CSI: A Hybrid Deep Model for Fake News Detection

The topic of fake news has drawn attention both from the public and the academic communities. Such misinformation has the potential of affecting public opinion, providing an opportunity for malicious parties to manipulate the outcomes of public events such as elections. Because such high stakes are at play, automatically detecting fake news is an important, yet challenging problem that is not yet well understood. Nevertheless, there are three generally agreed upon characteristics of fake news: the text of an article, the user response it receives, and the source users promoting it. Existing work has largely focused on tailoring solutions to one particular characteristic which has limited their success and generality. In this work, we propose a model that combines all three characteristics for a more accurate and automated prediction. Specifically, we incorporate the behavior of both parties, users and articles, and the group behavior of users who propagate fake news. Motivated by the three characteristics, we propose a model called CSI which is composed of three modules: Capture, Score, and Integrate. The first module is based on the response and text; it uses a Recurrent Neural Network to capture the temporal pattern of user activity on a given article. The second module learns the source characteristic based on the behavior of users, and the two are integrated with the third module to classify an article as fake or not. Experimental analysis on real-world data demonstrates that CSI achieves higher accuracy than existing models, and extracts meaningful latent representations of both users and articles.Comment: In Proceedings of the 26th ACM International Conference on Information and Knowledge Management (CIKM) 201

VIRHUS: uma plataforma computacional para a simulação de sinais fisiológicos de humanos virtuais

The ability to access bio-signals of participants for research activity is limited specially for informal settings like academic projects. These limitations can be in part overcome it by using software to simulated good enough physiological data. In this work we propose and develop a computational platform to simulate (biological signals of ) virtual humans as a service. The system adopts the concept of digital twin to structure the simulation processes. In this case, the system is not sensing real participants, rather uses pre-recorded signals as inputs to auto-encoders that generate realistic synthetic signal for a virtual human, i.e., a digital twin. The pre-recorded signals used were the electrocardiogram, electrodermal activity and electromyography signals which were labeled with the ongoing emotion. The system, VIRHUS, offers an interactive web environment to create the required virtual humans and manage the simulation processes. A scalable backend takes care of the asynchronous generation of signals, that can be streamed to endpoints (and consumed by external applications) or exported as files, for convenience. As a proof of concept, the “virtual human” data can be parameterized to include emotional traits in the bio-signals (happy, sad,...), generating meaningful variations in data for applications developers.A capacidade de aceder aos biossinais de participantes para actividades de investigação é limitada, especialmente em contextos informais, tais como projectos académicos. Estas limitações podem ser parcialmente ultrapassadas através da utilização de software para simular dados fisiológicos suficientemente fidedignos. Neste trabalho, propomos e desenvolvemos uma plataforma computacional para simular (sinais biológicos de ) seres humanos virtuais como um serviço. O sistema adopta o conceito de réplica digital ("digital twin") para estruturar os processos de simulação. Neste caso, o sistema não está a monitorar participantes reais, mas utiliza sinais pré-gravados como entradas para autocodificadores que geram um sinal sintético realista para um ser humano virtual, ou seja, uma réplica digital. Os sinais pré-gravados utilizados foram o electrocardiograma, a actividade electrodérmica e os sinais electromiográficos que foram marcados com a emoção em progresso. O sistema, VIRHUS, fornece um ambiente web interactivo para criar os seres humanos virtuais necessários e gerir os processos de simulação. Um backend escalável cuida da geração assíncrona de sinais, que podem ser transmitidos para pontos de acesso programático (e consumidos por aplicações externas) ou exportados como ficheiros por conveniência. Como prova de conceito, os dados "humanos virtuais" podem ser parametrizados para incluir traços emocionais nos biossinais (feliz, triste,...), gerando variações significativas nos dados para os programadores de aplicações.Mestrado em Engenharia Informátic