Detecting Aggressors and Bullies on Twitter

Online social networks constitute an integral part of people's every day social activity and the existence of aggressive and bullying phenomena in such spaces is inevitable. In this work, we analyze user behavior on Twitter in an effort to detect cyberbullies and cuber-aggressors by considering specific attributes of their online activity using machine learning classifiers

Mean birds: Detecting aggression and bullying on Twitter

In recent years, bullying and aggression against social media users have grown significantly, causing serious consequences to victims of all demographics. Nowadays, cyberbullying affects more than half of young social media users worldwide, suffering from prolonged and/or coordinated digital harassment. Also, tools and technologies geared to understand and mitigate it are scarce and mostly ineffective. In this paper, we present a principled and scalable approach to detect bullying and aggressive behavior on Twitter. We propose a robust methodology for extracting text, user, and network-based attributes, studying the properties of bullies and aggressors, and what features distinguish them from regular users. We find that bullies post less, participate in fewer online communities, and are less popular than normal users. Aggressors are relatively popular and tend to include more negativity in their posts. We evaluate our methodology using a corpus of 1.6M tweets posted over 3 months, and show that machine learning classification algorithms can accurately detect users exhibiting bullying and aggressive behavior, with over 90% AUC

Measuring #GamerGate: A Tale of Hate, Sexism, and Bullying

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 cyberbullying and cyberaggression in social media

Cyberbullying and cyberaggression are increasingly worrisome phenomena affecting people across all demographics. More than half of young social media users worldwide have been exposed to such prolonged and/or coordinated digital harassment. Victims can experience a wide range of emotions, with negative consequences such as embarrassment, depression, isolation from other community members, which embed the risk to lead to even more critical consequences, such as suicide attempts. In this work, we take the first concrete steps to understand the characteristics of abusive behavior in Twitter, one of today’s largest social media platforms. We analyze 1.2 million users and 2.1 million tweets, comparing users participating in discussions around seemingly normal topics like the NBA, to those more likely to be hate-related, such as the Gamergate controversy, or the gender pay inequality at the BBC station. We also explore specific manifestations of abusive behavior, i.e., cyberbullying and cyberaggression, in one of the hate-related communities (Gamergate). We present a robust methodology to distinguish bullies and aggressors from normal Twitter users by considering text, user, and network-based attributes. Using various state-of-the-art machine-learning algorithms, we classify these accounts with over 90% accuracy and AUC. Finally, we discuss the current status of Twitter user accounts marked as abusive by our methodology and study the performance of potential mechanisms that can be used by Twitter to suspend users in the future

Inclusion at Scale: Deploying a Community-Driven Moderation Intervention on Twitch

Harassment, especially of marginalized individuals, on networked gaming and social media platforms has been identified as a significant issue, yet few HCI practitioners have attempted to create interventions tackling toxicity online. Aligning ourselves with the growing cohort of design activists, we present a case study of the GLHF pledge, an interactive public awareness campaign promoting positivity in video game live streaming. We discuss the design and deployment of a community-driven moderation intervention for GLHF, intended to empower the inclusive communities emerging on Twitch. After offering a preliminary report on the effects we have observed based on the more than 370,000 gamers who have participated to date, the paper concludes with a reflection on the challenges and opportunities of using design activism to positively intervene in large-scale media platforms

Application-Oriented Approach for Detecting Cyberaggression in Social Media

International audienceThe paper discuses and demonstrates the use of named-entity recognition for automatic hate speech detection. Our approach also addresses the design of models to map storylines and social anchors. They provide valuable background information for the analysis and correct classification of the brief statements used in social media. Furthermore, named-entity recognition can help to tackle the specifics of the language style often used in hate tweets, a style that differs from regular language in deliberate and unintentional misspellings, strange abbreviations and interpunctuations, and the use of symbols. We implemented a prototype for our approach that automatically analyzes tweets along storylines. It operates on a series of bags of words containing names of persons, locations, characteristic words for insults, threats, and phenomena reflected in social anchors. We demonstrate our approach using a collection of German tweets that address the vitally discussed topic "refugees" in Germany