More Like this

1. U.S. parents' intentions to use anti-bullying apps: Insights from a comprehensive model

   https://doi.org/10.1016/j.heliyon.2023.e19630  

   Wheeler, Brittany; Baumel, Katie; Hall, Deborah L.; Silva, Yasin N. (September 2023, Heliyon)

   Cyberbullying has become a prominent risk for youth and an increasing concern for parents. To help parents reduce their child’s cyberbullying risk, anti-bullying apps (ABAs)—mobile applications for identifying and preventing instances of cyberbullying—have been developed in recent years. Given that ABAs are an emerging technology, limited research has been conducted to understand the factors predicting parents’ intentions to use them. Drawing on three interdisciplinary theoretical frameworks, a sample of parents in the U.S. recruited through Amazon Mechanical Turk completed an online survey to assess parents’ knowledge of, attitudes about, and intentions to use ABAs. Participants also rated the importance of a range of ABA functions and provided information about their child’s social media use and bullying history. A series of path analyses revealed that the importance parents placed on an app’s ability to provide information about their child’s cyberbullying risk predicted more positive attitudes toward ABAs and greater perceived usefulness of them. Stronger intentions to use ABAs were predicted by greater cyberbullying concern, greater importance of social recommendations, greater perceived usefulness, more positive attitudes toward the apps, and lower ratings of the importance of ease of use. These findings shed light on the factors predicting parents’ intentions to use ABAs and the app features they view as most important. Crucial directions for future research and implications for antibullying efforts are discussed. 

   more » « less
2. Harnessing the Power of Interdisciplinary Research with Psychology-Informed Cyberbullying Detection Models

   https://doi.org/10.1007/s42380-021-00107-5  

   Hall, D. L.; Silva, Y. N.; Wheeler, B.; Cheng, L.; Baumel, K. (March 2022, International Journal of Bullying Prevention)

   Cyberbullying has become increasingly prevalent, particularly on social media. There has also been a steady rise in cyberbullying research across a range of disciplines. Much of the empirical work from computer science has focused on developing machine learning models for cyberbullying detection. Whereas machine learning cyberbullying detection models can be improved by drawing on psychological theories and perspectives, there is also tremendous potential for machine learning models to contribute to a better understanding of psychological aspects of cyberbullying. In this paper, we discuss how machine learning models can yield novel insights about the nature and defining characteristics of cyberbullying and how machine learning approaches can be applied to help clinicians, families, and communities reduce cyberbullying. Specifically, we discuss the potential for machine learning models to shed light on the repetitive nature of cyberbullying, the imbalance of power between cyberbullies and their victims, and causal mechanisms that give rise to cyberbullying. We orient our discussion on emerging and future research directions, as well as the practical implications of machine learning cyberbullying detection models. 

   more » « less
3. Foregrounding Women's Safety in Mobile Social Matching and Dating Apps: A Participatory Design Study

   https://doi.org/10.1145/3567559  

   Aljasim, Hanan Khalid; Zytko, Douglas (January 2023, Proceedings of the ACM on Human-Computer Interaction)

   The design of social matching and dating apps has changed continually through the years, marked notably by a shift to mobile devices, and yet user safety has not historically been a driver of design despite mounting evidence of sexual and other harms. This paper presents a participatory design study with women-a demographic at disproportionate risk of harm through app-use-about how mobile social matching apps could be designed to foreground their safety. Findings indicate that participants want social matching apps to augment women's abilities for self-protection, reflected in three new app roles: 1) the cloaking device, through which the social matching app helps women dynamically manage visibility to geographically nearby users, 2) the informant, through which the app helps women predict risk of harm associated with a recommended social opportunity, and 3) the guardian, through which the app monitors a user's safety during face-to-face meetings and augments their response to risk. 

   more » « less
4. A Human-Centered Systematic Literature Review of Cyberbullying Detection Algorithms

   https://doi.org/10.1145/3476066  

   Kim, Seunghyun; Razi, Afsaneh; Stringhini, Gianluca; Wisniewski, Pamela J.; De Choudhury, Munmun (October 2021, Proceedings of the ACM on Human-Computer Interaction)

   Cyberbullying is a growing problem across social media platforms, inflicting short and long-lasting effects on victims. To mitigate this problem, research has looked into building automated systems, powered by machine learning, to detect cyberbullying incidents, or the involved actors like victims and perpetrators. In the past, systematic reviews have examined the approaches within this growing body of work, but with a focus on the computational aspects of the technical innovation, feature engineering, or performance optimization, without centering around the roles, beliefs, desires, or expectations of humans. In this paper, we present a human-centered systematic literature review of the past 10 years of research on automated cyberbullying detection. We analyzed 56 papers based on a three-prong human-centeredness algorithm design framework - spanning theoretical, participatory, and speculative design. We found that the past literature fell short of incorporating human-centeredness across multiple aspects, ranging from defining cyberbullying, establishing the ground truth in data annotation, evaluating the performance of the detection models, to speculating the usage and users of the models, including potential harms and negative consequences. Given the sensitivities of the cyberbullying experience and the deep ramifications cyberbullying incidents bear on the involved actors, we discuss takeaways on how incorporating human-centeredness in future research can aid with developing detection systems that are more practical, useful, and tuned to the diverse needs and contexts of the stakeholders. 

   more » « less
5. Stranger Danger!: Social Media App Features Co-designed with Children to Keep Them Safe Online

   https://doi.org/10.1145/3311927.3323133  

   Badillo-Urquiola, Karla; Smriti, Diva; McNally, Brenna; Golub, Evan; Bonsignore, Elizabeth; Wisniewski, Pamela J. (June 2019, Stranger Danger!: Social Media App Features Co-designed with Children to Keep Them Safe Online)

   Mobile social media applications ("apps"), such as TikTok (previously Musical.ly), have recently surfaced in news media due to harmful incidents involving young children engaging with strangers through these mobile apps. To better understand children's awareness of online stranger danger and explore their visions for technologies that can help them manage related online risks (e.g., sexual solicitations and cyberbullying), we held two participatory design sessions with 12 children (ages 8-11 years old). We found that children desired varying levels of agency, depending on the severity of the risk. In most cases, they wanted help resolving the issue themselves instead of relying on their parents to do it for them. Children also believed that social media apps should take on more responsibility in promoting online safety for children. We discuss the children's desires for agency, privacy, and automated intelligent assistance and provide novel design recommendations inspired by children. 

   more » « less