Over the past few years, online aggression and abusive behaviors have occurred in many different forms and on a variety of platforms. In extreme cases, these incidents have evolved into hate, discrimination, and bullying, and even materialized into real-world threats and attacks against individuals or groups. In this paper, we study the Gamergate controversy. Started in August 2014 in the online gaming world, it quickly spread across various social networking platforms, ultimately leading to many incidents of cyberbullying and cyberaggression. We focus on Twitter, presenting a measurement study of a dataset of 340k unique users and 1.6M tweets to study the properties of these users, the content they post, and how they differ from random Twitter users. We find that users involved in this "Twitter war" tend to have more friends and followers, are generally more engaged and post tweets with negative sentiment, less joy, and more hate than random users. We also perform preliminary measurements on how the Twitter suspension mechanism deals with such abusive behaviors. While we focus on Gamergate, our methodology to collect and analyze tweets related to aggressive and bullying activities is of independent interest.
Detecting abusive behavior. @cite_28 aim to detect offensive content and potential offensive users by analyzing YouTube comments. Then, @cite_17 @cite_27 turn to cyberbullying on Instagram and Ask.fm. Specifically, in @cite_27 , besides considering available text information, they also try to associate the topic of an image (e.g., drugs, celebrity, sports, etc.) to possible cyberbullying events, concluding that drugs are highly associated with cyberbullying. Also, in a effort to create a suitable dataset for their analysis, at first the authors collected a large number of media sessions -- i.e., videos and images along with comments -- from Instagram public profiles, with a subset selected for labeling. To ensure that an adequate number of cyberbullying instances will be present in the dataset, they selected media sessions with at least one profanity word. Finally, they relied on the CrowdFlower crowdsourcing platform to determine whether or not such sessions are related with cyberbullying or cyberaggression. @cite_17 authors leveraged both likes and comments to identify negative behavior in the Ask.fm social network. Here, their dataset was created by exploiting publicly accessible profiles, e.g. questions, answers, and likes.
@cite_28 @cite_27 @cite_17
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Since the textual contents on online social media are highly unstructured, informal, and often misspelled, existing research on message-level offensive language detection cannot accurately detect offensive content. Meanwhile, user-level offensiveness detection seems a more feasible approach but it is an under researched area. To bridge this gap, we propose the Lexical Syntactic Feature (LSF) architecture to detect offensive content and identify potential offensive users in social media. We distinguish the contribution of pejoratives profanities and obscenities in determining offensive content, and introduce hand-authoring syntactic rules in identifying name-calling harassments. In particular, we incorporate a user's writing style, structure and specific cyber bullying content as features to predict the user's potentiality to send out offensive content. Results from experiments showed that our LSF framework performed significantly better than existing methods in offensive content detection. It achieves precision of 98.24 and recall of 94.34 in sentence offensive detection, as well as precision of 77.9 and recall of 77.8 in user offensive detection. Meanwhile, the processing speed of LSF is approximately 10msec per sentence, suggesting the potential for effective deployment in social media. Cyberbullying is a growing problem affecting more than half of all American teens. The main goal of this paper is to study labeled cyberbullying incidents in the Instagram social network. In this work, we have collected a sample data set consisting of Instagram images and their associated comments. We then designed a labeling study and employed human contributors at the crowd-sourced CrowdFlower website to label these media sessions for cyberbullying. A detailed analysis of the labeled data is then presented, including a study of relationships between cyberbullying and a host of features such as cyberaggression, profanity, social graph features, temporal commenting behavior, linguistic content, and image content. Cyberbullying has emerged as an important and growing social problem, wherein people use online social networks and mobile phones to bully victims with offensive text, images, audio and video on a 247 basis. This paper studies negative user behavior in the Ask.fm social network, a popular new site that has led to many cases of cyberbullying, some leading to suicidal behavior.We examine the occurrence of negative words in Ask.fms question+answer profiles along with the social network of likes of questions+answers. We also examine properties of users with cutting behavior in this social network.