Emotions and personality traits in argumentation: An empirical evaluation
Bibliographic record
Abstract
Argumentation is a mechanism to support different forms of reasoning such as decision making and persuasion and always cast under the light of critical thinking. In the latest years, several computational approaches to argumentation have been proposed to detect conflicting information, take the best decision with respect to the available knowledge, and update our own beliefs when new information arrives. The common point of all these approaches is that they assume a purely rational behavior of the involved actors, be them humans or artificial agents. However, this is not the case as humans are proved to behave differently, mixing rational and emotional attitudes to guide their actions. Some works have claimed that there exists a strong connection between the argumentation process and the emotions felt by people involved in such process. We advocate a complementary, descriptive and experimental method, based on the collection of emotional data about the way human reasoners handle emotions during debate interactions. Across different debates, people’s argumentation in plain English is correlated with the emotions automatically detected from the participants, their engagement in the debate, and the mental workload required to debate. Results show several correlations among emotions, engagement and mental workload with respect to the argumentation elements. For instance, when two opposite opinions are conflicting, this is reflected in a negative way on the debaters’ emotions. Beside their theoretical value for validating and inspiring computational argumentation theory, these results have applied value for developing artificial agents meant to argue with human users or to assist users in the management of debates.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".