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

نویسندگان

  • Despoina Chatzakou
  • Nicolas Kourtellis
  • Jeremy Blackburn
  • Emiliano De Cristofaro
  • Gianluca Stringhini
  • Athena Vakali
چکیده

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.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Using Convolutional Neural Networks to Classify Hate-Speech

The paper introduces a deep learningbased Twitter hate-speech text classification system. The classifier assigns each tweet to one of four predefined categories: racism, sexism, both (racism and sexism) and non-hate-speech. Four Convolutional Neural Network models were trained on resp. character 4-grams, word vectors based on semantic information built using word2vec, randomly generated word ve...

متن کامل

A Unified Deep Learning Architecture for Abuse Detection

Hate speech, offensive language, sexism, racism and other types of abusive behavior have become a common phenomenon in many online social media platforms. In recent years, such diverse abusive behaviors have been manifesting with increased frequency and levels of intensity. This is due to the openness and willingness of popular media platforms, such as Twitter and Facebook, to host content of s...

متن کامل

بررسی میزان شیوعِ‎ ‎انواع زورگویی در مدارس راهنمایی شهرستان یزد از دیدگاه دبیران

This study examined the prevalence of bullying in schools from the perspective of the teachers. In this Research 416 ‎teachers were employed in secondary sector (200 men and 216 women) selected to a cluster random sampling method ‎and participated in the survey. For measuring different types of bullying a 15-Items questionnaire was made. Data ‎were analyzed using descriptive statistics and Chi ...

متن کامل

Don’t Talk Dirty to Me: How Sexist Beliefs Affect Experience in Sexist Games

Research on sexism in digital games has suggested that women self-select out of playing sexist games; however, assuming a homogenous gender-based response does not account for the diversity of identities within a gender group. Gender-incongruent responses to recent events like #gamergate implies that the gender of the participant is not paramount to experience, but that their beliefs about gend...

متن کامل

Are You a Racist or Am I Seeing Things? Annotator Influence on Hate Speech Detection on Twitter

Hate speech in the form of racism and sexism is commonplace on the internet (Waseem and Hovy, 2016). For this reason, there has been both an academic and an industry interest in detection of hate speech. The volume of data to be reviewed for creating data sets encourages a use of crowd sourcing for the annotation efforts. In this paper, we provide an examination of the influence of annotator kn...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2017