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DISSERTATION 84 Eprint EXPERTS AND MACHINES UNITED AGAINST CYBERBULLYING Maral Dadvar PhD dissertation committee: Chairman and Secretary: Prof. dr. P. M. G. Apers University of Twente Promotor Prof. dr. F. M. G. de Jong University of Twente Members: Prof. dr. D. J. Pepler York University Prof. dr. V. Hoste Ghent University Prof. dr. ir. U. Kaymak Eindhoven University of Technology Prof. dr. T. W. C. Huibers University of Twente Dr. C. H. C. Drossaert University of Twente CTIT Ph.D. Thesis Series No. 14-323 Centre for Telematics and Information Technology P.O. Box 217, 7500 AE, Enschede, The Netherlands. SIKS Dissertation Series No. 2014-37 The research reported in this thesis has been carried out under the auspices of SIKS, the Dutch Research School for Information and Knowledge Systems. This work was part of the PuppyIR project, which is supported by a grant of the 7th Framework ICT program (FP7-ICT-2007-3) of the European Union. ISBN: 978-90-365-3739-1 ISSN: 1381-3617.14-323 DOI: 10.3990/1.9789036537391 Copyright ©2014, Maral Dadvar, Enschede, the Netherlands EXPERTS AND MACHINES UNITED AGAINST CYBERBULLYING DISSERTATION to obtain the degree of doctor at the University of Twente, on the authority of the Rector Magnificus, prof. dr. H. Brinksma, on account of the decision of the graduation committee, to be publicly defended on Friday 12 September 2014 at 14:45 hrs. by Maral Dadvar born on 19 September 1981 in Tehran, Iran. This dissertation is approved by: Prof. dr. F. M. G. de Jong (promotor) Cover Photo: Courtesy of Maryam Zandi ©2007 Cover Design: Benno Masselink Printed by: ITC Printing Department All rights reserved. No part of this book may be reproduced or transmitted, in any form or by any means, electronic or mechanical, including photocopying, microfilming, and recording, or by any information storage or retrieval system, without the prior written permission of the author. Summary One form of online misbehaviour which has deeply affected society with harmful consequences is known as cyberbullying. Cyberbullying can simply be defined as an intentional act that is conducted through digital technology to hurt someone. Cyberbullying is a widely covered topic in the social sciences. There are many studies in which the problem of cyberbullying has been introduced and its origins and consequences have been explored in detail. There are also studies which have investigated the intervention and prevention strategies and have proposed guidelines for parents and adults in this regard. However, studies on the technical dimensions of this topic are relatively rare. In this research the overall goal was to bridge the gap between social science approaches and technical solutions. In order to be able to suggest solutions that could contribute to minimizing the risk and impact of cyberbullying we have investigated the phenomenon of cyberbullying from different angles. We have thoroughly studied the origin of cyberbullying and its growth over time, as well as the role of technology in the emergence of this type of virtual behaviour and in the potential for reducing the extent of the social concern it raises. First we introduced a novel outlook towards the cyberbullying phenomenon. We looked into the gradual changes which have occurred in relationships and social communication with the emergence of the Internet. We argued that one should look at virtual environments as virtual communities, because the human needs projected on these environments, the relationships, human concerns and misbehaviour have the same nature as in real-life societies. Therefore, to make virtual communities safe, we need to take safety measures and precautions that are similar to the ones that are common in non-virtual communities. We derived the assumption that if cyberbullying is recognized and treated as a social problem and not just seen as some random mischief conducted by individuals with the use of technology, the methods for handling its consequences are likely to be i more realistic, effective and comprehensive. This part of our study led to the conviction that for combating cyberbullying, behavioural and psychological studies, and the study of technical solutions should go hand in hand. One of the main limitations that we faced when we started our research was the lack of a comprehensive dataset for cyberbullying studies. We needed a dataset which included real instances of bullying incidents. Moreover, it was essential for our studies to also have the demographic information of the social media users as well as the history of their activities. We started our preliminary experiments using a dataset that was collected from MySpace forums. This dataset did not meet all the requirements for our experiment, namely in terms of size and sufficiency of information. Therefore we developed our own YouTube dataset, with the aim to encompass extensive information about the users and their activities as well as larger numbers of bullying comments. We collected information on user activities and posted textual comments as well as personal and demographic details of the users involved. Detecting a bullying comment or post at the earliest possible moment in time can substantially decrease the negative effects of cyberbullying incidents. We started our experiments by showing that besides the conventional features used for text mining methods such as sentiment analysis and specifically bullying detection, more personal features, in this experiment gender, can improve the accuracy of the detection models. As expected the models which were optimized accordingly resulted in a more accurate classification. The improved outcome motivated us to look into other personal features as well, such as age and the writing style of users. By adding more personal information, the previous classification results were outperformed and the detection accuracy enhanced even further. In the last experiment we made use of experts’ knowledge to identify potential bully users in social networks. To better understand and interpret ii the intentions underlying the online activities of users of social media, we decided to incorporate human reasoning and knowledge into a bulliness rating system by developing a Multi-Criteria Evaluation System. Moreover, to have more sources of information and to make use of the potential of both human and machine, we designed a hybrid approach, incorporating machine learning models on top of the expert system. The hybrid approach reached an optimum model which outperformed the results obtained from the machine learning models and the expert system individually. Our hybrid model illustrates the added value of integrating technical solutions with insights from the social sciences for the first time. As argued in this thesis, the integration of social studies into a software- enhanced monitoring workflow could pave the way towards the tackling of this kind of online misbehaviour. The ideas and algorithms proposed for fulfilling this purpose can be a stepping stone for future research in this direction. The work carried out is also a demonstration of the added value of frameworks for text categorization, sentiment mining and user profiling in applications addressing societal issues. This work can be viewed as a contribution to the more general societal challenge of increasing the level of cybersecurity, in particular for the younger generations of social network users. By turning the internet into a safer place for children, the chances increase that they will be able to benefit from the informational richness that it also offers. iii iv Acknowledgements I have finally reached the end of the road that I started four years and couple of months ago. I knew it would be hard and would need a lot of effort but now I know that it needed encouragement and support most of all. Accomplishment of my PhD was only made possible by the advice and help of my colleagues and by the love and cheer of my family and friends. I would like to thank my supervisor Franciska de Jong who gave me the opportunity to pursue this PhD. She offered her advice whenever needed and coped with my stubbornness at so many points. Without saying much she could always recognize my weaknesses and problems and solve them by just saying what I needed to hear. Claudia Hauff deserves a big thanks for being a patient daily supervisor in the first year of my PhD when everything was Greek to me. She introduced me to the world of IR and she was a good friend and colleague since then. Most of all I should thank her for warning me about the tens of rounds of comments that I was going to receive from Franciska on my thesis. It really helped me to know it’s not just me! I started a new phase of my research with Roeland Ordelman and he made me to become a stronger person at work. Throughout my PhD, specifically last 2 years, Dolf Trieschnigg’s help, support and comments played an important role in finishing my thesis. His critical view on my work and our tough discussions improved my work enormously and made me prepared for the rest to come. I would like to also thank Jimmy Huang who gave me the chance to spend three fruitful and wonderful months at his group at York University in Canada. I learnt a lot and I met many nice people. During this time I also had the opportunity to meet professor Pepler, professor Connolly and professor Mishna. Their input was of great importance to my work. I would like to also extend my sincere gratitude to professor Hoste, dr v Gutteling and dr Drossaert who were members of my expert panel and allowed me to benefit from their knowledge and experience. Randy, thanks for being such a nice office mate and friend and keeping up with my mood swings, taking care of my plants (as I would say OUR plants) and trying your best to acquaint me with Dutch culture :D Our coffee breaks was made even more fun with Andrea stopping by, explaining why life is so hard, talking about his secret connections in town and at some point trying to scare me and leave.
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