The rise of online communication platforms has been accompanied by some undesirable effects, such as the proliferation of aggressive and abusive behaviour online. Aiming to tackle this problem, the natural language processing (NLP) community has experimented with a range of techniques for abuse detection. While achieving substantial success, these methods have so far only focused on modelling the linguistic properties of the comments and the online communities of users, disregarding the emotional state of the users and how this might affect their language. The latter is, however, inextricably linked to abusive behaviour. In this paper, we present the first joint model of emotion and abusive language detection, experimenting in a multi-task learning framework that allows one task to inform the other. Our results demonstrate that incorporating affective features leads to significant improvements in abuse detection performance across datasets.
Longitudinal Abuse and Sentiment Analysis of Hollywood Movie Dialogues using Language Models
Transitional Artificial Intelligence Research Group, School of Mathematics and Statistics, UNSW Sydney（过渡人工智能研究组，数学与统计学学院）；Centre for Artificial Intelligence and Innovation, Pingla Institute, Sydney, Australia（人工智能与创新中心，Pingla研究所）
EmoPerso: Enhancing Personality Detection with Self-Supervised Emotion-Aware Modelling
School of Electronics and Computer Science, University of Southampton（电子工程与计算机科学学院，南安普顿大学）；School of Electronic Engineering and Computer Science, Queen Mary University of London（电子工程与计算机科学学院，伦敦女王学院）；Department of Computational Biology, Mohamed bin Zayed University of Artificial Intelligence（计算生物学系，Mohamed bin Zayed人工智能大学）
Refashioning Emotion Recognition Modelling: The Advent of Generalised Large Models
College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机科学与电子工程学院）；Department of Computer Science and Technology, University of Cambridge（剑桥大学计算机科学与技术系）；GLAM – the Group on Language, Audio, & Music, Imperial College London（帝国理工学院语言、音频与音乐研究组（GLAM））；Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg（奥格斯堡大学医疗与福祉嵌入式智能教授席（该机构按要求提取到大学级别））
Language Independent Emotion Quantification using Non linear Modelling of Speech
Sir C.V. Raman Centre for Physics and Music, Jadavpur University（贾达普大学C.V.拉曼物理与音乐中心）；Department of Humanities and Social Sciences, IIT Kharagpur（印度理工学院卡哈拉格普尔分校人文与社会科学系）；Shrutinandan School of Music, Kolkata（加尔各答Shrutinandan音乐学院）；Department of Physics, Jadavpur University（贾达普大学物理系）；Department of Electrical Engineering, Jadavpur University（贾达普大学电气工程系）；School of Languages and Linguistics, Jadavpur University（贾达普大学语言与语言学学院）
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。
