Living Knowledge

Diversity, especially manifested in language and knowledge, is a function of local goals, needs, competences, beliefs, culture, opinions and personal experience. The Living Knowledge project considers diversity as an asset rather than a problem. With the project, foundational ideas emerged from the synergic contribution of different disciplines, methodologies (with which many partners were previously unfamiliar) and technologies flowed in concrete diversity-aware applications such as the Future Predictor and the Media Content Analyser providing users with better structured information while coping with Web scale complexities. The key notions of diversity, fact, opinion and bias have been defined in relation to three methodologies: Media Content Analysis (MCA) which operates from a social sciences perspective; Multimodal Genre Analysis (MGA) which operates from a semiotic perspective and Facet Analysis (FA) which operates from a knowledge representation and organization perspective. A conceptual architecture that pulls all of them together has become the core of the tools for automatic extraction and the way they interact. In particular, the conceptual architecture has been implemented with the Media Content Analyser application. The scientific and technological results obtained are described in the following

Citizen Sensor Data Mining, Social Media Analytics and Development Centric Web Applications

With the rapid rise in the popularity of social media (500M+ Facebook users, 100M+ twitter users), and near ubiquitous mobile access (4.1 billion actively-used mobile phones), the sharing of observations and opinions has become common-place (nearly 100M tweets a day, 1.8 trillion SMSs in US last year). This has given us an unprecedented access to the pulse of a populace and the ability to perform analytics on social data to support a variety of socially intelligent applications -- be it towards targeted online content delivery, crisis management, organizing revolutions or promoting social development in underdeveloped and developing countries. This tutorial will address challenges and techniques for building applications that support a broad variety of users and types of social media. This tutorial will focus on social intelligence applications for social development, and cover the following research efforts in sufficient depth: 1) understanding and analysis of informal text, esp. microblogs (e.g., issues of cultural entity extraction and role of semantic/background knowledge enhanced techniques), and 2) building social media analytics platforms. Technical insights will be coupled with identification of computational techniques and real-world examples

Nomenclature and Contemporary Affirmation of the Unsupervised Learning in Text and Document Mining

Document clustering is primarily a method applied for an uncomplicated, document search, analysis and review of content or is a process of automatic classification of documents of similar type categorized to relevant clusters, in a clustering hierarchy. In this paper a review of the related work in the field of document clustering from the simple techniques of word and phrase to the present complex techniques of statistical analysis, machine learning etc are illustrated with their implications for future research work

Evaluation Measures for Relevance and Credibility in Ranked Lists

Recent discussions on alternative facts, fake news, and post truth politics have motivated research on creating technologies that allow people not only to access information, but also to assess the credibility of the information presented to them by information retrieval systems. Whereas technology is in place for filtering information according to relevance and/or credibility, no single measure currently exists for evaluating the accuracy or precision (and more generally effectiveness) of both the relevance and the credibility of retrieved results. One obvious way of doing so is to measure relevance and credibility effectiveness separately, and then consolidate the two measures into one. There at least two problems with such an approach: (I) it is not certain that the same criteria are applied to the evaluation of both relevance and credibility (and applying different criteria introduces bias to the evaluation); (II) many more and richer measures exist for assessing relevance effectiveness than for assessing credibility effectiveness (hence risking further bias). Motivated by the above, we present two novel types of evaluation measures that are designed to measure the effectiveness of both relevance and credibility in ranked lists of retrieval results. Experimental evaluation on a small human-annotated dataset (that we make freely available to the research community) shows that our measures are expressive and intuitive in their interpretation

Analyzing the strength of ties of Retweet in health domain

Social Network (SN) is created whenever people interact with other people. Online SN gained considerable popularity in the last years such as Fa- cebook, Twitter and etc Twitter is SN and microblogging service that creates some interesting social network structures - follow relationships. Users follow someone mostly because they share common interests and they may exchange messages called tweets. If a user post a tweet, if their follower like it they repost it or retweet it. In this context, we aim to explore and study the topological structure of user‟s retweet network, as well, new scaling measures based on strength of retweet ties. The findings suggested that relations of “friendship” are important but not enough to find out how important users are. We uncovered other some principles that must be studied like, homophily phenomenon. Ho- mophily explores properties of social network relationships, i.e. the preference for associating with individuals of the same background. Last but not least, it is worth emphasizing that we uncovered a weak correlation between Degree Cen- trality and Betweenness Centrality (49 percent) in Retweet-network and strong correlation between Degree and Betweenness centrality in Follower-network (89 percent). These find suggests that retweet network may have some fractal properties