Reinforcement Learning-based Counter-Misinformation Response Generation: A Case Study of COVID-19 Vaccine Misinformation
Reinforcement Learning-based Counter-Misinformation Response Generation: A Case Study of COVID-19 Vaccine Misinformation
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DOI：
10.1145/3543507.3583388
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发表时间：
2023-03
期刊：
影响因子：
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通讯作者：
Bing He;M. Ahamad;Srijan Kumar
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文献类型：
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作者：
Bing He;M. Ahamad;Srijan Kumar
网络错误信息的传播威胁着公共卫生、民主和更广泛的社会。虽然专业的事实核查人员通过核查流行的虚假声明形成了第一道防线，但他们并不直接与错误信息传播者进行对话。另一方面，非专业的普通用户充当了主动反击错误信息的地面眼睛-最近的研究表明，96%的反击错误信息的反应是由普通用户做出的。然而，研究也发现，有2/3的时候，这些回应是粗鲁的，缺乏证据。这项工作旨在创建一个反错误信息响应生成模型，使用户能够有效地纠正错误信息。这一目标是具有挑战性的，因为缺乏包含理想的反错误信息响应的基础事实的数据集，并且缺乏可以生成由传播理论支持的响应的模型。在这项工作中，我们创建了两个新的数据集的错误信息和反错误信息的反应对在野外的社交媒体和众包从受过大学教育的学生。我们对收集到的数据进行注释，以区分差的和理想的反应，这些反应是真实的，礼貌的，并反驳错误信息。我们提出了MisinfoCorrect，这是一个基于强化学习的框架，可以学习为输入的错误信息帖子生成反错误信息响应。该模型在保持文本流畅性和相关性的同时，奖励生成者提高礼貌性、真实性和反驳态度。定量和定性评估表明，我们的模型优于几个基线生成高质量的反响应。这项工作说明了生成文本模型对社会公益的承诺-在这里，帮助创建一个安全可靠的信息生态系统。代码和数据可在https://github.com/claws-lab/MisinfoCorrect上访问。
The spread of online misinformation threatens public health, democracy, and the broader society. While professional fact-checkers form the first line of defense by fact-checking popular false claims, they do not engage directly in conversations with misinformation spreaders. On the other hand, non-expert ordinary users act as eyes-on-the-ground who proactively counter misinformation – recent research has shown that 96% counter-misinformation responses are made by ordinary users. However, research also found that 2/3 times, these responses are rude and lack evidence. This work seeks to create a counter-misinformation response generation model to empower users to effectively correct misinformation. This objective is challenging due to the absence of datasets containing ground-truth of ideal counter-misinformation responses, and the lack of models that can generate responses backed by communication theories. In this work, we create two novel datasets of misinformation and counter-misinformation response pairs from in-the-wild social media and crowdsourcing from college-educated students. We annotate the collected data to distinguish poor from ideal responses that are factual, polite, and refute misinformation. We propose MisinfoCorrect, a reinforcement learning-based framework that learns to generate counter-misinformation responses for an input misinformation post. The model rewards the generator to increase the politeness, factuality, and refutation attitude while retaining text fluency and relevancy. Quantitative and qualitative evaluation shows that our model outperforms several baselines by generating high-quality counter-responses. This work illustrates the promise of generative text models for social good – here, to help create a safe and reliable information ecosystem. The code and data is accessible on https://github.com/claws-lab/MisinfoCorrect.