Dimensions of Abusive Language on Twitter

In this paper, we use a new categorical form of multidimensional register analysis to identify the main dimensions of functional linguistic variation in a corpus of abusive language, consisting of racist and sexist Tweets. By analysing the use of a wide variety of parts-of-speech and grammatical constructions, as well as various features related to Twitter and computer-mediated communication, we discover three dimensions of linguistic variation in this corpus, which we interpret as being related to the degree of interactive, antagonistic and attitudinal language exhibited by individual Tweets. We then demonstrate that there is a significant functional difference between racist and sexist Tweets, with sexists Tweets tending to be more interactive and attitudinal than racist Tweets

Hate is not Binary: Studying Abusive Behavior of #GamerGate on Twitter

Over the past few years, online bullying and aggression have become increasingly prominent, and manifested in many different forms on social media. However, there is little work analyzing the characteristics of abusive users and what distinguishes them from typical social media users. In this paper, we start addressing this gap by analyzing tweets containing a great large amount of abusiveness. We focus on a Twitter dataset revolving around the Gamergate controversy, which led to many incidents of cyberbullying and cyberaggression on various gaming and social media platforms. We study the properties of the users tweeting about Gamergate, the content they post, and the differences in their behavior compared to typical Twitter users. We find that while their tweets are often seemingly about aggressive and hateful subjects, "Gamergaters" do not exhibit common expressions of online anger, and in fact primarily differ from typical users in that their tweets are less joyful. They are also more engaged than typical Twitter users, which is an indication as to how and why this controversy is still ongoing. Surprisingly, we find that Gamergaters are less likely to be suspended by Twitter, thus we analyze their properties to identify differences from typical users and what may have led to their suspension. We perform an unsupervised machine learning analysis to detect clusters of users who, though currently active, could be considered for suspension since they exhibit similar behaviors with suspended users. Finally, we confirm the usefulness of our analyzed features by emulating the Twitter suspension mechanism with a supervised learning method, achieving very good precision and recall.Comment: In 28th ACM Conference on Hypertext and Social Media (ACM HyperText 2017

Comparative Studies of Detecting Abusive Language on Twitter

The context-dependent nature of online aggression makes annotating large collections of data extremely difficult. Previously studied datasets in abusive language detection have been insufficient in size to efficiently train deep learning models. Recently, Hate and Abusive Speech on Twitter, a dataset much greater in size and reliability, has been released. However, this dataset has not been comprehensively studied to its potential. In this paper, we conduct the first comparative study of various learning models on Hate and Abusive Speech on Twitter, and discuss the possibility of using additional features and context data for improvements. Experimental results show that bidirectional GRU networks trained on word-level features, with Latent Topic Clustering modules, is the most accurate model scoring 0.805 F1.Comment: ALW2: 2nd Workshop on Abusive Language Online to be held at EMNLP 2018 (Brussels, Belgium), October 31st, 201

Researching and enhancing athlete welfare: Proceedings of the Second International Symposium of the Brunel International Research Network for Athlete Welfare (BIRNAW) 2013

Copyright @ 2014 Brunel University. All rights reserved by the authors who assert their rights under the Berne Convention. Copyright rests with Brunel University London. All research designs, concepts, models and theories herein are the intellectual property of the contributing authors. No part of this document may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise without the prior written permission of Dr Daniel Rhind via Brunel University London.The chapters within this book are based on presentations delivered at the 2nd BIRNAW Symposium which was held at Brunel University London in November 2013.Sport is a cultural phenomenon that touches the lives and captures the imagination of many people. Most people assume that sport is “a good thing” and that participation in sport will bring physical, psychological and social benefits to participants and societies. However, as this body of work shows, this is not necessarily or always the case. Abuse and exploitation can and does occur in sport – a fact that sports enthusiasts and sports organisations have been slow to acknowledge. The Brunel International Research Network for Athlete Welfare (BIRNAW) is a remarkable initiative that brings together researchers and policy makers from a variety of disciplines, organisations and countries. The activities and publications of this group have successfully provided an evidence base that has drawn attention to the issues in a powerful and convincing way. Its impact on the world of sport has been significant and is an excellent example of research informing sport policy and improving the practice of sport. Through the work of those involved in BIRNAW, inspired by the vision of Celia Brackenridge and her colleagues at Brunel University London, awareness has been raised, and safeguarding measures are being put in place to ensure the welfare of athletes. There is still much to be done, but the world of sport, and those athletes whose welfare is now safeguarded, already have much to thank them for