The Ugliness of Trolls: Comparing the Methodologies of the Alt-Right and the Ku Klux Klan

Eckstrand, Nathan

doi:10.5130/ccs.v10.i3.6026

Cited by 5 publications

(6 citation statements)

References 3 publications

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“…If it is isolation and social disenfranchisement that make these young men easy targets for recruitment The alt-right is a digital form and logical extension of groups that have existed for much longer than the Internet, most notably, the Ku Klux Klan (the KKK). The KKK was founded after the civil war, originally with the claim to provide excitement and adventure for former soldiers who were no longer needed for service (Eckstrand, 2018). Compared to other movements, such as George Linclon Rockwell's attempt to make Nazism mainstream in the United States, the KKK has survived for over 100 years, and has been readily accepted in many communities.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Compared to other movements, such as George Linclon Rockwell's attempt to make Nazism mainstream in the United States, the KKK has survived for over 100 years, and has been readily accepted in many communities. The acceptance of the KKK is well documented across communities, law enforcement and the media across it's active eras (Eckstrand, 2018). While some white supremacists maintain that the historic popularity of the KKK stems from a cultural need for a white ethnostate, there is an argument that much of their success was due to violence and intimidation, even intimidating other white Americans to join their ranks (Evans, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

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Some Things Never Change: An Interactive Guide to Online White Supremacy

Boisjoli

2023

Preprint

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<p>White supremacy and its values are not new, but the ways they recruit are ever-changing. These communities have found places to thrive online and, shrouded in anonymity, have developed new and sinister ways of attracting new members to their ideology. While it is certainly an uncomfortable topic, it is important to understand how these groups work, recruit and communicate with one another in order to understand how to quell their activity and influence. Through a combination of leaked Discord server chats, personal indoctrination stories and academic studies, this interactive fiction project will seek to make the tactics employed by white supremacists clear and easily understood, by allowing the player to experience the deterioration of relationships encouraged in white supremacist groups, and engage with a breadth of research in-game.</p>

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Some Things Never Change: An Interactive Guide to Online White Supremacy

Boisjoli

2023

Preprint

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“…Following Crandall and Eshelman's Justification-Suppression model of prejudice, Day [13] finds racism on Twitter flourishes when the offender is motivated by a group of like-minded racist peers, when prejudice against targets can be socially justified, and when capable guardians are not present to suppress their activities. It has been suggested that community ties also operate within far-right online communities, motivating and deepening individuals' commitment to extremist politics [5,15].…”

Section: The Social Dynamics Of Online Hatementioning

confidence: 99%

Tackling racial bias in automated online hate detection: Towards fair and accurate detection of hateful users with geometric deep learning

2022

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Online hate is a growing concern on many social media platforms, making them unwelcoming and unsafe. To combat this, technology companies are increasingly developing techniques to automatically identify and sanction hateful users. However, accurate detection of such users remains a challenge due to the contextual nature of speech, whose meaning depends on the social setting in which it is used. This contextual nature of speech has also led to minoritized users, especially African–Americans, to be unfairly detected as ‘hateful’ by the very algorithms designed to protect them. To resolve this problem of inaccurate and unfair hate detection, research has focused on developing machine learning (ML) systems that better understand textual context. Incorporating social networks of hateful users has not received as much attention, despite social science research suggesting it provides rich contextual information. We present a system for more accurately and fairly detecting hateful users by incorporating social network information through geometric deep learning. Geometric deep learning is a ML technique that dynamically learns information-rich network representations. We make two main contributions: first, we demonstrate that adding network information with geometric deep learning produces a more accurate classifier compared with other techniques that either exclude network information entirely or incorporate it through manual feature engineering. Our best performing model achieves an AUC score of 90.8% on a previously released hateful user dataset. Second, we show that such information also leads to fairer outcomes: using the ‘predictive equality’ fairness criteria, we compare the false positive rates of our geometric learning algorithm to other ML techniques and find that our best-performing classifier has no false positives among a subset of African–American users. A neural network without network information has the largest number of false positives at 26, while a neural network incorporating manual network features has 13 false positives among African–American users. The system we present highlights the importance of effectively incorporating social network features in automated hateful user detection, raising new opportunities to improve how online hate is tackled.

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“…Following Crandall and Eshelman's Justification-Suppression model of prejudice, Day [10] finds racism on Twitter flourishes when the offender is socially motivated by a group of like-minded racists, when prejudice against targets is easy to socially justify, and when capable guardians are not present to suppress their activities. It has been suggested that similar dynamics operate amongst hateful far-right online communities [12].…”

Section: Related Work 21 the Social Dynamics Of Online Hatementioning

confidence: 99%

Tackling Racial Bias in Automated Online Hate Detection: Towards Fair and Accurate Classification of Hateful Online Users Using Geometric Deep Learning

Ahmed¹,

Vidgen²,

Hale³

2021

Preprint

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Online hate is a growing concern on many social media platforms and other sites. To combat it, technology companies are increasingly identifying and sanctioning 'hateful users' rather than simply moderating hateful content. Yet, most research in online hate detection to date has focused on hateful content. This paper examines how fairer and more accurate hateful user detection systems can be developed by incorporating social network information through geometric deep learning. Geometric deep learning dynamically learns information-rich network representations and can generalise to unseen nodes. This is essential for moving beyond manually engineered network features, which lack scalability and produce information-sparse network representations. This paper compares the accuracy of geometric deep learning with other techniques which either exclude network information or incorporate it through manual feature engineering (e.g., node2vec). It also evaluates the fairness of these techniques using the 'predictive equality' criteria, comparing the false positive rates on a subset of 136 African-American users with 4836 other users. Geometric deep learning produces the most accurate and fairest classifier, with an AUC score of 90.8% on the entire dataset and a false positive rate of zero among the African-American subset for the best performing model. This highlights the benefits of more effectively incorporating social network features in automated hateful user detection. Such an approach is also easily operationalized for real-world content moderation as it has an efficient and scalable design.

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The Ugliness of Trolls: Comparing the Methodologies of the Alt-Right and the Ku Klux Klan

Cited by 5 publications

References 3 publications

Some Things Never Change: An Interactive Guide to Online White Supremacy

Some Things Never Change: An Interactive Guide to Online White Supremacy

Tackling racial bias in automated online hate detection: Towards fair and accurate detection of hateful users with geometric deep learning

Tackling Racial Bias in Automated Online Hate Detection: Towards Fair and Accurate Classification of Hateful Online Users Using Geometric Deep Learning

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