аналитика - Language Processing - # Deception and Factuality in Argumentation

Deception and Factuality in Argumentation: The DeFaBel Corpus

Q: What ethical considerations should be taken into account when using deception detection models?

When utilizing deception detection models, several ethical considerations must be carefully addressed to ensure responsible and fair use of the technology. Firstly, it is crucial to consider the potential impact on individuals' privacy and reputation. Deception detection models may analyze personal texts or communications, leading to inferences about an individual's honesty or integrity. As such, there is a risk of misinterpretation or unfair assessment that could harm an individual's reputation. Secondly, transparency and consent are essential ethical principles to uphold when deploying deception detection models. Individuals should be informed if their texts are being analyzed for deceptive content, and they should have the option to opt-out or provide explicit consent for such analysis. Without transparency and consent, there is a risk of violating individuals' rights and autonomy. Moreover, bias mitigation is critical in developing and using deception detection models ethically. Biases can inadvertently influence model predictions based on factors like race, gender, or cultural background. It is imperative to address these biases through diverse training data sets, regular audits of model performance for fairness metrics, and ongoing monitoring during deployment. Lastly, considering the potential consequences of false positives from deception detection models is vital. Misidentifying truthful statements as deceptive can have severe repercussions for individuals unjustly accused of dishonesty. Therefore, mechanisms must be in place to rectify errors promptly and minimize any negative impacts on affected parties.

Q: How can future studies better understand the reasons behind varying distributions of familiarity and self-assessed persuasiveness?

To gain deeper insights into the reasons behind varying distributions of familiarity with topics and self-assessed persuasiveness in argumentative texts: Qualitative Interviews: Conduct qualitative interviews with participants who provided ratings on familiarity with topics and confidence levels in persuasiveness. By delving into their thought processes while generating arguments or assessing familiarity with topics, researchers can uncover underlying motivations influencing their responses. Content Analysis: Analyze the actual argumentative texts produced by participants alongside their ratings on familiarity/self-assessed persuasiveness. Look for patterns or language cues within the text that correlate with high/low ratings in these categories. Survey Design: Refine survey instruments used for collecting familiarity/persuasiveness ratings by incorporating open-ended questions that prompt participants to elaborate on why they rated themselves as such. 4 .Experimental Manipulation: Introduce experimental conditions where external factors (e.g., time constraints) are varied systematically during argument generation tasks to observe how these variables affect participants' perceptions of topic familiarity/self-confidence.

Q: In what ways can readers' ratings be incorporated to assess the persuasiveness of argumentative texts beyond self-assessment?

Incorporating readers' ratings offers valuable insights into evaluating persuasive effectiveness beyond authors' self-assessments: 1 .Peer Review Panels: Establish peer review panels comprising independent evaluators who rate argumentative texts based on persuasiveness criteria agreed upon beforehand. 2 .Crowdsourced Ratings: Utilize crowdsourcing platforms where multiple raters evaluate arguments anonymously based on predefined rubrics measuring persuasive elements like logic coherence, evidence quality,and emotional appeal. 3 .**Expert Evaluation: Invite domain experts familiar with specific subject matter areas covered in argumentative texts to provide expert assessments regarding persuasiveness, factual accuracy,and overall effectiveness. 4 .Comparative Analysis: Compare reader ratings against author self-assessments,to identify discrepanciesand gain a more comprehensive understandingof persuasive strengthsand weaknessesinargumentativetexts By integrating diverse perspectives through reader evaluations,researcherscan obtain amore robustassessmentofthequalityandimpactofargumentativetextsbeyondsolelyrelyingonauthors'self-perceptions.Thismulti-facetedapproachenhancescredibilityandreliabilityinassessingpersuasivecontentwhileprovidinginsightsthatmaynotbecapturedthroughself-evaluationsalone

Основные понятия

The interplay between deception and factuality in argumentation is explored through the creation of the DeFaBel corpus, shedding light on how personal beliefs influence persuasive arguments.

Аннотация

The DeFaBel Corpus investigates the relationship between deception, factuality, and personal beliefs in argumentation. Participants were tasked with writing arguments supporting statements, regardless of their factual accuracy or personal beliefs. The corpus contains 1031 texts in German, with 643 deceptive and 388 non-deceptive instances. Surprisingly, individuals showed more confidence when arguing for non-deceptive statements aligned with their beliefs but less confidence when supporting factual claims. This corpus serves as a foundation for developing deception detection models that disentangle factuality and deceptiveness.

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Статистика

The DeFaBel corpus contains 1031 texts in German.
643 texts are labeled as deceptive while 388 are non-deceptive.
Participants reported higher confidence when arguing for non-deceptive statements aligned with their beliefs.
Individuals were less confident when supporting factual claims compared to non-factual ones.

Цитаты

"We find that people are more confident in the persuasiveness of their arguments when the statement is aligned with their belief." - Research Findings
"Our work lays the foundation for the development of deception detection models that are not confounded by the interaction of factuality and belief." - Future Implications
"Participants showed more familiarity and confidence when arguing for non-factual statements than for factual ones." - Participant Behavior

Ключевые выводы из

Can Factual Statements be Deceptive? The DeFaBel Corpus of Belief-based Deception

by Aswa... в arxiv.org 03-18-2024

https://arxiv.org/pdf/2403.10185.pdf

Can Factual Statements be Deceptive? The DeFaBel Corpus of Belief-based Deception

Дополнительные вопросы

What ethical considerations should be taken into account when using deception detection models?

When utilizing deception detection models, several ethical considerations must be carefully addressed to ensure responsible and fair use of the technology. Firstly, it is crucial to consider the potential impact on individuals' privacy and reputation. Deception detection models may analyze personal texts or communications, leading to inferences about an individual's honesty or integrity. As such, there is a risk of misinterpretation or unfair assessment that could harm an individual's reputation.
Secondly, transparency and consent are essential ethical principles to uphold when deploying deception detection models. Individuals should be informed if their texts are being analyzed for deceptive content, and they should have the option to opt-out or provide explicit consent for such analysis. Without transparency and consent, there is a risk of violating individuals' rights and autonomy.
Moreover, bias mitigation is critical in developing and using deception detection models ethically. Biases can inadvertently influence model predictions based on factors like race, gender, or cultural background. It is imperative to address these biases through diverse training data sets, regular audits of model performance for fairness metrics, and ongoing monitoring during deployment.
Lastly, considering the potential consequences of false positives from deception detection models is vital. Misidentifying truthful statements as deceptive can have severe repercussions for individuals unjustly accused of dishonesty. Therefore, mechanisms must be in place to rectify errors promptly and minimize any negative impacts on affected parties.

How can future studies better understand the reasons behind varying distributions of familiarity and self-assessed persuasiveness?

To gain deeper insights into the reasons behind varying distributions of familiarity with topics and self-assessed persuasiveness in argumentative texts:

Qualitative Interviews: Conduct qualitative interviews with participants who provided ratings on familiarity with topics and confidence levels in persuasiveness. By delving into their thought processes while generating arguments or assessing familiarity with topics, researchers can uncover underlying motivations influencing their responses.

Content Analysis: Analyze the actual argumentative texts produced by participants alongside their ratings on familiarity/self-assessed persuasiveness. Look for patterns or language cues within the text that correlate with high/low ratings in these categories.

Survey Design: Refine survey instruments used for collecting familiarity/persuasiveness ratings by incorporating open-ended questions that prompt participants to elaborate on why they rated themselves as such.

4 .Experimental Manipulation: Introduce experimental conditions where external factors (e.g., time constraints) are varied systematically during argument generation tasks to observe how these variables affect participants' perceptions of topic familiarity/self-confidence.

In what ways can readers' ratings be incorporated to assess the persuasiveness of argumentative texts beyond self-assessment?

Incorporating readers' ratings offers valuable insights into evaluating persuasive effectiveness beyond authors' self-assessments:
1 .Peer Review Panels: Establish peer review panels comprising independent evaluators who rate argumentative texts based on persuasiveness criteria agreed upon beforehand.
2 .Crowdsourced Ratings: Utilize crowdsourcing platforms where multiple raters evaluate arguments anonymously based on predefined rubrics measuring persuasive elements like logic coherence,
evidence quality,and emotional appeal.
3 .**Expert Evaluation: Invite domain experts familiar with specific subject matter areas covered in argumentative texts
to provide expert assessments regarding persuasiveness,
factual accuracy,and overall effectiveness.
4 .Comparative Analysis: Compare reader ratings against author self-assessments,to identify discrepanciesand gain a more comprehensive understandingof persuasive strengthsand weaknessesinargumentativetexts
By integrating diverse perspectives through reader evaluations,researcherscan obtain amore robustassessmentofthequalityandimpactofargumentativetextsbeyondsolelyrelyingonauthors'self-perceptions.Thismulti-facetedapproachenhancescredibilityandreliabilityinassessingpersuasivecontentwhileprovidinginsightsthatmaynotbecapturedthroughself-evaluationsalone