Results of the summarizing process for the arXiv paper: 2210.13770v1

In their paper titled "The Virality of Hate Speech on Social Media," authors Abdurahman Maarouf, Nicolas Pröllochs, and Stefan Feuerriegel explore the phenomenon of hate speech going viral on social media platforms. They highlight that online hate speech poses a significant threat to vulnerable groups and society as a whole, citing examples such as the Pittsburgh synagogue shooting in 2018. To address the limited understanding of what factors contribute to the virality of hate speech, the authors collected a dataset consisting of 25,219 Twitter cascades with 65,946 retweets and classified them as either hateful or normal content. They then employed generalized linear regression techniques to estimate differences in the spread of hateful versus normal content based on various author and content variables. Through their analysis, the authors identified important determinants that explain variations in the spreading of hateful versus normal content. Notably, they found that hateful content authored by verified users is disproportionately more likely to go viral compared to non-verified users. Specifically, hateful content from verified users exhibited a cascade size 3.5 times larger than normal content, a cascade lifetime 3.2 times longer than normal content, and a structural virality 1.2 times larger than normal content. Overall, this study provides novel insights into the virality of hate speech on social media platforms by shedding light on the factors that contribute to its spread. It also contributes to our understanding of how online hate speech can have real-world consequences for vulnerable groups and society at large.

• Hate speech on social media poses a significant threat to vulnerable groups and society as a whole
• Examples of real-world consequences include the Pittsburgh synagogue shooting in 2018
• The authors collected a dataset of Twitter cascades and classified them as hateful or normal content
• Hateful content authored by verified users is more likely to go viral compared to non-verified users
• Verified users' hateful content had larger cascade size, longer cascade lifetime, and greater structural virality compared to normal content
• This study provides insights into the factors contributing to the spread of hate speech on social media platforms

Summary: Hate speech on social media is a big problem that can hurt people and society. Sometimes, it can even lead to violent events like the shooting at a synagogue. The authors of a study looked at tweets and found some were mean and others were normal. They discovered that famous people who say mean things are more likely to get lots of attention online. This study helps us understand why hate speech spreads on social media. Definitions- Hate speech: Mean and hurtful words or messages that target certain groups of people. - Vulnerable groups: People who are easily hurt or affected by mean words, like children or minorities. - Social media: Websites and apps where people can share pictures, videos, and messages with others. - Dataset: A collection of information or data used for studying something. - Verified users: People on social media who have proven their identity as real celebrities or important figures. - Viral: When something becomes very popular and gets shared by many people quickly. - Structural virality: How much a post spreads through different networks or connections on social media platforms.

The Virality of Hate Speech on Social Media

In recent years, social media platforms have become increasingly popular as a way for people to communicate with each other and share their thoughts and opinions. Unfortunately, this has also led to an increase in the spread of hate speech online. In their paper titled "The Virality of Hate Speech on Social Media," authors Abdurahman Maarouf, Nicolas Pröllochs, and Stefan Feuerriegel explore the phenomenon of hate speech going viral on social media platforms. They highlight that online hate speech poses a significant threat to vulnerable groups and society as a whole, citing examples such as the Pittsburgh synagogue shooting in 2018.

Understanding the Factors Contributing to Viral Hate Speech

To address the limited understanding of what factors contribute to the virality of hate speech, the authors collected a dataset consisting of 25,219 Twitter cascades with 65,946 retweets and classified them as either hateful or normal content. They then employed generalized linear regression techniques to estimate differences in the spread of hateful versus normal content based on various author and content variables. Through their analysis, they identified important determinants that explain variations in the spreading of hateful versus normal content.

Notable Findings

Notably, they found that hateful content authored by verified users is disproportionately more likely to go viral compared to non-verified users. Specifically, hateful content from verified users exhibited a cascade size 3.5 times larger than normal content, a cascade lifetime 3.2 times longer than normal content, and a structural virality 1.2 times larger than normal content. This finding suggests that even though only 0.1% percent of Twitter accounts are verified users (according to data from 2019), these accounts can still have an outsized influence when it comes to spreading hate speech online due to their increased reach and visibility compared with non-verified accounts..

Conclusion

Overall, this study provides novel insights into the virality of hate speech on social media platforms by shedding light on the factors that contribute to its spread. It also contributes to our understanding of how online hate speech can have real-world consequences for vulnerable groups and society at large – something which should not be taken lightly given its potential implications for public safety and security worldwide