Deep Learning Models for Passability Detection of Flooded Roads

In this paper we study and compare several approaches to detect floods and evidence for passability of roads by conventional means in Twitter. We focus on tweets containing both visual information (a picture shared by the user) and metadata, a combination of text and related extra information intrinsic to the Twitter API. This work has been done in the context of the MediaEval 2018 Multimedia Satellite Task

Recommender Systems and Misinformation: The Problem or the Solution?

Recommender Systems have been pointed as one of the major culprits of misinformation spreading in the digital sphere. These systems have recently gone under heavy criticism for promoting the creation of filter bubbles, lowering the diversity of information users are exposed to and the social contacts they create. This influences the dynamics of social news sharing, and particularly the ways misinformation initiates and propagates. However, while Recommender Systems have been accused of fuelling the spread of misinformation, it is still unclear which particular types of recommender algorithms are more prone to recommend misinforming news, and if, and how, existing recommendation algorithms and evaluation metrics, can be modified or adapted to mitigate the misinformation spreading effect. In this position paper, we describe some of the key challenges behind assessing and measuring the effect of existing recommendation algorithms on the recommendation of misinforming articles and how such algorithms could be adapted, modified, and evaluated to counter this effect based on existing social science and psychology research