Debate Chatbots to Facilitate Critical Thinking on YouTube: Social Identity and Conversational Style Make A Difference
Bibliographic record
Abstract
Exposure to diverse perspectives is helpful for bursting the filter bubble in online public video platforms. The recent advancement of Large Language Models (LLMs) illuminates the potential of creating a debate chatbot that prompts users to critically examine their stances on a topic formed by watching videos. However, whether the viewer is influenced by the chatbot may depend on its persona. In this paper, we investigated the effect of two relevant persona attributes - social identity and rhetorical styles - on critical thinking. In a mixed-methods study (n=36), we found that chatbots with outgroup (vs. ingroup) identity (t(33)=-2.33, p=0.03) and persuasive (vs. eristic) rhetoric (t(44)=1.98, p=0.05) induced critical thinking most effectively, making participants re-examine their arguments. However, participants’ stances remain largely unaffected, likely due to the chatbot’s lack of contextual knowledge and human touch. Our paper provides empirical groundwork for designing chatbot persona for remedying filter bubbles in online communities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".