Background
This study will focus on radicalization and polarization within gaming contexts and unmoderated all-chats in online games (e.g., pre-game lobby of Starcraft 2). As online gaming communities continue to grow, they have become significant social platforms where individuals interact, form identities, and engage in discussions. These virtual environments can inadvertently foster radicalization and polarization, posing substantial risks to social cohesion and individual well-being.
Radicalization within gaming contexts often occurs through exposure to extremist ideologies, hate speech, and toxic behaviors that can be amplified by the anonymity and lack of accountability in online interactions. Polarization, on the other hand, is exacerbated by echo chambers and algorithmic biases that reinforce divisive viewpoints, leading to a fragmented community. Understanding these dynamics is crucial for developing strategies to mitigate harmful behaviors and promote positive social interactions.
Moreover, the immersive nature of gaming and the strong emotional investments of players can make these environments particularly susceptible to manipulation by extremist groups seeking to recruit and radicalize individuals. Unmoderated, potentially global, chatrooms, where players from diverse backgrounds communicate in real-time, can become hotspots for the spread of misinformation, propaganda, and inflammatory rhetoric.
Studying radicalization and polarization in these contexts provides valuable insights into the mechanisms of online radicalization and the factors that contribute to the polarization of opinions. This knowledge is essential for policymakers, game developers, and community managers to create safer and more inclusive gaming environments. By implementing effective moderation tools, educational initiatives, and community guidelines, it is possible to counteract the negative effects of radicalization and polarization, fostering a more positive and cohesive online gaming culture.
Type of Research
Experimental research. Mixed Methods. Analysis of large text-based dataset.
Combining it with an internship is recommended but not necessary.
Interested?
Please contact Steven Watson (s.j.watson@utwente.nl) when you are interested in this assignment.
Literature
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