Trollslayer: Crowdsourcing and Characterization of Abusive Birds in Twitter

As of today, abuse is a pressing issue to participants and administrators of Online Social Networks (OSN). Abuse in Twitter can spawn from arguments generated for influencing outcomes of a political election, the use of bots to automatically spread misinformation, and generally speaking, activities that deny, disrupt, degrade or deceive other participants and, or the network. Given the difficulty in finding and accessing a large enough sample of abuse ground truth from the Twitter platform, we built and deployed a custom crawler that we use to judiciously collect a new dataset from the Twitter platform with the aim of characterizing the nature of abusive users, a.k.a abusive birds, in the wild. We provide a comprehensive set of features based on users' attributes, as well as social-graph metadata. The former includes metadata about the account itself, while the latter is computed from the social graph among the sender and the receiver of each message. Attribute-based features are useful to characterize user's accounts in OSN, while graph-based features can reveal the dynamics of information dissemination across the network. In particular, we derive the Jaccard index as a key feature to reveal the benign or malicious nature of directed messages in Twitter. To the best of our knowledge, we are the first to propose such a similarity metric to characterize abuse in Twitter.Comment: SNAMS 201

Securing Federated Sensitive Topic Classification against Poisoning Attacks

We present a Federated Learning (FL) based solution for building a distributed classifier capable of detecting URLs containing GDPR-sensitive content related to categories such as health, sexual preference, political beliefs, etc. Although such a classifier addresses the limitations of previous offline/centralised classifiers,it is still vulnerable to poisoning attacks from malicious users that may attempt to reduce the accuracy for benign users by disseminating faulty model updates. To guard against this, we develop a robust aggregation scheme based on subjective logic and residual-based attack detection. Employing a combination of theoretical analysis, trace-driven simulation, as well as experimental validation with a prototype and real users, we show that our classifier can detect sensitive content with high accuracy, learn new labels fast, and remain robust in view of poisoning attacks from malicious users, as well as imperfect input from non-malicious ones

A PIMS Development Kit for New Personal Data Platforms

The web ecosystem is based on a market where stakeholders collect and sell personal data, but nowadays users expect stronger guarantees of transparency and privacy. With the PIMCity personal information management system (PIMS) development kit, we provide an open-source development kit for building PIMSs to foster the development of open and user-centric data markets

Spindle cell squamous carcinoma of the tongue in a child

AbstractBackgroundSpindle cell carcinoma (SpCC) is an infrequent and aggressive type of squamous cell carcinoma (SCC) characterized by the proliferation of epithelial and mesenchymal components. Oral SCC in children is an extremely rare entity and the SpCC variant has been reported in one case in the paediatric patient literature.MethodsIn this paper, we report a case of SpCC of the tongue in an 11-year-old boy treated by on-block surgical resection and microvascular tissue reconstruction.ResultsAfter 14 months the patient is free of disease with easily intelligible speech and normal swallowing.ConclusionsDiagnosis and treatment of this rare tumour in this age group is a challenge because of the overlapping of histopathological features and the complex reconstruction required to achieve adequate aesthetic and functional results