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

拟议的研究将推动人工智能和自然语言处理领域的发展，并建立在我们最近在在线和社交媒体论坛自动摘要方面取得的进展的基础上。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用，并为试图根据在线材料做出明智决定的公民个人提供工具。我们的长期研究目标是开发一种健壮而准确的非监督方法，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便于、及时和适用于在线出现的新话题，例如新冠肺炎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，并解释关于谣言说法真实性的决定。准确的姿态检测和摘要仍然是具有挑战性的任务；我们的研究将有助于缓解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)在没有监督的情况下，通过计算机学习在社交媒体上关于有争议的问题的讨论中表达的句子的稳健表示。句子的表示将导致经过训练的计算机模型，这些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建立。这种注释独立的方法将加快构建涉及立场内容的不同领域的表示(例如，总结对新冠肺炎疫苗的不同观点)。2)设计一种无监督的方法来总结社交媒体上表达对某个话题的立场的讨论线索。该方法将生成一个简洁、抽象和对比的摘要，由两个不同的单词序列组成，每个单词序列强调用户两个主要相对位置的关键点。这种方法不会求助于人造摘要的例子，允许它更广泛地应用于在线上关于新出现的问题或主题的讨论。3)通过在给定几个对话线索的情况下自动生成解释，证明模型对社交媒体上表达的谣言的准确性预测是合理的，从而使谣言核查模型更加透明。我们将问题框架为一个多任务问题，包括姿态检测、准确性预测和解释生成，并学习相应的计算机模型。拟议的创新研究计划还将使HQP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，他们还将学习如何在具有真实世界数据的相关应用中利用这些技能，例如在谣言检测方面--这些技能将使他们成为就业的理想人选，并成为加拿大劳动力和经济的熟练贡献者。

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.