Arkaitz Zubiaga

TL;DR: In this paper, the authors report on the progress of a project that aims to address barriers, one of which is information overload, to achieving effective direct citizen participation in democratic decision-making processes and explore if the application of Natural Language Processing (NLP) and machine learning can improve citizens' experience of digital citizen participation platforms.

TL;DR: This study studies the performance stability when switching to the new domain of mental health disorders, and confirms that performance drops when the trained model is applied on a new domain, emphasising the differences in rumours across domains.

TL;DR: An overview of research in data mining and natural language processing for mining social media for newsgathering is provided, and five different areas that researchers have worked on to mitigate the challenges inherent to social media newsg gathering are discussed.

TL;DR: The WISC team on the Multimedia Satellite Task at MediaEval 2017 finds that tags defined by users to describe the images are very helpful for achieving high accuracy classification and social media can increase the precision in analyses when combined with satellite images by taking advantage of spatial and temporal overlaps between data sources.

TL;DR: A benchmark for lexical normalization of social media posts, specifically for tweets in Spanish language is presented, including an evaluation framework, as well as an annotated corpus of Spanish tweets—TweetNorm_es—, which is made publicly available.