Online social networks (OSN) are a permanent presence in today's personal and
professional lives of a huge segment of the population, with direct consequences to offline …

Federated learning over distributed multi-party data is an emerging paradigm that iteratively
aggregates updates from a group of devices to train a globally shared model. Relying on a …

Machine learning (ML) over distributed multi-party data is required for a variety of domains.
Existing approaches, such as federated learning, collect the outputs computed by a group of …

Rating platforms enable large-scale collection of user opinion about items (eg, products or
other users). However, untrustworthy users give fraudulent ratings for excessive monetary …

Recent studies in social media spam and automation provide anecdotal argumentation of
the rise of a new generation of spambots, so-called social spambots. Here, for the first time …

Current approaches to characterize and detect hate speech focus on content posted in
Online Social Networks (OSNs). They face shortcomings to get the full picture of hate speech …

Malicious crowdsourcing forums are gaining traction as sources of spreading misinformation
online, but are limited by the costs of hiring and managing human workers. In this paper, we …

Twitter is a web application playing dual roles of online social networking and micro-
blogging. The popularity and open structure of Twitter have attracted a large number of …

Microblogs are increasingly exploited for predicting prices and traded volumes of stocks in
financial markets. However, it has been demonstrated that much of the content shared in …

Federated Learning is the current state of the art in supporting secure multi-party machine
learning (ML): data is maintained on the owner's device and the updates to the model are …