Fake News Detection (AILAB-Udine – MIT Boston)
In the last few years, we have assisted at the explosion of news sharing and commenting in social networks. While this practice has positive aspects, as it stimulates the debate, it has been polluted by the diffusion of unreliable news, generally referred to as Fake News. Since these contents are often produced with malicious intents and they have a tremendous real-world political and social impact, the Natural Language Processing (NLP) community has been called to propose algorithms for their identification. Most of the currently existing works are so far based on stylistic and linguistic peculiarity of the Fake News texts (such as excessive use emphasis and hyperbolic expressions). As time passes, however, the Fake News tend to be stylistically and linguistically more similar to Real News, so that Fact Checking remains as the only reliable approach to isolate them. In this project, we employ Artificial Intelligence to assess the reliability of news on the base of not only intrinsic criteria […]
We welcome Saida
Saida has joined us at the AILAB in the last days. She is a PhD student from Egypt. Her interests are in Machine Learning and Deep Learning, and in Fuzzy Logic too. We wish Saida to have a good time in our group and to achieve great goals in her work.
A new member joins AILAB
We are very pleased to welcome a new member of the AILAB. Giuseppe Lancioni joined us as a Ph.D. student whose main research focus will cover the Data Analysis and Natural Language Processing fields. Everyone in the AILAB team is looking forward to work with him and see him achieve great goals!
The remaining useful life (RUL) estimation of a component is an interesting problem within the Prognostics and Health Management (PHM) field, which consists in estimating the number of time steps occurring between the current time step and the end of the component life. Being able to reliably estimate this value can lead to an improvement of the maintenance scheduling and a reduction of the costs associated with it. Data driven approaches are often used in the literature and they are the preferred choice over model-based approaches: in fact, not only they are easier to build, but the data over which they are built can be gathered easily in many industrial applications. During the last years, Long-Short Term Memory (LSTM) networks have found many applications in this area because they are suitable to deal with time-series data and because they inherently learn how to remember long-term dependencies, which can be really useful to uncover hidden patterns within the sensor data. Here […]