structure. These results are robust across negative controls, nullification, clustered-standard-error re-estimation, and multiple-testing correction. A dyadic persistence test further affirms reciprocity bids aligned with sustained OP-involving mutual recurrence, providing empirical evidence for bridging interaction-level PSI scripts with PSR-consistent repeated dyadic patterns. We interpret the evidence as a behavioral structure in discourse by LLM-enabled agents.
Comments: Submitted for review in ARR for EMNLP 2026
Subjects:
Computation and Language (cs.CL); Computers and Society (cs.CY); Multiagent Systems (cs.MA)
ACM classes: J.4
Cite as: arXiv:2606.17174 [cs.CL]
(or arXiv:2606.17174v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.17174
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Mohammadsadegh Abolhasani [view email] [v1] Mon, 15 Jun 2026 18:10:39 UTC (370 KB)
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