Characterizing AI Manipulation Risks in Brazilian YouTube Climate Discourse¶
Conference: AAAI2026 arXiv: 2511.06091 Code: To be confirmed Area: Audio & Speech Keywords: Climate Discourse, Persuasion, Theory of Mind, YouTube, LLM-generated Manipulation, Social Media Analysis
TL;DR¶
Through a psycholinguistic framework, this work analyzes 226,775 Brazilian YouTube climate change videos and 2,756,165 comments, revealing that emotional and moral rhetoric significantly drives user engagement. It further demonstrates that fine-tuned LLMs can automatically generate high-engagement climate denial comments, warning of the potential risks of generative AI in public opinion manipulation.
Background & Motivation¶
Climate change is a global threat that demands evidence-based policymaking and adequate public understanding. Social media platforms—YouTube in particular—have become primary channels for the dissemination of climate narratives, while simultaneously serving as breeding grounds for misinformation. Brazil, as a representative country of the Global South with significant ecological importance due to the Amazon rainforest, and with YouTube reaching approximately 68% of its population, constitutes an ideal setting for studying climate discourse.
The rapid development of LLMs in recent years has introduced a new risk dimension: prior research has shown that AI-generated text is persuasive and can even influence the formation of beliefs in conspiracy theories. This raises a central concern—can generative AI be weaponized to automate large-scale manipulation of climate discourse, for instance by fabricating a false consensus of "climate denial"?
The motivation of this paper is twofold: (1) to systematically quantify the effect of psycholinguistic features (persuasion strategies and Theory of Mind) on user engagement; and (2) to assess whether these patterns can be exploited by LLMs to automatically generate high-engagement manipulative content.
Core Problem¶
- Which psychological content features (persuasion strategies) most effectively drive audience engagement in Brazilian climate YouTube videos?
- To what extent can these psychological features predict content popularity?
- Can these insights be leveraged to design automated persuasive synthetic content (e.g., climate denial campaigns)?
Method¶
Dataset Construction¶
- Scale: 226,775 Brazilian Portuguese-language YouTube video metadata entries and 2,756,165 user comments, spanning 2019–2025.
- Collection Pipeline: Videos retrieved via the YouTube Data API v3 using 65 climate-related keywords; non-Portuguese content filtered using FastText language identification; low-relevance videos subsequently filtered by GPT-4.1-mini (temperature = 0).
- Video Classification: Videos categorized as short-form (< 3 minutes) or long-form (≥ 3 minutes); since 2023, short-form videos have become the dominant format for climate topics.
Psycholinguistic Annotation¶
Persuasion Strategy Annotation (Video Level)¶
GPT-4.1 with 5-shot prompting is used to annotate 10 persuasion strategies at the video level:
| Strategy | Description |
|---|---|
| Logical Appeal | Persuading through reasons and evidence |
| Emotional Appeal | Eliciting emotional responses |
| Statistical Evidence | Providing concrete data and statistics |
| Social Norm | Applying pressure through social conformity |
| Authority | Citing experts, institutions, and official reports |
| Personal Stories | Narrating personal experiences |
| Moral Appeal | Appealing to moral responsibility |
| Reciprocity | Emphasizing mutual benefits |
| Scarcity | Presenting time-limited and irreversible consequences |
| Common Ground | Building shared identity and values |
Manual validation yields an average F1 = 0.93 and accuracy = 0.98.
Theory of Mind Annotation (Comment Level)¶
GPT-4.1-mini is used to annotate user comments with 7 Theory of Mind categories: Belief, Intention, Desire, Emotion, Knowledge, Percept, and Non-literal. Manual validation yields F1 = 0.66 and accuracy = 0.83.
Case Study 1: Engagement Modeling¶
The impact of psycholinguistic features on user engagement is assessed through a three-stage analysis:
- Video level: Linear regression is applied to analyze the effect of the persuasion strategy vector \(\mathbf{p}_i\) on normalized like rate \(L_i\) and comment rate \(R_i\), controlling for confounders such as video duration and channel identity.
- Strategy–Mind correlation: Each video's comment ToM vectors are aggregated as \(\bar{\mathbf{t}}_i = (1/|C_i|)\sum_{c_k \in C_i} \mathbf{t}_k\), and partial correlations between persuasion strategies and ToM categories are computed.
- Comment level: Comment like counts and reply counts serve as dependent variables, with ToM annotations as independent variables, controlling for comment length and temporal distance.
Case Study 2: Popularity Prediction¶
Comment pairs \((c_i, c_j)\) are constructed with binary label \(y_{ij}^{(\ell)} = \mathbb{I}[\ell_i > \ell_j]\), predicting which comment is more popular. Three classes of methods are employed:
- LLM-as-a-Judge: GPT-4.1, o4-mini, Phi-4, Llama-3.1-8B, Llama-4-Maverick
- Fine-tuned Encoder Models: BERTimbau (Brazilian Portuguese BERT), DeBERTa V3
- Bradley-Terry Model: A linear classifier trained on comment embeddings
Case Study 3: Comment Generation¶
Llama-3-8B is fine-tuned to generate targeted comments under three scenarios:
- Sampling by persuasion strategy: Controlling for video-level effects.
- Sampling by ToM profile: Generating comments that reflect specific mental states.
- Sampling by belief stance: Three distinct models corresponding to "belief in climate change," "climate denial," and "extreme denial."
Evaluation: For each generated comment, \(K\) most similar real comments are retrieved, and their average like/reply counts serve as a proxy evaluation metric.
Key Experimental Results¶
Effect of Persuasion Strategies on Engagement¶
- The most frequently used strategies (Logical Appeal 51%, Authority 47%, Common Ground 36%) are all associated with lower user engagement.
- Emotional Appeal (33%) and Moral Appeal (26%) are associated with significantly higher engagement; moral appeals increase video likes by an average of 2.1%.
- The effectiveness of moral rhetoric in short-form videos has grown consistently over time.
Popularity Prediction¶
| Model | Best Accuracy | Condition |
|---|---|---|
| BERTimbau | 88% | No context, random pairing |
| GPT-4.1 | 82% | With video context + few-shot |
| DeBERTa V3 | 84% | With video context |
- Emotional ToM improves prediction performance by an average of 4.69%.
- BERTimbau achieves 88% using comment text alone, indicating that comment content itself contains sufficient signals for engagement prediction.
Comment Generation¶
| Model | Estimated Like Count \(\hat{\ell}_{gen|1}\) | |---|---| | Baseline (random comments) | 2.20 | | Engaging (fine-tuned on high-like comments) | 7.25 (3.3× improvement) | | Believe (climate belief) | 3.23 | | Denial (climate denial) | 1.91 | | Extreme (extreme denial) | 2.37 |
Comments generated by the extreme denial model exhibit greater detail and rhetorical intensity, making them more engaging than those from the standard denial model.
Highlights & Insights¶
- Large-scale psycholinguistic dataset: A Brazilian climate discourse dataset comprising 226,775 videos and 2,756,165 comments, annotated with persuasion strategies and ToM labels, is released—one of the largest non-English resources in this domain.
- Clear causal chain from persuasion to engagement: A complete analytical pipeline from "persuasion strategies → user psychological states → engagement behavior" is established across three progressively layered case studies.
- Empirical demonstration of AI manipulation risks: Rather than remaining at the level of theoretical discussion, the work demonstrates the feasibility of automated opinion manipulation through actual LLM fine-tuning; the outputs of the extreme denial model exhibit a striking degree of realism.
- Unique Brazilian/Portuguese perspective: This work fills a gap in climate discourse research concerning Global South countries. The finding that BERTimbau outperforms GPT-4.1 on Portuguese comments further underscores the importance of language-specificity.
- In-depth analysis of short-form video trends: The paper reveals that climate short-form videos have surpassed long-form videos since 2023, a format shift that further compresses the space available for fact-checking.
Limitations & Future Work¶
- Text-only analysis: Visual, audio, and other multimodal elements affecting persuasiveness are not considered—a particularly notable limitation given YouTube's nature as a video platform.
- Incomplete engagement metrics: Important factors such as recommendation algorithms, individual psychological differences, and user demographics are not accounted for.
- Geographic and linguistic scope: All findings are limited to Brazilian Portuguese YouTube content; cross-lingual and cross-platform generalizability remains unverified.
- Moderate ToM annotation quality: An F1 of 0.66 represents a substantial gap compared to the 0.93 achieved for persuasion strategy annotation, potentially affecting the reliability of downstream analyses.
- Indirect evaluation of generated comments: Proxy evaluation via nearest-neighbor retrieval rather than real-platform deployment precludes confirmation of actual engagement effects.
- Future work may extend the framework to multilingual and multi-platform (TikTok/X) comparisons, and incorporate multimodal analysis.
Related Work & Insights¶
| Dimension | Ours | Existing Climate Discourse Research |
|---|---|---|
| Language/Region | Brazilian Portuguese | Predominantly English |
| Analytical Framework | Dual-dimension: persuasion strategies + ToM | Typically single-dimension (stance detection or sentiment analysis) |
| Manipulation Risk Assessment | LLM fine-tuning generation experiments | Primarily theoretical discussion |
| Data Scale | 226,775 videos + 2,756,165 comments | Typically < 150,000 tweets |
| Platform | YouTube (videos + comments) | Predominantly Twitter/X |
Compared to Costello et al. (2024) on AI persuasion, this work shifts focus from controlled experiments to real-world social media settings. Compared to Breum et al. (2024) on LLM persuasiveness analysis, this work adds the ToM dimension and actual generation experiments.
Implications and Connections
- Warning for generative AI governance: The high engagement efficacy of emotional and moral rhetoric, combined with LLMs' generative capacity, creates the conditions for low-cost, large-scale opinion manipulation, calling for governance frameworks around synthetic media.
- Aggravated challenges for fact-checking: The shift toward short-form video compresses the space for in-depth information dissemination, and algorithmic recommendation further amplifies emotional content, undermining fact-checking effectiveness.
- BERTimbau > GPT-4.1 suggests that in specific linguistic and cultural contexts, localized smaller models may outperform general-purpose large models—an important finding for multilingual AI research.
- Transferable methodology: The dual-dimension analytical framework of persuasion strategies and ToM can be applied to other social issues (e.g., vaccine hesitancy, political polarization) and other platforms.
Rating¶
- Novelty: ⭐⭐⭐⭐ (The dual-dimension framework of persuasion strategies + ToM is novel; the empirical analysis of LLM manipulation risks is forward-looking.)
- Experimental Thoroughness: ⭐⭐⭐⭐ (Three well-designed case studies with large-scale data, though ToM annotation quality and the generation evaluation approach leave room for improvement.)
- Writing Quality: ⭐⭐⭐⭐ (Clear structure, rich case studies, and a coherent logical chain from analysis to risk warning.)
- Value: ⭐⭐⭐⭐ (Significant implications for AI ethics and social media governance; the public release of the dataset constitutes a meaningful community contribution.)