Countering hate on social media: Large scale classification of hate and counter speech
Santa Fe Institute（圣塔菲研究所）；Center for the Study of Complex Systems（复杂系统研究中心）；University of Michigan（密歇根大学）；Vermont Complex Systems Center（佛蒙特复杂系统中心）；University of Vermont（佛蒙特大学）
Hateful rhetoric is plaguing online discourse, fostering extreme societal movements and possibly giving rise to real-world violence. A potential solution to this growing global problem is citizen-generated counter speech where citizens actively engage in hate-filled conversations to attempt to restore civil non-polarized discourse. However, its actual effectiveness in curbing the spread of hatred is unknown and hard to quantify. One major obstacle to researching this question is a lack of large labeled data sets for training automated classifiers to identify counter speech. Here we made use of a unique situation in Germany where self-labeling groups engaged in organized online hate and counter speech. We used an ensemble learning algorithm which pairs a variety of paragraph embeddings with regularized logistic regression functions to classify both hate and counter speech in a corpus of millions of relevant tweets from these two groups. Our pipeline achieved macro F1 scores on out of sample balanced test sets ranging from 0.76 to 0.97---accuracy in line and even exceeding the state of the art. On thousands of tweets, we used crowdsourcing to verify that the judgments made by the classifier are in close alignment with human judgment. We then used the classifier to discover hate and counter speech in more than 135,000 fully-resolved Twitter conversations occurring from 2013 to 2018 and study their frequency and interaction. Altogether, our results highlight the potential of automated methods to evaluate the impact of coordinated counter speech in stabilizing conversations on social media.
Assessing How Hate, Counterspeech, and Toxicity Affect Hate Group Newcomers
School of Information, University of California, Berkeley（加州大学伯克利分校信息学院）；Department of Anthropology, University of California, Los Angeles（加州大学洛杉矶分校人类学系）；Bedari Kindness Institute, University of California, Los Angeles（加州大学洛杉矶分校贝达里仁慈研究所）；Center for Behavior, Evolution, & Culture, University of California（加州大学行为、进化与文化中心）；USC Information Sciences Institute（南加州大学信息科学研究所）；Department of Cognitive and Information Sciences, University of California, Merced（加州大学默塞德分校认知与信息科学学院）；Santa Fe Institute（圣塔菲研究所）；Complexity Science Hub（复杂性科学中心）；Luddy School of Informatics, Computing and Engineering, Indiana University（印第安纳大学拉迪信息、计算与工程学院）；School of Data Science, University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校数据科学学院）
Online Hate: Behavioural Dynamics and Relationship with Misinformation
Ca’ Foscari University of Venice（威尼斯大学）；Jozef Stefan Institute（约瑟夫·斯特凡研究所）；Jozef Stefan International Postgraduate School（约瑟夫·斯特凡国际研究生院）；Sapienza University of Rome（罗马第一大学）
Deep Learning Approaches for Detecting Adversarial Cyberbullying and Hate Speech in Social Networks
School of Information Technology University of Cincinnati Cincinnati, Ohio, USA；School of Human Services University of Cincinnati Cincinnati, Ohio, USA
