Exaggeration, cohesion, and fragmentation in on-line forums

在线论坛可以支持形成具有共同兴趣和需求的社会社区。如果用户群体以分裂的态度或错误的信念相互支持，它们也会有消极的一面。这些所谓的回音室效应导致的社会分裂被认为是暴力和极端主义抬头、政治僵局和社会流动性下降背后的一个引擎。这个项目的动机是观察到回声室效应涉及到从更温和的语言到更极端的语言的逐渐转变。此外，当极端的社会分裂已经发生时，修复损害是困难的。因此，能够利用联机语言的模式及早发现联机社会的分裂，将是支持及早和更有效地干预联机论坛有害趋势的重大突破。我们已经确定了创建此功能的两个主要挑战。首先，目前的NLP方法在理解意义是一个尺度上的程度的表达方面很差，比如在成本、质量、诚实或绩效等维度上定义的尺度。例如，“相当种族主义”、“真正的种族主义”和“令人难以置信的种族主义”表达了不同程度的反对，但这些差异并没有被当前的算法充分捕捉到。这个限制是我们问题的核心，因为回音室效应通常涉及事实主张、情绪或态度的逐渐夸大。第二个挑战来自这样一个事实，即在社会行为分析中使用语言内容的方法是有限的。虽然许多研究揭示了词汇选择和社会群体之间的系统性联系，但很少有研究涉及语言推理和社会趋势之间的关系。然而，要追踪回声室效应中向语义极端的逐渐转变，需要做出一定的语言推断。这是因为在任何特定情况下，推断意义的哪个潜在维度是相关的，关键取决于谁在说话以及他们在说什么。例如，“利物浦要好得多”可能与乐迷之间讨论的文化卓越程度有关，但与讨论住房的人之间的成本规模有关。因此，需要在结合语言和社会信息的方法上取得根本性的进步，以在线表征回声室效应并预测未来分裂的风险。该项目是实验和计算语言学家（PI）和机器学习和社会网络分析专家（Co-I）之间的新合作。它的组成部分整合了双方合作者的专业知识。高级文本挖掘和数据分析将用于生成大规模和实验规范的标量表达式数据集的材料，使用流行的在线论坛Reddit的档案。这种类型的规范化数据集不存在，它将提供开发和评估新算法所需的培训和测试材料。使用模块化的工作计划，项目团队将首先开发和验证单独的算法，以评估和预测标量表达式的含义，以及Reddit用户社交网络中的碎片化程度。然后，这些组件将使用先进的基于图的机器学习方法进行集成。该项目的主要成果将是一个软件包，它将通过标记显示回声室效应早期阶段的reddits或线程来促进在线版主的工作。规范化的数据集对于改进需要重要语义推理的NLP应用程序（如情感分析、聊天机器人和问答系统）也非常有价值。更一般地说，该项目是一个示范项目，用于处理与社会关系和人类行为有关的语言意义的先进方法。

On-line forums can support the formation of social communities with shared interests and needs. They can also have a negative side if groups of users support each other in divisive attitudes or false beliefs. The social fragmentation resulting from these so-called echo-chamber effects has been identified as an engine behind the rise of violence and extremism, political gridlock, and decreases in social mobility. This project is motivated by the observation that echo-chamber effects involve a gradual shift from more moderate language to more extreme language. Further, damage repair is difficult when extreme social fragmentation has already occurred. The ability to use patterns in on-line language for early detection of on-line social fragmentation would thus be a major breakthrough in supporting earlier, and more effective, intervention against harmful trends in on-line forums.We have identified two major challenges in creating this capability. First, current NLP methods are poor at understanding expressions whose meaning is a degree on a scale, such as a scale defined on the dimensions of cost, quality, honesty, or performance. For example, "rather racist", "really racist", and "incredibly racist" express different degrees of disapproval, but such differences are not adequately captured by current algorithms. This limitation is central to our problem, because echo-chamber effects often involve incremental exaggerations of factual claims, emotions, or attitudes. The second challenge results from the fact that methods for using linguistic content in the analysis of social behaviour are limited. While much research has uncovered systematic associations between word choices and social groups, very little has addressed relationships between linguistic inferences and social trends. However, tracking the gradual shifts towards semantic extremes in echo-chamber effects requires making certain linguistic inferences. This is because inferring which underlying dimension of meaning is relevant in any specific case critically depends on information about who is talking and what they are talking about. For example, "Liverpool is far better" might to relate a scale of cultural excellence in a discussion amongst music fans, but to a scale of costs amongst people who are discussing housing. A fundamental advance in the methodology for combining linguistic and social information is thus needed to characterise echo-chamber effects on-line and make predictions about risks of future fragmentation. The project is a new collaboration between an experimental and computational linguist (the PI) and an expert in machine learning and social network analysis (the Co-I). Its components integrate the expertise of both collaborators. Advanced text-mining and data analytics will be used to generate the materials for a large-scale and experimentally normed data set of scalar expressions, using archives of the popular on-line forum Reddit. No normed data set of this type exists, and it will provide the training and test materials needed to develop and evaluate new algorithms. Using a modular work plan, the project team will first develop and validate separate algorithms to assess and predict the meanings of scalar expressions, and the level of fragmentation in the social network of Reddit users. These components will then be integrated using advanced graph-based machine learning methods. The primary outcome of the project will be a software package that will facilitate the work of on-line moderators by flagging subReddits or threads that display early stages of echo-chamber effects. The normed data set will also be extremely valuable for improving NLP applications that require nontrivial semantic inference, such as sentiment analysis, chatbots, and question-answering systems. More generally, the project is a demonstration project for advanced methodology in processing linguistic meaning in relation to social relationships and human behaviour.