Most social media platforms implement content moderation to address interpersonal harms such as harassment. Content moderation relies on offender-centered, punitive approaches, e.g., bans and content removal. We consider an alternative justice framework, restorative justice, which aids victims in healing, supports offenders in repairing the harm, and engages community members in addressing the harm collectively. To assess the utility of restorative justice in addressing online harm, we interviewed 23 users from Overwatch gaming communities, including moderators, victims, and offenders; such communities are particularly susceptible to harm, with nearly three quarters of all online game players suffering from some form of online abuse. We study how the communities currently handle harm cases through the lens of restorative justice and examine their attitudes toward implementing restorative justice processes. Our analysis reveals that cultural, technical, and resource-related obstacles hinder implementation of restorative justice within the existing punitive framework despite online community needs and existing structures to support it. We discuss how current content moderation systems can embed restorative justice goals and practices and overcome these challenges.
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NoCDN: scalable content delivery without a middleman
Today's websites achieve scalability by either deploying their own platforms with sufficient spare capacity or signing up for services from a content delivery network (CDN). This paper investigates another alternative, where a website directly recruits Internet users to contribute their resources to help deliver the site's content. We show that this alternative, which we call NoCDN, can be implemented securely, transparently to the users accessing the site, and without changes to the content itself.
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- Award ID(s):
- 1647145
- NSF-PAR ID:
- 10054246
- Date Published:
- Journal Name:
- 5th ACM/IEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb '17)
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract As intelligent textbooks become more ubiquitous in classrooms and educational settings, the need to make them more interactive arises. An alternative is to ask students to generate knowledge in response to textbook content and provide feedback about the produced knowledge. This study develops Natural Language Processing models to automatically provide feedback to students about the quality of summaries written at the end of intelligent textbook sections. The study builds on the work of Botarleanu et al. (2022), who used a Longformer Large Language Model (LLM) to develop a summary grading model. Their model explained around 55% of holistic summary score variance as assigned by human raters. This study uses a principal component analysis to distill summary scores from an analytic rubric into two principal components – content and wording. This study uses two encoder-only classification large language models finetuned from Longformer on the summaries and the source texts using these principal components explained 82% and 70% of the score variance for content and wording, respectively. On a dataset of summaries collected on the crowd-sourcing site Prolific, the content model was shown to be robust although the accuracy of the wording model was reduced compared to the training set. The developed models are freely available on HuggingFace and will allow formative feedback to users of intelligent textbooks to assess reading comprehension through summarization in real time. The models can also be used for other summarization applications in learning systems.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3132465.3132476
