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Digital Library

Breaking the Building Blocks of Hate A Case Study of Minecraft Servers

Topic:

General/Other

Principal Investigators:

Austin Botelho, Rachel Kowert, Ph.D, Alex Newhouse

Study Date: 

2022

Source:

Center for Technology and Society,GamerSafer,Middlebury Institute of International Studies,Take This,Anti-Defamation League (ADL)

Key Findings:

This analysis aims to understand how important online gaming spaces operate, the form that hate takes in these spaces, and whether content moderation can mitigate hate. 


The online game Minecraft, owned by Microsoft, has amassed 141 million active users since it was launched in 2011. It is used in school communities, among friend groups and even has been employed by the U.N. Despite its ubiquity as an online space, little has been reported on how hate and harassment manifest in Minecraft, as well as how it performs content moderation. 


After examining chats and user reports from data provided by GamerSafer, the researchers found: 


-Many in-game offenders are repeat offenders. Almost a fifth of offending users had multiple actions taken against them during the data collection. 

 

-Temporary bans proved to be an effective solution for reprimanding bad behavior. Early evidence shows temporary bans to be more effective than muting in reducing the rate of offending behaviors by the moderated player. 

 

-Servers with in-depth community guidelines were associated with more positive social spaces. Of the three servers reviewed, Server 3 had the the most extensive community guidelines and the lowest frequency of sexually explicit, hateful, and severely toxic behavior between users, suggesting the positive impact of robust guidelines. 

 

-Server rules appear to matter more than moderation enforcement in shaping communication norms. There was no effect of moderation events on rates of server-wide toxic behaviors over time. Nonetheless, rates of toxic behaviors still correlated with server staffing and rules. 

 

-The rates of harmful behavior differed depending on the message type. 

-Hateful messages were 21% more likely for public chats than private ones. 


-Sexually explicit messages were 9% more likely for private chats than public ones. 


-Analysis further suggests hateful rhetoric has been normalized in gaming spaces. The presence of slurs previously only affiliated with white nationalism and hate groups suggests the normalization of extreme language in gaming spaces. 


-Sexually explicit language occurred 3x as often as hateful language. 


Recommendations:

 

-Increasing researcher access to data. Granting researchers and watchdogs with access to data helps to identify and address the challenges of hateful, harassing, and toxic behavior in spaces meant to have a positive social impact. 

 

-Investing in content moderation efforts and robust community guidelines. Active, effective human moderation and community guidelines are critical to reducing sexually explicit, hateful, and severely toxic behavior in gaming spaces as the sever with the most staff and most extensive guidelines had the fewest incidents of these kinds of behaviors. 

 

-Additional research into content moderation and complementary tools and techniques, including: 

 

(1) the short and long term benefits of content moderation efforts. Moderator intervention seems to reduce behavior in the short term, but it remains unclear as to whether this holds over time. Future work should focus on determining the long-term effects of moderator intervention. 

 

(2) the impact of tools and techniques that enable player accountability and go beyond moderation, such as player verification and globally blocklisting players banned for severe harms. These measures can add responsibility and minimize reoffending.

Methodology:

Take This, ADL and the Middlebury Institute of International Studies, in collaboration with GamerSafer, analyzed hate and harassment in Minecraft based on anonymized data from January 1st to March 30th, 2022 consensually provided from three private Minecraft servers (no other data was gathered from the servers except the anonymized chat and report logs used in this study).

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