• Corpus ID: 10634337

Analyzing the Targets of Hate in Online Social Media

  title={Analyzing the Targets of Hate in Online Social Media},
  author={Leandro Ara{\'u}jo Silva and Mainack Mondal and Denzil Correa and Fabr{\'i}cio Benevenuto and Ingmar Weber},
Social media systems allow Internet users a congenial platform to freely express their thoughts and opinions. [] Key Method To do that, we gather traces from two social media systems: Whisper and Twitter. We then develop and validate a methodology to identify hate speech on both these systems. Our results identify online hate speech forms and offer a broader understanding of the phenomenon, providing directions for prevention and detection approaches.

Tables from this paper

A Measurement Study of Hate Speech in Social Media
The results identify hate speech forms and unveil a set of important patterns, providing not only a broader understanding of online hate speech, but also offering directions for detection and prevention approaches.
An Analysis of Hate Speech among Armenian Facebook Users
  • L. Bekaryan
  • Computer Science
    FLEKS - Scandinavian Journal of Intercultural Theory and Practice
  • 2021
Analysis of hate speech samples extracted from popular Armenian Facebook pages shows that hate speech can find its explicit and implicit reflection in the online communication of Armenian Facebook users, and can be characterised by contextual markers such as invisibility, incitement to violence, invectiveness and immediacy.
Peer to Peer Hate: Hate Speech Instigators and Their Targets
It is found that hate instigators target more popular and high profile Twitter users, and that participating in hate speech can result in greater online visibility, which advance the state of the art of understanding online hate speech engagement.
Hate speech on social media networks: towards a regulatory framework?
This paper argues that social networks existence, as facilitated by the Code of Conduct, serves as a light at the end of the Internet hate tunnel where issues of multiple jurisdictions as well as technological realities have resulted in the task of online regulation being more than a daunting one.
An Empirical Study of Offensive Language in Online Interactions
A neural transformer approach for detecting the tokens that make a particular post aggressive, and a new multi-task aggression detection (MAD) framework for post and token-level aggression detection using neural transformers are proposed.
Crowdsourcing of Hate Speech for Detecting Abusive Behavior on Social Media
This paper aims at targeting the tweets shared on twitter on the basis of emotion behind them-whether it falls under the category of hate speech or not, and applies the hate speech check on anti-national tweets.
Detecting the Hate Code on Social Media
A step forward from classifying tweets by allowing us to study the usage pattern of these concentrated set of users, which involves substituting references to communities by benign words that seem out of context, in hate filled posts or Tweets.
Hate Lingo: A Target-based Linguistic Analysis of Hate Speech in Social Media
This work provides a data-driven analysis of the nuances of online-hate speech that enables not only a deepened understanding of hate speech and its social implications but also its detection.
Interaction dynamics between hate and counter users on Twitter
The interaction dynamics of the hate and counter users could pave a more effective way for combating hate content on Twitter than just suspending the hate accounts.
Online hate speech: A survey on personal experiences and exposure among adult New Zealanders
Online hate speech has been a topic of public concern and research interest for some time. Initially the focus of this centred on the proliferation of online groups and websites promoting and


Anti-social media
To inform the discussion over free speech and hate speech, this study examines the way racial, religious and ethnic slurs are employed on Twitter. Executive summary: How to define the limits of free
#Hashtagging hate: Using Twitter to track racism online
This paper considers three different projects that have used Twitter to track racist language in Canada, and highlights why Twitter is an important data collection tool for researchers interested in studying race and racism.
Whispers in the dark: analysis of an anonymous social network
This paper presents results of the first large-scale empirical study of an anonymous social network, using a complete 3-month trace of the Whisper network covering 24 million whispers written by more than 1 million unique users, and analyzes Whisper from a number of perspectives.
The Many Shades of Anonymity: Characterizing Anonymous Social Media Content
The notion of anonymity sensitivity of a social media post is introduced, which captures the extent to which users think the post should be anonymous and a human annotator based methodology is proposed to measure the same for Whisper and Twitter posts.
Locate the Hate: Detecting Tweets against Blacks
A supervised machine learning approach is applied, employing inexpensively acquired labeled data from diverse Twitter accounts to learn a binary classifier for the labels “racist” and “nonracist", which has a 76% average accuracy on individual tweets, suggesting that with further improvements, this work can contribute data on the sources of anti-black hate speech.
Cyber Hate Speech on Twitter: An Application of Machine Classification and Statistical Modeling for Policy and Decision Making
It is demonstrated how the results of the classifier can be robustly utilized in a statistical model used to forecast the likely spread of cyber hate in a sample of Twitter data.
A Lexicon-based Approach for Hate Speech Detection
The goal of the research is to create a model classifier that uses sentiment analysis techniques and in particular subjectivity detection to not only detect that a given sentence is subjective but also to identify and rate the polarity of sentiment expressions.
Detecting Hate Speech on the World Wide Web
The definition of hate speech, the collection and annotation of the hate speech corpus, and a mechanism for detecting some commonly used methods of evading common "dirty word" filters are described.
Hate Speech Detection with Comment Embeddings
This work proposes to learn distributed low-dimensional representations of comments using recently proposed neural language models, that can then be fed as inputs to a classification algorithm, resulting in highly efficient and effective hate speech detectors.
Countering online Hate Speech. UNESCO
  • Countering online Hate Speech. UNESCO
  • 2015