DeMod: A ChatGPT-Based Tool for Detecting, Explaining, and Modifying Toxic Social Media Content

核心概念

DeMod is a new tool that leverages ChatGPT to help social media users detect, understand, and modify toxic content in their posts before publishing, aiming to promote responsible online communication.

要約

This paper introduces DeMod, a novel tool designed to assist social media users in censoring toxic content before posting. Recognizing the limitations of existing toxicity detection tools that primarily focus on identification, the authors conducted a needfinding study on Weibo, a popular Chinese social media platform. The study revealed users' desire for a more comprehensive tool that not only detects toxicity but also provides explanations and suggests modifications.

Based on these findings, the authors developed DeMod, a ChatGPT-enhanced tool with three key modules: User Authorization, Explainable Detection, and Personalized Modification. The Explainable Detection module utilizes ChatGPT to provide fine-grained detection results, highlighting specific toxic keywords and offering immediate explanations for their toxicity. Additionally, it simulates audience reactions to the post, providing users with insights into potential social impact. The Personalized Modification module leverages ChatGPT's few-shot learning capabilities to suggest revisions that detoxify the content while preserving the user's intended meaning and personal language style.

The authors implemented DeMod as a third-party tool for Weibo and conducted evaluations with 35 participants. Results demonstrated DeMod's effectiveness in detecting and modifying toxic content, outperforming baseline methods. Participants also praised its ease of use and appreciated the comprehensive functionality, particularly the dynamic explanation and personalized modification features.

The paper concludes by highlighting the importance of holistic censorship tools that go beyond simple detection. The authors emphasize the need for interpretability in both the detection process and results, empowering users to understand and regulate their online behavior.

統計

The number of offensive expressions identified on Weibo from November 2022 to August 2023 exceeded 120 million.
DeMod with the GPT-4 model achieved 73.50% accuracy in toxicity detection.
DeMod with the GPT-3.5-turbo model achieved 69.35% accuracy in toxicity detection.
Perspective API achieved 52.45% accuracy in toxicity detection.
After using DeMod, the proportion of toxic samples decreased by 94.38%.

引用

"This tool can greatly unload my brain. I am often not aware my words may hurt others."
"It’s not enough to tell me whether my post is toxic or not. The specific words or phrases that might harm others should be identified."
"The keywords should be highlighted directly. I don’t want to waste my time, it’s just a post."
"For the toxic content that I may not realize, it’s better to offer some reasons to let me know whether I should post the content."
"If there are some inappropriate sentences or words, it would be better to replace with some subtly expressions automatically and I don’t want to edit directly."

抽出されたキーインサイト

DeMod: A Holistic Tool with Explainable Detection and Personalized Modification for Toxicity Censorship

by Yaqiong Li, ... 場所 arxiv.org 11-05-2024

https://arxiv.org/pdf/2411.01844.pdf

深掘り質問

How can DeMod be adapted to address the nuances of toxicity across different cultures and languages on social media?

DeMod's current design, while effective for Weibo, needs significant adaptations to handle the complexities of toxicity across diverse cultures and languages:

Multilingual Support: The foundation of DeMod, ChatGPT, is inherently multilingual. Leveraging this, DeMod can be trained on datasets of toxic language spanning various languages. This requires careful selection of representative datasets that capture the nuances of toxicity in each language.
Cultural Sensitivity Training: Toxicity is not universally defined. What's considered offensive in one culture might be acceptable in another. DeMod needs to be trained on culturally-specific datasets that account for these variations. This could involve:

Contextual Understanding: DeMod should be able to differentiate between similar phrases with varying meanings based on cultural context. For example, a phrase might be harmless in a friendly banter but offensive in a formal setting.
Slang and Idioms: Each language and culture has its own set of slang and idioms, which might be misinterpreted by a generic model. DeMod should be trained to recognize and interpret these correctly.

Collaboration with Cultural Experts: Developing culturally sensitive AI requires collaboration with sociologists, linguists, and cultural experts from different regions. Their insights can help in:

Dataset Curation: Ensuring the training data accurately reflects the cultural nuances of toxicity.
Model Evaluation: Assessing the model's performance in identifying and modifying toxic language in a culturally appropriate manner.

Continuous Learning and Adaptation: Culture is dynamic, and so is language. DeMod should be designed to continuously learn and adapt to evolving cultural norms and linguistic trends. This can be achieved through:

User Feedback Mechanisms: Allowing users to flag false positives or suggest improvements to the model's understanding of cultural nuances.
Regular Model Updates: Periodically retraining the model on updated datasets that reflect the latest cultural and linguistic trends.
By incorporating these adaptations, DeMod can evolve from a language-specific tool to a culturally-aware platform for mitigating toxicity on a global scale.

While DeMod focuses on individual posts, could similar tools be developed to analyze and mitigate the spread of toxicity at a larger scale, such as identifying and addressing toxic communities or trends?

Absolutely, tools inspired by DeMod's core functionalities can be adapted to combat toxicity on a larger scale, targeting communities and trends:

Network Analysis for Toxic Community Identification: Instead of individual posts, the focus shifts to analyzing user interactions and network structures. Techniques like:

Graph-based Analysis: Can identify communities with dense connections exhibiting high toxicity levels.
Sentiment Analysis: Can track the overall sentiment within a community, identifying those with predominantly negative or hostile communication patterns.

Trend Analysis for Proactive Toxicity Mitigation:

Natural Language Processing (NLP): Can be used to identify emerging toxic keywords, hashtags, or phrases spreading across the platform.
Time Series Analysis: Can detect sudden spikes in the usage of toxic language, indicating a potential viral trend.

Intervention Strategies: Once toxic communities or trends are identified, these tools can facilitate interventions like:

Targeted Content Moderation: Prioritizing content moderation efforts towards identified communities or trends.
Community Guidelines Enforcement: Prompting users in these communities with platform guidelines and encouraging positive interactions.
Promoting Counter-Narratives: Elevating positive content and voices within these communities to counter the spread of toxicity.

Explainability and Transparency: Similar to DeMod, these large-scale tools should provide explanations for their decisions, highlighting the factors contributing to a community or trend being flagged as toxic. This transparency is crucial for building trust and ensuring fair moderation practices.
However, addressing toxicity at a larger scale raises ethical considerations:

Risk of Over-Blocking: Aggressively targeting entire communities risks suppressing valuable discussions and limiting free expression.
Bias Amplification: If the training data is biased, these tools can inadvertently amplify existing societal biases, disproportionately impacting marginalized communities.
Therefore, developing these tools requires careful consideration of ethical implications, ensuring they promote healthy online environments without stifling free speech.

What are the ethical implications of using AI to moderate online speech, and how can we ensure that tools like DeMod are used responsibly and do not stifle free expression?

Using AI for online speech moderation, while promising, presents significant ethical challenges:

Bias and Discrimination: AI models are trained on data, which can reflect and amplify existing societal biases. This can lead to unfair moderation, disproportionately silencing marginalized voices.
Censorship and Free Speech: Overly aggressive moderation can stifle free expression and limit open dialogue, even if the intent is to curb toxicity. Striking a balance between protecting users and upholding free speech is crucial.
Lack of Transparency and Explainability: Black-box AI models make it difficult to understand why certain content is flagged or removed. This lack of transparency can erode trust and make it challenging to appeal decisions.
Contextual Understanding: AI often struggles with nuance and sarcasm, potentially misinterpreting harmless expressions as toxic. This can lead to the unjust removal of content and user frustration.
To ensure responsible use of tools like DeMod and mitigate these ethical concerns:

Diverse and Unbiased Datasets: Training data should be carefully curated to represent diverse viewpoints and minimize bias. Regular audits and debiasing techniques can help identify and mitigate bias.
Human Oversight and Appeal Mechanisms: Human moderators should be involved in overseeing AI decisions, particularly in complex cases. Clear appeal processes are crucial for users to challenge incorrect flagging or removal.
Transparency and Explainability: Tools should provide clear explanations for their decisions, highlighting the specific words or phrases deemed toxic and the reasoning behind it. This transparency builds trust and allows for user education.
Focus on Education and Empowerment: Rather than solely relying on content removal, tools should prioritize educating users about online etiquette and empowering them to engage in respectful dialogue.
Continuous Evaluation and Improvement: Regularly evaluate the tool's impact on different communities, identify potential biases or unintended consequences, and make necessary adjustments to ensure fairness and uphold free expression.
Developing ethical AI for online speech moderation is an ongoing process. It requires collaboration between AI developers, ethicists, social scientists, and the wider community to create tools that foster inclusive and respectful online environments without compromising freedom of expression.