Unsupervised Representation Learning and Abstractive Summarization of Stances in Social Media: Towards Explainable Detection Systems for Emerging Rumours

无监督表示学习和社交媒体立场的抽象总结：针对新出现的谣言的可解释检测系统

• 批准号: RGPIN-2022-04789
• 负责人: Trabelsi, Amine
• 金额: $ 1.82万
• 依托单位: Université de Sherbrooke
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749932
• 关键词: Unsupervised; Representation; Learning; Abstractive; Summarization

The proposed research will advance the fields of Artificial Intelligence and Natural Language Processing and build on our recent progress in automated summarization of online and social media forums. Outcomes from our work will be of use to any decision- and policy-making bodies trying to effectively gauge public opinion and provide a tool for individual citizens trying to make informed decisions out of online material. Our long-term research goal is to develop a robust and accurate unsupervised approach for automatic stance (people's views or opinions) detection and summarization from often polarizing social media content. The unsupervised nature of the computerized approach would make it convenient, timely and applicable for new topics arising online, for example the Covid-19 pandemic and vaccination program, and can be used to aggregate recent societal controversies and political issues and explain decisions on the veracity of rumour claims. Accurate stance detection and summarization remain challenging tasks; our research will help alleviate some of the technical hurdles preventing its achievement. Our specific objectives over the next 5 years include: 1) Computer-learning of robust representations of sentences expressed in social media discussions regarding contentious issues in an unsupervised fashion. The representations of sentences will lead to trained computer models that are less sensitive to perturbations and can be built in an unsupervised fashion by the model itself. This annotation-independence approach will accelerate the construction of representations for different domains involving stance content (e.g., summarizing differing views on the Covid-19 vaccination). 2) Devising an unsupervised method to summarize social media threads of discussions expressing stances about a topic. The method will generate a concise, abstractive and contrastive summary consisting of two different sequences of words, each emphasizing the key points of users' two main opposite positions. The method will not resort to examples of human-made summaries, permitting its broader application to discussions about new emerging issues or subjects online. 3) Making rumour-checking models more transparent by automatically generating explanations justifying a model's veracity prediction for a rumour expressed in social media given several conversational threads. We will frame the problem as a multi-task problem, including stance detection, veracity prediction and explanation generation and learn the computer models accordingly. The proposed innovative research program will also enable HQPs to master state-of-the-art natural language understanding and generation methods in computer science. Further, they will learn how to exploit them in relevant applications with real-world data, such as in rumour detection-skills that will make them ideal candidates for employment and skilled contributors to the Canadian labour force and economy.

拟议的研究将推进人工智能和自然语言处理领域，并建立在我们最近在在线和社交媒体论坛的自动摘要方面取得的进展。我们的工作成果将对任何试图有效衡量公众舆论的决策和政策制定机构有所帮助，并为试图从在线材料中做出明智决策的公民个人提供工具。我们的长期研究目标是开发一种强大而准确的无监督方法，用于从经常两极分化的社交媒体内容中自动检测和总结立场（人们的观点或意见）。计算机化方法的无监督性质将使其方便、及时并适用于在线出现的新话题，例如Covid-19大流行和疫苗接种计划，并可用于汇总最近的社会争议和政治问题，并解释谣言声明真实性的决定。准确的姿态检测和总结仍然是具有挑战性的任务；我们的研究将有助于减轻阻碍其实现的一些技术障碍。我们在未来5年的具体目标包括：1)以无监督的方式对社交媒体讨论中关于有争议问题的句子进行计算机学习。句子的表示将导致训练有素的计算机模型对扰动不那么敏感，并且可以由模型本身以无监督的方式构建。这种与注释无关的方法将加速构建涉及立场内容的不同领域的表示（例如，总结关于Covid-19疫苗接种的不同观点）。2)设计一种无监督的方法来总结社交媒体上表达某一话题立场的讨论线索。该方法将生成一个简洁、抽象、对比鲜明的摘要，由两个不同的词序列组成，每个词序列都强调用户两个主要对立立场的关键点。该方法不会诉诸于人工总结的例子，允许其更广泛地应用于在线讨论新出现的问题或主题。3)通过自动生成解释来证明模型对社交媒体上表达的谣言的准确性预测，从而使谣言检查模型更加透明。我们将把这个问题作为一个多任务问题，包括姿态检测、准确性预测和解释生成，并相应地学习计算机模型。拟议的创新研究计划还将使hqp能够掌握计算机科学中最先进的自然语言理解和生成方法。此外，他们将学习如何在现实世界数据的相关应用中利用它们，例如在谣言检测中，这些技能将使他们成为就业的理想候选人和加拿大劳动力和经济的熟练贡献者。