Add to ORKGWe have developed a system that automatically detects online jihadist hate speech with over 80% accuracy, by using techniques from Natural Language Processing and Machine Learning. The system is trained on a corpus of 45,000 subversive Twitter messages collected from October 2014 to December 2016. We present a qualitative and quantitative analysis of the jihadist rhetoric in the corpus, examine the network of Twitter users, outline the technical procedure used to train the system, and discuss examples of use.status: Published onlin
Reissued 30 May 2017 with Second Reader’s non-NPS affiliation added to title page.Online propaganda ...
University of Minnesota M.S. thesis. May 2020. Major: Computer Science. Advisor: Richard Maclin. 1 c...
As research on hate speech becomes more and more relevant every day, most of it is still focused on ...
In recent years, the main medium for communication and dissemination of information amongst internet...
Islamophobic hate speech on social media is a growing concern in contemporary Western politics and s...
The objective of this dissertation is to explore the use of machine learning algorithms in understan...
Jihadist groups like ISIS are spreading online propaganda using various forms of social media such a...
Abstract With the objective of facilitating and reducing analysis tasks undergone by law enforcement...
This paper presents a survey on hate speech detection. Given the steadily growing body of social med...
White supremacist hate speech is one of the most recently observed harmful content on social media. ...
The pervasiveness of the Internet and social media have enabled the rapid and anonymous spread of Ha...
Hate speech has been an ongoing problem on the Internet for many years. Besides, social media, espec...
Hateful and abusive speech presents a major challenge for all online social media platforms. Recent ...
Social media posts containing hate speech are reproduced and redistributed at an accelerated pace, r...
Abstract This paper suggests a new approach for radicalization detection using natural language pro...
Reissued 30 May 2017 with Second Reader’s non-NPS affiliation added to title page.Online propaganda ...
University of Minnesota M.S. thesis. May 2020. Major: Computer Science. Advisor: Richard Maclin. 1 c...
As research on hate speech becomes more and more relevant every day, most of it is still focused on ...
In recent years, the main medium for communication and dissemination of information amongst internet...
Islamophobic hate speech on social media is a growing concern in contemporary Western politics and s...
The objective of this dissertation is to explore the use of machine learning algorithms in understan...
Jihadist groups like ISIS are spreading online propaganda using various forms of social media such a...
Abstract With the objective of facilitating and reducing analysis tasks undergone by law enforcement...
This paper presents a survey on hate speech detection. Given the steadily growing body of social med...
White supremacist hate speech is one of the most recently observed harmful content on social media. ...
The pervasiveness of the Internet and social media have enabled the rapid and anonymous spread of Ha...
Hate speech has been an ongoing problem on the Internet for many years. Besides, social media, espec...
Hateful and abusive speech presents a major challenge for all online social media platforms. Recent ...
Social media posts containing hate speech are reproduced and redistributed at an accelerated pace, r...
Abstract This paper suggests a new approach for radicalization detection using natural language pro...
Reissued 30 May 2017 with Second Reader’s non-NPS affiliation added to title page.Online propaganda ...
University of Minnesota M.S. thesis. May 2020. Major: Computer Science. Advisor: Richard Maclin. 1 c...
As research on hate speech becomes more and more relevant every day, most of it is still focused on ...