Alas, coordinated hate attacks, or raids, are becoming increasingly common online. In a nutshell, these are perpetrated by a group of aggressors who organize and coordinate operations on a platform (e.g., 4chan) to target victims on another community (e.g., YouTube). In this paper, we focus on attributing raids to their source community, paving the way for moderation approaches that take the context (and potentially the motivation) of an attack into consideration. We present TUBERAIDER, an attribution system achieving over 75% accuracy in detecting and attributing coordinated hate attacks on YouTube videos. We instantiate it using links to YouTube videos shared on 4chan's /pol/ board, r/The_Donald, and 16 Incels-related subreddits. We use a peak detector to identify a rise in the comment activity of a YouTube video, which signals that an attack may be occurring. We then train a machine learning classifier based on the community language (i.e., TF-IDF scores of relevant keywords) to perform the attribution. We test TUBERAIDER in the wild and present a few case studies of actual aggression attacks identified by it to showcase its effectiveness.

This paper presents a privacy-enhancing identity management platform designed to address the challenges associated with online identity verification and privacy protection. INCOGNITO offers a comprehensive solution by leveraging concepts such as Qualified Anonymity and cryptographic credentials, along with technologies including blockchain, Tor Network, and software stacks like Idemix. By employing these mechanisms, INCOGNITO aims to enable users to securely acquire and manage their identity attributes, while preserving their privacy and ensuring compliance with both regulatory bodies and Service Providers’ requirements. The platform facilitates the issuance and verification of cryptographic credentials, granting users access to online services based on fine-grained subsets of their identity attributes. Furthermore, the effectiveness and feasibility of the platform are demonstrated through two pilot projects focused on online multimedia content sharing and identifying bots or fake users in online social networks. These pilots showcase the practical applicability of INCOGNITO in solving identity-related challenges while safeguarding user privacy and security.

YouTube is by far the largest host of user-generated video content worldwide. Alas, the platform has also come under fire for hosting inappropriate, toxic, and hateful content. One community that has often been linked to sharing and publishing hateful and misogynistic content are the Involuntary Celibates (Incels), a loosely defined movement ostensibly focusing on men's issues. In this paper, we set out to analyze the Incel community on YouTube by focusing on this community's evolution over the last decade and understanding whether YouTube's recommendation algorithm steers users towards Incel-related videos. We collect videos shared on Incel communities within Reddit and perform a data-driven characterization of the content posted on YouTube. Among other things, we find that the Incel community on YouTube is getting traction and that, during the last decade, the number of Incel-related videos and comments rose substantially. We also find that users have a 6.3% chance of being suggested an Incel-related video by YouTube's recommendation algorithm within five hops when starting from a non Incel-related video. Overall, our findings paint an alarming picture of online radicalization: not only Incel activity is increasing over time, but platforms may also play an active role in steering users towards such extreme content.

Attention networks constitute the state-of-the-art paradigm for capturing long temporal dynamics. This paper examines the efficacy of this paradigm in the challenging task of emotion recognition in dyadic conversations. In this work, we introduce a novel attention mechanism capable of inferring the immensity of the effect of each past utterance on the current speaker emotional state. The proposed self-attention network captures the correlation patterns among consecutive encoder network states, thus enabling the robust and effective modeling of temporal dynamics over arbitrary long temporal horizons. We exhibit the effectiveness of our approach considering the challenging IEMOCAP benchmark. We show that, our devised methodology outperforms state-of-the-art alternatives and commonly used approaches, giving rise to promising new research directions in the context of Online Social Network (OSN) analysis tasks.

Current authentication methods on the Web have serious weaknesses. First, services heavily rely on the traditional password paradigm, which diminishes the end-users' security and usability. Second, the lack of attribute-based authentication does not allow anonymity-preserving access to services. Third, users have multiple online accounts that often reflect distinct identity aspects. This makes proving combinations of identity attributes hard on the users. In this paper, we address these weaknesses by proposing a privacy-preserving architecture for device-centric and attribute-based authentication based on: 1) the seamless integration between usable/strong device-centric authentication methods and federated login solutions; 2) the separation of the concerns for Authorization, Authentication, Behavioral Authentication and Identification to facilitate incremental deployability, wide adoption and compliance with NIST assurance levels; and 3) a novel centralized component that allows end-users to perform identity profile and consent management, to prove combinations of fragmented identity aspects, and to perform account recovery in case of device loss. To the best of our knowledge, this is the first effort towards fusing the aforementioned techniques under an integrated architecture. This architecture effectively deems the password paradigm obsolete with minimal modification on the service provider's software stack.

Mobile devices, which users habitually carry along, have become the main data gateway for the majority of the online services. Any device is able to collect at any time various types of data through its sensors. At the same time, modern identification techniques ask users to send photos of their ID documentation in order to be verified by an online service. Those photos are captured by the device's camera and are considered extremely sensitive. They must be secured and establish that they will not be modified. This paper describes a security framework that preserves the authenticity of a captured photo and ensures that it remains intact while transferred to a remote server. The key inside is to use a background service that is tied to the photo-capturing application and uses secure key storing and cryptographic computation capabilities offered by the Trusted Execution Environment (TEE) of commodity Android devices. At the same time, we leverage Playstore's Licencing Verification Library (LVL) to remotely attest the authenticity of the photo-capturing application at registration time. We have implemented our framework as an Android application on a Nexus 5X, which is powered by a Qualcomm processor with ARM TrustZone Technology. The evaluation of our prototype implementation demonstrates the efficacy of the proposed framework in terms of performance overhead and usability.