Related papers: Content-Agnostic Moderation for Stance-Neutral Rec…
In this paper, we assess existing technical proposals for content moderation in End-to-End Encryption (E2EE) services. First, we explain the various tools in the content moderation toolbox, how they are used, and the different phases of the…
Hate speech remains a persistent and unresolved challenge in online platforms. Content moderators, working on the front lines to review user-generated content and shield viewers from hate speech, often find themselves unprotected from the…
To meet the demands of content moderation, online platforms have resorted to automated systems. Newer forms of real-time engagement($\textit{e.g.}$, users commenting on live streams) on platforms like Twitch exert additional pressures on…
The expansion of the attention economy has led to the growing issue of inappropriate content being posted by profit-driven users. Previous countermeasures against inappropriate content have relied on moderation, which raises ethical…
We investigate the dynamics of opinion formation on social networking platforms, focusing on how individual opinions, influenced by both social connections and platform algorithms, evolve. We model this process using a differential…
The spread of toxic content on online platforms presents complex challenges that call for both theoretical insight and practical tools to test intervention strategies. In this novel research paper, we introduce a simulation-based framework…
User interests are usually dynamic in the real world, which poses both theoretical and practical challenges for learning accurate preferences from rich behavior data. Among existing user behavior modeling solutions, attention networks are…
There is a rapidly-growing research interest in engaging users with multi-modal data for accurate user modeling on recommender systems. Existing multimedia recommenders have achieved substantial improvements by incorporating various…
There is an ongoing debate about how to moderate toxic speech on social media and the impact of content moderation on online discourse. This paper proposes and validates a methodology for measuring the content-moderation-induced distortions…
Social-media platforms have created new ways for citizens to stay informed and participate in public debates. However, to enable a healthy environment for information sharing, social deliberation, and opinion formation, citizens need to be…
Social media platforms have become an integral part of everyday life, serving as a primary source of news and information for many users. These platforms increasingly rely on personalised recommendation systems that shape what users see and…
Despite the valuable social interactions that online media promote, these systems provide space for speech that would be potentially detrimental to different groups of people. The moderation of content imposed by many social media has…
Popular messaging applications now enable end-to-end-encryption (E2EE) by default, and E2EE data storage is becoming common. These important advances for security and privacy create new content moderation challenges for online services,…
This paper addresses the problem of designing recommendation systems for social networks and e-commerce platforms from a control-theoretic perspective. We treat the design of recommendation systems as a state-feedback infinite-horizon…
Unlike the more observable phenomenon of group opinion reinforcement, self-censorship online has received comparatively less attention. Our goal in this work is to dissect the phenomena of self-censorship and to examine the implications of…
Content moderation is a global challenge, yet major tech platforms prioritize high-resource languages, leaving low-resource languages with scarce native moderators. Since effective moderation depends on understanding contextual cues, this…
Large language models (LLMs) are powerful tools for content moderation, but their inference costs and latency make them prohibitive for casual use on large datasets, such as the Google Ads repository. This study proposes a method for…
With a vast number of items, web-pages, and news to choose from, online services and the customers both benefit tremendously from personalized recommender systems. Such systems however provide great opportunities for targeted…
Recommendation and ranking systems are known to suffer from popularity bias; the tendency of the algorithm to favor a few popular items while under-representing the majority of other items. Prior research has examined various approaches for…
Recommendation systems capable of providing diverse sets of results are a focus of increasing importance, with motivations ranging from fairness to novelty and other aspects of optimizing user experience. One form of diversity of recent…