Abstract. Social Media provide a vast amount of information identifying stories, events, entities that play the crucial role of shaping the community in an everyday heavy user involvement. This work involves the study of social media information in terms of type (multimodal: text, video, sound, picture) and role players (agents, users, opinion leaders) and the potential of designing accessible, usable interfaces that integrate that information. This case examines the design of a user interface that uses an underlying engine for modality components (plain text, sound, image, video) analysis, social media crawling, contextual search fusion and semantic analysis. The interface is the only point of user interaction to the world of knowledge. This work reports on the usability and accessibility methods and concerns for the user requirements phase and the design control and testing. The findings of the pilot user testing and evaluation provide indications on how the semantic analysis of the social media information can be integrated to the design methodologies for user interfaces resulting in maximization of user experience in terms of social information involvement.
Digital archives comprise a valuable asset for effective information retrieval. In many cases, however, the special vocabulary of the archive restricts its access only to experts in the domain of the material it contains and, as a result, researchers of other disciplines or the general public cannot take full advantage of the wealth of information it offers. To this end, the Papyrus research project has worked towards a solution which makes cross-discipline search possible in digital libraries. The developed prototype showcases this approach demonstrating how we can discover history in news archives. In this demo we focus on demonstrating two of the end user tools available in the prototype, the cross-discipline search and the Papyrus browser.
There are both positive and negative aspects in the use of social media in news and information dissemination. To deal with the negative aspects, such as the spread of rumours and fake news, the flow of information should implicitly be filtered and marked to specific criteria such as credibility, trustworthiness, reputation, popularity, influence, and authenticity. This paper proposes an approach that can enhance trustworthiness and content validity in the presence of information overload. We introduce Alethiometer, a framework for assessing truthfulness in social media that can be used by professional and general news users alike. We present different measures that delve into the detailed analysis of the content, the contributors of the content and the underlying context. We further propose an approach for deriving a single metric that considers, in a unified manner, the quality of a contributor and of the content provided by that contributor. Finally, we present some preliminary statistical results from the examination of a set of 10 million twitter users, that provide useful insights on the characteristics of social media data.
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