Culture, Identity, and Inclusion

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    Understanding toxicity in multiplayer online games: The roles of national culture and demographic variables
    ( 2022-01-04) Kordyaka, Bastian ; Krath, Jeanine ; Park, Solip ; Wesseloh, Henrik ; Laato, Samuli
    Toxic behavior (TB) is a negative response to in-game frustration in multiplayer online games (MOG) that can ruin the playing experience, causing financial damage to MOG operators. Understanding the drivers of TB is an important step to curb the behavior. In this work, we consult the model of national culture (MNC) as well as demographic variables (e.g., education, gender, and age) as antecedent variables of TB using an exploratory design. We surveyed players of League of Legends and Dota 2 with two samples, based on the MNC, from North America (n=155) and India (n=119). We observed significant cultural differences in TB, with higher levels of self-reported toxicity in the Indian sample. In both samples, consistent with previous findings, age was negatively associated with TB. However surprisingly, there was a statistically significant difference among the two groups in terms of the relationship between education and TB.
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    Understanding cyberslacking intention during Covid-19 online classes: An fsQCA analysis
    ( 2022-01-04) Mumu, Jinnatul Raihan ; Connolly, Regina ; Wanke, Peter ; Azad, Md. Abul Kalam
    Due to the Covid-19 pandemic, students have been restricted to their homes and forced to take online classes as an alternative for in-person classes. However, data shows that this enforced online education is resulting in a learning deficit for a generation of students and that many engage in cyberslacking behavior, which is the use of non-work-related Internet use during designated work time. Cyberslacking tendency among university students in particular is on the rise. A fuzzy-set qualitative comparative analysis (fsQCA) was applied. Sentiment analysis was subsequently performed using Natural Language Processing. Findings from the fsQCA analysis identified five core factors that underpin cyberslacking attention. Alternative paths have been identified based on gender and the students’ current education status. The study findings contain a number of contributions, illustrating different topologies of student intention towards cyberslacking and identifying causal factors that influence cyberslacking intention, as well as illustrating student sentiment regarding this behavior.
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    The Impact of Culture on Online Toxic Disinhibition: Trolling in India and the USA
    ( 2022-01-04) Fichman, Pnina ; Rathi, Maanvi
    The pervasiveness of online trolling has been attributed to the effect of online toxic disinhibition, suggesting that perpetrators behave in less socially desirable ways online than they do offline. It is possible that this disinhibition effect allows for everyone to start on a level playing field online, regardless of race, gender, or nationality, but it is likewise possible that the disinhibition effect is context-dependent and sensitive to socio-cultural variations. We aim to explore if toxic online disinhibition effects depend on national culture and gender by examining the extent of trolling towards tweets by Americans and Indians, from both genders. Content analysis of 3,000 Twitter posts reveals that significantly more trolling comments were posted on tweets by Americans than by Indians, and on tweets by women than men. We conclude that the online disinhibition effect may exacerbate, replicate, or mediate existing socio-cultural differences, but it does not eliminate them.
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    Intersectional Identities and Machine Learning: Illuminating Language Biases in Twitter Algorithms
    ( 2022-01-04) Fitzsimons, Aidan
    Intersectional analysis of social media data is rare. Social media data is ripe for identity and intersectionality analysis with wide accessibility and easy to parse text data yet provides a host of its own methodological challenges regarding the identification of identities. We aggregate Twitter data that was annotated by crowdsourcing for tags of “abusive,” “hateful,” or “spam” language. Using natural language prediction models, we predict the tweeter’s race and gender and investigate whether these tags for abuse, hate, and spam have a meaningful relationship with the gendered and racialized language predictions. Are certain gender and race groups more likely to be predicted if a tweet is labeled as abusive, hateful, or spam? The findings suggest that certain racial and intersectional groups are more likely to be associated with non-normal language identification. Language consistent with white identity is most likely to be considered within the norm and non-white racial groups are more often linked to hateful, abusive, or spam language.
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    Examining Cultural and Technological Change: A Study of Cultural Affordances on WeChat
    ( 2022-01-04) Sun, Yinan ; Suthers, Dan
    This study presents qualitative research of the interaction among WeChat, users, and Chinese culture by applying a three-dimensional notion of cultural affordances. The data is collected by conducting 10 semi-structured in-depth interviews, follow-up discussions, and observations, and is analyzed via the grounded theory approach. The study provides preliminary findings that demonstrate the roles of WeChat, users, and Chinese culture in terms of cultural and technological changes. The study also discusses the interactive patterns among three dimensions of cultural affordances and offers possible direction for further study.