Me and My Bot: What Users Talk About in AI Companion Communities on Reddit

Source: arXiv:2608.00748 · Published 2026-08-01 · By Richard A. Fabes

TL;DR

This study investigates the content and emotional tone of user discourse about AI companion bots within Reddit communities dedicated to AI companionship. Drawing on the Synthetic Resonance framework—which posits that users can form psychologically meaningful but asymmetrical relationships with AI bots—the author analyzes 5,504 posts across eight subreddits to assess how often users discuss their own relationships versus general bot topics, the predominant themes of those discussions, and the emotional valence expressed. By employing multiple large language models (LLMs) from different organizations to code each post independently, the study achieves reliable thematic and emotional annotation at scale.

Key findings include that despite search terms biased toward relational language, only about 45% of posts focused explicitly on users' own AI relationships, with the rest addressing bots in general or technical/platform issues. Posts about users’ own bots were far more likely to contain emotional language and to focus on relational themes such as companionship and romance. Emotional valence in own-bot posts skewed positive or mixed rather than negative, supporting the idea that users experience these relationships as meaningful. The discourse is complex, involving ambivalence alongside affection. The results extend and nuance prior work, showing that AI companion communities simultaneously serve as spaces for social-emotional expression and technical discussion, rather than purely relational forums. These insights have implications for understanding users’ experiences with AI companionship and how platforms might better support them.

Key findings

• Only 45% of retrieved Reddit posts (2,500/5,504) explicitly focused on the user's own relationship with their AI bot despite search terms biased toward relational language.
• Posts about own AI companions contained emotional language in 67% of cases (positive, negative, or mixed), compared to only 15% in posts about bots in general.
• Among the 970 posts explicitly about users' own bots and focused on relationships, companionship and romance each accounted for roughly 46% of content, with sexual/erotic roleplay making up 8%.
• Emotional valence varied by subtopic: companionship posts were most often positive or mixed and had more negative emotion than romance posts, which were least negative overall.
• Mixed emotional valence (positive and negative emotion together) was low in general-bot posts (<1%) but prominent in own-bot posts about relationships (over 25%).
• Posts focusing on bots in general were primarily technical (43%) or miscellaneous (15%), while posts about own bots were mostly relational (39%) or continuity (21%) topics.
• LLM coders from Anthropic, OpenAI, and DeepSeek independently coded posts, with overall human-LLM agreement around 87% and moderate inter-LLM agreement (kappa ~0.5-0.6).
• The distribution of emotional valence and topics significantly differed between own-bot posts and general bot posts (Cramér's V = 0.45 and 0.55 respectively, p < .001).

Threat model

Not applicable; the study investigates naturalistic user discourse about AI companions on Reddit and does not involve security adversaries or attacker capabilities.

Methodology — deep read

The study aimed to understand how users publicly narrate their relationships with AI companion bots on Reddit, guided by the Synthetic Resonance framework, which explains users’ feelings of connection as arising from repeated structural alignment with AI behavior rather than genuine reciprocity.

1. Threat model & assumptions: The approach assumes a human-centered study perspective rather than attacker/defender in security. The focus is on naturalistic, unsolicited discourse from Reddit users in AI companion communities. No adversarial manipulation or deception framing was involved.

2. Data: Data were collected July 2026 via the PullPush API, sampling posts from 11 AI companion subreddits using 21 relationally weighted keywords (e.g., “my AI,” “soulmate,” “attached”). Up to 100 posts per subreddit-keyword combo were retrieved, resulting in 7,240 raw posts. After excluding official company subreddits and merging small ones with similar topics, 5,504 posts across eight communities remained. Posts were truncated at 3,000–4,768 characters.

3. Coding architecture: Three different LLMs (Anthropic's Claude Haiku 4.5, OpenAI’s GPT-4o, and DeepSeek V4 Flash), representing diverse organizations, independently coded each post with identical prompts and codebook instructions. The variables coded were: (a) relationship focus (whether the post discussed the user’s own bot relationship), (b) user's emotional valence (No emotion, Positive, Negative, Mixed), and (c) primary topic (one of six categories: Relational, Continuity, Technical, Storytelling, Consciousness, Miscellaneous).

4. Training regime: Pilot batches refined the codebook and pipeline; a validation batch of 175 posts established inter-LLM agreement with ~60% unanimous and ~35% majority agreement. A subsample of 125 posts was human-coded for accuracy checks, yielding 87% human-LLM agreement. Final coding on the entire corpus used majority decision among the three LLM outputs, with OpenAI GPT-4o used as tie-breaker when no agreement existed.

5. Evaluation protocol: Statistical analyses included chi-square tests for distributions of post types, topics, and emotional valence. Effect sizes were calculated with Cramér's V; significance was reported at p < .001. Analyses were primarily descriptive, focusing on distributions across categories. No cross-validation or adversarial testing was conducted.

6. Reproducibility: The paper does not indicate public release of code or data. Use of three distinct LLMs improves reliability but the corpus is proprietary and derived from Reddit’s API. Exact prompt and codebook details are described but not fully replicated here.

One concrete example: A given Reddit post about the user's own AI bot (e.g., describing a supportive conversation) was input individually into all three LLMs with coding instructions. Each LLM parsed the text to assign relationship presence (yes), emotional valence (positive), and topic (relational/companionship). Results agreed at 2/3 or 3/3; if discordant, OpenAI’s label was chosen. This procedure was repeated for all 5,504 posts to produce the dataset for analysis.

Technical innovations

• Use of multiple heterogeneous LLMs (Anthropic Claude, OpenAI GPT-4o, DeepSeek) to independently code large-scale qualitative data to enhance reliability and mitigate systematic bias.
• Development of a detailed qualitative codebook focused on relationship focus, emotional valence, and primary topic tailored to AI companion discourse under the Synthetic Resonance framework.
• Integration of explicit candidate grounding validation, where emotion valence codings required the annotating model to cite specific text snippets as evidence, verified against the post's content.
• Systematic tie-break strategy favoring the coder (GPT-4o) with highest agreement with majority, rather than arbitrary or random resolution of annotation disagreements.

Datasets

• Reddit AI companion posts — 5,504 posts — collected via PullPush API from eight AI companion-related subreddits (r/CharacterAI, r/KindroidAI, r/Replika, r/NomiAI, r/ChaiApp, r/Paradot, r/Chatbots, r/MyBoyfriendIsAI+r/AIRelationships)

Baselines vs proposed

• General bot posts: No emotion = 85.5% vs Own-bot posts: No emotion = 33.2%
• General bot posts: Relational topic = 7.8% vs Own-bot posts: Relational topic = 38.8%
• Positive emotional valence in own-bot posts = 33.0% vs general bot posts = 5.4%
• Mixed emotional valence in own-bot posts = 15.7% vs general bot posts = 0.8%

Limitations

• Data derive exclusively from Reddit; findings may not generalize to other platforms or more private user experiences.
• No temporal or longitudinal analysis to capture changes over time or evolution of relationships.
• Emotion coding depended on LLM interpretation of text, which may miss nuances, sarcasm, or implicit emotions; although validated, some noise likely remains.
• No direct adversarial robustness evaluation of coding against deceptive or insincere posts.
• Posts truncated at 3,000 characters in many cases, potentially losing contextual information relevant for accurate coding.
• Mixed-topic posts or posts containing multiple themes/subtopics were forced into a single primary category, potentially oversimplifying discourse.

Open questions / follow-ons

• How do these relationship narratives and emotional valences evolve longitudinally within users over time?
• To what extent do private, off-platform conversations about AI companions differ in emotional content and themes from public Reddit posts?
• What is the impact of platform policy changes (e.g., content moderation) on user emotional expression and relational discourse?
• How do specific AI companion features or bot personalities influence the nature and emotional valence of user relationships?

Why it matters for bot defense

This paper primarily informs AI bot-defense and CAPTCHA practitioners interested in the social and emotional dynamics around AI companion bots rather than direct adversarial attacks. Understanding user engagement with AI companions—including the frequency and emotional content of relational discourse—can help bot-defense professionals develop better behavioral threat models that distinguish genuine human interactions from bots in companion-like contexts. The finding that users publicly express complex emotional valence, including ambivalence, about their AI bots suggests that behavioral signals in such communities have nuanced patterns rather than simplistic positive or negative sentiment. Recognizing that AI companion forums serve dual social and technical support functions also highlights the need to separate social discourse from technical complaint signals when monitoring for anomalous behavior. Although not directly about CAPTCHAs or security attacks, the methodologies used to parse large-scale qualitative data with LLM coding may inspire more sophisticated behavioral analysis tools for bot detection and profiling in similar conversational settings.

Cite

@article{arxiv2608_00748,
  title={ Me and My Bot: What Users Talk About in AI Companion Communities on Reddit },
  author={ Richard A. Fabes },
  journal={arXiv preprint arXiv:2608.00748},
  year={ 2026 },
  url={https://arxiv.org/abs/2608.00748}
}

Read the full paper

• arXiv:2608.00748 (abstract)
• arXiv:2608.00748 (PDF)