MediaEval 2016: Multimedia Benchmark Workshop, Working Notes Proceedings of the MediaEval 2016 Workshop Hilversum, The Netherlands, October 20-21, 2016

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The MediaEval 2016 Context of Experience Task: Recommending Videos Suiting a Watching Situation

In this paper we present an overview of the Context of ExperienceTask: recommending videos suiting a watching situationwhich is part of the MediaEval 2016 Benchmark. Theaim of the task is to explore multimedia content that iswatched under a certain situation. The scope of the thisyears task lies on movies watched during a flight. We hypothesizethat users will have different preferences for moviesthat are watched during a flight compared to when a movieis watched at home or the cinema. This is most probablyinfluenced by the context and the devices used to watch. Inthe case of being on a flight, the context is clearly differentto normal situation (noise, compact, bad air) and also thedevices differ (small screens, bad audio quality). The maingoal of the task is to estimate if a person would like to watcha certain movie on the airplane or not. As dataset we providea large collection of movies, collected from an airline,including pre-extracted visual, text and audio features

Beyond Fact-Checking: Network Analysis Tools for Monitoring Disinformation in Social Media

Operated by the H2020 SOMA Project, the recently established Social Observatory for Disinformation and Social Media Analysis supports researchers, journalists and fact-checkers in their quest for quality information. At the core of the Observatory lies the DisInfoNet Toolbox, designed to help a wide spectrum of users understand the dynamics of (fake) news dissemination in social networks. DisInfoNet combines text mining and classification with graph analysis and visualization to offer a comprehensive and user-friendly suite. To demonstrate the potential of our Toolbox, we consider a Twitter dataset of more than 1.3M tweets focused on the Italian 2016 constitutional referendum and use DisInfoNet to: (i) track relevant news stories and reconstruct their prevalence over time and space; (ii) detect central debating communities and capture their distinctive polarization/narrative; (iii) identify influencers both globally and in specific “disinformation networks”.SCOPUS: cp.kinfo:eu-repo/semantics/publishe