# Incongruent Positivity: When Miscalibrated Positivity Undermines Online Supportive Conversations

arXiv: https://arxiv.org/abs/2509.10184 · HTML: https://mlsanity.com/paper/2509.10184/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 54 references that are in the map.
- First posted on arXiv 12 Sep 2025.

## Builds on (most cited first, 13 of 13)

- [Reducing conversational agents' overconfidence through linguistic calibration](https://mlsanity.com/paper/2012.14983/) (2020)
- [Emotionally Numb or Empathetic? Evaluating How LLMs Feel Using EmotionBench](https://mlsanity.com/paper/2308.03656/) (2023)
- [Generating Empathetic Responses by Looking Ahead the User's Sentiment](https://mlsanity.com/paper/1906.08487/) (2019)
- [Emotion-aware Chat Machine: Automatic Emotional Response Generation for Human-like Emotional Interaction](https://mlsanity.com/paper/2106.03044/) (2021)
- [Boosting Distress Support Dialogue Responses with Motivational Interviewing Strategy](https://mlsanity.com/paper/2305.10195/) (2023)
- [Countering Misinformation via Emotional Response Generation](https://mlsanity.com/paper/2311.10587/) (2023)
- [Socratic Reasoning Improves Positive Text Rewriting](https://mlsanity.com/paper/2403.03029/) (2024)
- [Don't Get Too Excited -- Eliciting Emotions in LLMs](https://mlsanity.com/paper/2503.02457/) (2025)
- [ReflectDiffu:Reflect between Emotion-intent Contagion and Mimicry for Empathetic Response Generation via a RL-Diffusion Framework](https://mlsanity.com/paper/2409.10289/) (2024)
- [Mitigating Unhelpfulness in Emotional Support Conversations with Multifaceted AI Feedback](https://mlsanity.com/paper/2401.05928/) (2024)
- [Supporters and Skeptics: LLM-based Analysis of Engagement with Mental Health (Mis)Information Content on Video-sharing Platforms](https://mlsanity.com/paper/2407.02662/) (2024)
- [The Muddy Waters of Modeling Empathy in Language: The Practical Impacts of Theoretical Constructs](https://mlsanity.com/paper/2501.14981/) (2025)
- [Fluent but Unfeeling: The Emotional Blind Spots of Language Models](https://mlsanity.com/paper/2509.09593/) (2025)

## Built on by (most cited first, 1 of 1)

- [Aligning with Lived Experience: Heterogeneous Benefits of Fine Tuning in Mental Health Support Generation](https://mlsanity.com/paper/2609.21075/) (2026)

## Abstract (arXiv)

In emotionally supportive conversations, well-intended positivity can sometimes misfire, leading to responses that feel dismissive, minimizing, or unrealistically optimistic. We examine this phenomenon of incongruent positivity as miscalibrated expressions of positive support in both human and LLM generated responses. To this end, we collected real user-assistant dialogues from Reddit across a range of emotional intensities and generated additional responses using large language models for the same context. We categorize these conversations by intensity into two levels: Mild, which covers relationship tension and general advice, and Severe, which covers grief and anxiety conversations. This level of categorization enables a comparative analysis of how supportive responses vary across lower and higher stakes contexts. Our analysis reveals that LLMs are more prone to unrealistic positivity through dismissive and minimizing tone, particularly in high-stakes contexts. To further study the underlying dimensions of this phenomenon, we finetune LLMs on datasets with strong and weak emotional reactions. Moreover, we developed a weakly supervised multilabel classifier ensemble (DeBERTa and MentalBERT) that shows improved detection of incongruent positivity types across two sorts of concerns (Mild and Severe). Our findings shed light on the need to move beyond merely generating generic positive responses and instead study the congruent support measures to balance positive affect with emotional acknowledgment. This approach offers insights into aligning large language models with affective expectations in the online supportive dialogue, paving the way toward context-aware and trust preserving online conversation systems.