________________________________________________________________________We explore a novel ontological approach to user profiling within recommender systems, working on the problem of recommending on-line academic research papers. Our two experimental systems, Quickstep and Foxtrot, create user profiles from unobtrusively monitored behaviour and relevance feedback, representing the profiles in terms of a research paper topic ontology. A novel profile visualization approach is taken to acquire profile feedback. Research papers are classified using ontological classes and collaborative recommendation algorithms used to recommend papers seen by similar people on their current topics of interest. Two small-scale experiments, with 24 subjects over 3 months, and a large-scale experiment, with 260 subjects over an academic year, are conducted to evaluate different aspects of our approach. Ontological inference is shown to improve user profiling, external ontological knowledge used to successfully bootstrap a recommender system and profile visualization employed to improve profiling accuracy. The overall performance of our ontological recommender systems are also presented and favourably compared to other systems in the literature.

We present a social media crisis mapping platform for natural disasters. We take locations from gazetteer, street map and volunteered geographic information (VGI) sources for areas at risk of disaster and match them to geo-parsed real-time tweet data streams. We use statistical analysis to generate real-time crisis maps. Geo-parsing results are benchmarked against existing published work and evaluated across multi-lingual datasets. We report two case studies comparing 5-day tweet crisis maps to official post-event impact assessment from the US National Geospatial Agency (NGA) compiled from verified satellite and aerial imagery sources.

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An increasing amount of posts on social media are used for disseminating news information and are accompanied by multimedia content. Such content may often be misleading or be digitally manipulated. More often than not, such pieces of content reach the front pages of major news outlets, having a detrimental effect on their credibility. To avoid such effects, there is profound need for automated methods that can help debunk and verify online content in very short time. To this end, we present a comparative study of three such Multimed Tools Appl methods that are catered for Twitter, a major social media platform used for news sharing. Those include: a) a method that uses textual patterns to extract claims about whether a tweet is fake or real and attribution statements about the source of the content; b) a method that exploits the information that same-topic tweets should be also similar in terms of credibility; and c) a method that uses a semi-supervised learning scheme that leverages the decisions of two independent credibility classifiers. We perform a comprehensive comparative evaluation of these approaches on datasets released by the Verifying Multimedia Use (VMU) task organized in the context of the 2015 and 2016 MediaEval benchmark. In addition to comparatively evaluating the three presented methods, we devise and evaluate a combined method based on their outputs, which outperforms all three of them. We discuss these findings and provide insights to guide future generations of verification tools for media professionals.

Location extraction, also called “toponym extraction,” is a field covering geoparsing, extracting spatial representations from location mentions in text, and geotagging, assigning spatial coordinates to content items. This article evaluates five “best-of-class” location extraction algorithms. We develop a geoparsing algorithm using an OpenStreetMap database, and a geotagging algorithm using a language model constructed from social media tags and multiple gazetteers. Third-party work evaluated includes a DBpedia-based entity recognition and disambiguation approach, a named entity recognition and Geonames gazetteer approach, and a Google Geocoder API approach. We perform two quantitative benchmark evaluations, one geoparsing tweets and one geotagging Flickr posts, to compare all approaches. We also perform a qualitative evaluation recalling top N location mentions from tweets during major news events. The OpenStreetMap approach was best (F1 0.90+) for geoparsing English, and the language model approach was best (F1 0.66) for Turkish. The language model was best (F1@1km 0.49) for the geotagging evaluation. The map database was best (R@20 0.60+) in the qualitative evaluation. We report on strengths, weaknesses, and a detailed failure analysis for the approaches and suggest concrete areas for further research.

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This article presents a part of the ongoing Economic and Social Research Council (ESRC)-funded project “FloraGuard: Tackling the illegal trade in endangered plants” that relies on cross-disciplinary approaches to analyze online marketplaces for the illegal trade in endangered plants, and explores strategies to develop digital resources to assist law enforcement in countering and disrupting this criminal market. This contribution focuses on how the project brought together computer science, criminology, conservation science, and law enforcement expertise to create a tool for the automatic gathering of relevant online information to be used for research, intelligence, and investigative purposes. The article also discusses the ethical standards applied and proposes the concept of “artificial intelligence (AI) review” to provide a sociotechnical solution that builds trustworthiness in the AI approaches used for this type of cross-disciplinary information and communications technology (ICT)-enabled methodology.