In this paper, we present a study of regret and its expression on social media platforms. Specifically, we present a novel dataset of Reddit texts that have been classified into three classes: Regret by Action, Regret by Inaction, and No Regret. We then use this dataset to investigate the language used to express regret on Reddit and to identify the domains of text that are most commonly associated with regret. Our findings show that Reddit users are most likely to express regret for past actions, particularly in the domain of relationships. We also found that deep learning models using GloVe embedding outperformed other models in all experiments, indicating the effectiveness of GloVe for representing the meaning and context of words in the domain of regret. Overall, our study provides valuable insights into the nature and prevalence of regret on social media, as well as the potential of deep learning and word embeddings for analyzing and understanding emotional language in online text. These findings have implications for the development of natural language processing algorithms and the design of social media platforms that support emotional expression and communication.
Cryptocurrency Bubble Detection: A New Stock Market Dataset, Financial Task & Hyperbolic Models
University of Texas at Dallas（德克萨斯大学达拉斯分校）；Georgia Institute of Technology（佐治亚理工学院）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Universitat Politècnica de València（瓦伦西亚理工大学）
Us vs. Them: A Dataset of Populist Attitudes, News Bias and Emotions
Institute for Logic, Language and Computation, University of Amsterdam（阿姆斯特丹大学逻辑、语言与计算研究所）；Babelscape Srl, Sapienza University of Rome（罗马第一大学Babelscape有限公司）；Department of Psychology, University of Amsterdam（阿姆斯特丹大学心理学系）
政治语篇任务的计算建模已经成为自然语言处理中越来越重要的研究领域。近年来，民粹主义言论在整个政治领域有所上升;然而，由于其复杂性，对其的计算方法一直很稀缺。在本文中，我们提出了新的$\textit{Us vs. Them}$数据集，包括6861条Reddit评论，注释了民粹主义的态度和这种现象的第一个大规模计算模型。我们调查了民粹主义心态和社会群体之间的关系，以及一系列通常与这些相关的情绪。我们设定了一个基线的两个任务相关的民粹主义的态度，并提出了一套多任务的学习模型，利用和展示的重要性，情绪和群体识别作为辅助任务。
