Ambiguous Social-Media Cues and Overconfident AI Relationship Interpretation

Conversational AI is increasingly used to interpret ambiguous interpersonal cues, but confident interpretations can shape how users understand emotionally significant situations. This case study examines a long Google Gemini conversation in which ambiguous social-media activity was repeatedly framed as evidence of a third party's hidden romantic interest in the user. The supplied PDF contains 888 user-model exchange pairs across 736 pages, primarily in Bengali and romanized Bengali (Banglish). An exploratory qualitative reading identified examples of third-party mind-reading, certainty inflation, narrative escalation, and inconsistent treatment of alternative explanations. In a random sample of 50 exchanges coded by the author, 74% contained at least one of the first three patterns; a second coder agreed only partially ($\kappa=0.33$ to 0.78 for four categories and near zero for a rare fifth), so the categories need refinement. The case illustrates how overconfident AI responses may reinforce a user's confirmation bias. As a single case, it cannot establish causal effects on the user or reveal the third party's actual intentions. The study was undertaken to identify response failure modes that could inform efforts to reduce overconfident inference and confirmation-bias reinforcement in a chatbot project associated with the author. It does not evaluate that project or demonstrate that any intervention has reduced bias. The analysis proposes safeguards: separate observations from inferences, preserve uncertainty, avoid unsupported claims about third-party mental states, and respect direct interpersonal feedback. Keywords: large language models; sycophancy; epistemic overconfidence; confirmation bias; romantic advice; third-party mind-reading; Banglish; case study