Detection of Trending Topic Communities: Bridging Content Creators and Distributors

The rise of a trending topic on Twitter or Facebook leads to the temporal emergence of a set of users currently interested in that topic. Given the temporary nature of the links between these users, being able to dynamically identify communities of users related to this trending topic would allow for a rapid spread of information. Indeed, individual users inside a community might receive recommendations of content generated by the other users, or the community as a whole could receive group recommendations, with new content related to that trending topic. In this paper, we tackle this challenge, by identifying coherent topic-dependent user groups, linking those who generate the content (creators) and those who spread this content, e.g., by retweeting/reposting it (distributors). This is a novel problem on group-to-group interactions in the context of recommender systems. Analysis on real-world Twitter data compare our proposal with a baseline approach that considers the retweeting activity, and validate it with standard metrics. Results show the effectiveness of our approach to identify communities interested in a topic where each includes content creators and content distributors, facilitating users' interactions and the spread of new information.Comment: 9 pages, 4 figures, 2 tables, Hypertext 2017 conferenc

Data Portraits and Intermediary Topics: Encouraging Exploration of Politically Diverse Profiles

In micro-blogging platforms, people connect and interact with others. However, due to cognitive biases, they tend to interact with like-minded people and read agreeable information only. Many efforts to make people connect with those who think differently have not worked well. In this paper, we hypothesize, first, that previous approaches have not worked because they have been direct -- they have tried to explicitly connect people with those having opposing views on sensitive issues. Second, that neither recommendation or presentation of information by themselves are enough to encourage behavioral change. We propose a platform that mixes a recommender algorithm and a visualization-based user interface to explore recommendations. It recommends politically diverse profiles in terms of distance of latent topics, and displays those recommendations in a visual representation of each user's personal content. We performed an "in the wild" evaluation of this platform, and found that people explored more recommendations when using a biased algorithm instead of ours. In line with our hypothesis, we also found that the mixture of our recommender algorithm and our user interface, allowed politically interested users to exhibit an unbiased exploration of the recommended profiles. Finally, our results contribute insights in two aspects: first, which individual differences are important when designing platforms aimed at behavioral change; and second, which algorithms and user interfaces should be mixed to help users avoid cognitive mechanisms that lead to biased behavior.Comment: 12 pages, 7 figures. To be presented at ACM Intelligent User Interfaces 201

A Topic Recommender for Journalists

The way in which people acquire information on events and form their own opinion on them has changed dramatically with the advent of social media. For many readers, the news gathered from online sources become an opportunity to share points of view and information within micro-blogging platforms such as Twitter, mainly aimed at satisfying their communication needs. Furthermore, the need to deepen the aspects related to news stimulates a demand for additional information which is often met through online encyclopedias, such as Wikipedia. This behaviour has also influenced the way in which journalists write their articles, requiring a careful assessment of what actually interests the readers. The goal of this paper is to present a recommender system, What to Write and Why, capable of suggesting to a journalist, for a given event, the aspects still uncovered in news articles on which the readers focus their interest. The basic idea is to characterize an event according to the echo it receives in online news sources and associate it with the corresponding readers’ communicative and informative patterns, detected through the analysis of Twitter and Wikipedia, respectively. Our methodology temporally aligns the results of this analysis and recommends the concepts that emerge as topics of interest from Twitter and Wikipedia, either not covered or poorly covered in the published news articles

Modelling Requirements for Content Recommendation Systems

This paper addresses the modelling of requirements for a content Recommendation System (RS) for Online Social Networks (OSNs). On OSNs, a user switches roles constantly between content generator and content receiver. The goals and softgoals are different when the user is generating a post, as opposed as replying to a post. In other words, the user is generating instances of different entities, depending on the role she has: a generator generates instances of a "post", while the receiver generates instances of a "reply". Therefore, we believe that when addressing Requirements Engineering (RE) for RS, it is necessary to distinguish these roles clearly. We aim to model an essential dynamic on OSN, namely that when a user creates (posts) content, other users can ignore that content, or themselves start generating new content in reply, or react to the initial posting. This dynamic is key to designing OSNs, because it influences how active users are, and how attractive the OSN is for existing, and to new users. We apply a well-known Goal Oriented RE (GORE) technique, namely i-star, and show that this language fails to capture this dynamic, and thus cannot be used alone to model the problem domain. Hence, in order to represent this dynamic, its relationships to other OSNs' requirements, and to capture all relevant information, we suggest using another modelling language, namely Petri Nets, on top of i-star for the modelling of the problem domain. We use Petri Nets because it is a tool that is used to simulate the dynamic and concurrent activities of a system and can be used by both practitioners and theoreticians.Comment: 28 pages, 7 figure

Benchmarking News Recommendations in a Living Lab

Most user-centric studies of information access systems in literature suffer from unrealistic settings or limited numbers of users who participate in the study. In order to address this issue, the idea of a living lab has been promoted. Living labs allow us to evaluate research hypotheses using a large number of users who satisfy their information need in a real context. In this paper, we introduce a living lab on news recommendation in real time. The living lab has first been organized as News Recommendation Challenge at ACM RecSys’13 and then as campaign-style evaluation lab NEWSREEL at CLEF’14. Within this lab, researchers were asked to provide news article recommendations to millions of users in real time. Different from user studies which have been performed in a laboratory, these users are following their own agenda. Consequently, laboratory bias on their behavior can be neglected. We outline the living lab scenario and the experimental setup of the two benchmarking events. We argue that the living lab can serve as reference point for the implementation of living labs for the evaluation of information access systems

Report on the Information Retrieval Festival (IRFest2017)

The Information Retrieval Festival took place in April 2017 in Glasgow. The focus of the workshop was to bring together IR researchers from the various Scottish universities and beyond in order to facilitate more awareness, increased interaction and reflection on the status of the field and its future. The program included an industry session, research talks, demos and posters as well as two keynotes. The first keynote was delivered by Prof. Jaana Kekalenien, who provided a historical, critical reflection of realism in Interactive Information Retrieval Experimentation, while the second keynote was delivered by Prof. Maarten de Rijke, who argued for more Artificial Intelligence usage in IR solutions and deployments. The workshop was followed by a "Tour de Scotland" where delegates were taken from Glasgow to Aberdeen for the European Conference in Information Retrieval (ECIR 2017

Enhancing E-learning platforms with social networks mining

Social Networks appeared as an Internet application that offers several tools to create a personal virtual profile, add other users as friends, and interact with them through messages. These networks quickly evolved and won particular importance in people lives. Now, everyday, people use social networks to share news, interests, and discuss topics that in some way are important to them. Together with social networks, e-learning platforms and related technologies have evolved in the recent years. Both platforms and technologies (social networks and e-learning) enable access to specific information and are able to redirect specific content to an individual person. This dissertation is motivated on social networks data mining over e-learning platforms. It considers the following four social networks: Facebook, Twitter, Google Plus, and Delicious. In order to acquire, analyze, and make a correct and precise implementation of data, two different approaches were followed: enhancement of a current e-learning platform and improvement of search engines. The first approach proposes and elaborates a recommendation tool for Web documents using, as main criterion, social information to support a custom Learning Management System (LMS). In order to create the proposed system, three distinct applications (the Crawler, the SocialRank, and the Recommender) were proposed. Such data will be then incorporated into an LMS system, such as the Personal Learning Environment Box (PLEBOX). PLEBOX is a custom platform based on operating systems layout, and also, provides a software development kit (SDK), a group of tools, to create and manage modules. The results of recommendation tool about ten course units are presented. The second part presents an approach to improve a search engine based on social networks content. Subsequently, a depth analysis to justify the abovementioned procedures in order to create the SocialRank is presented. Finally, the results are presented and validated together with a custom search engine. Then, a solution to integrate and offer an order improvement of Web contents in a search engine was proposed, created, demonstrated, and validated, and it is ready for use.As redes sociais surgiram como um serviço Web com funcionalidades de criação de perfil, criação e interação de amigos. Estas redes evoluíram rapidamente e ganharam uma determinada importância na vida das pessoas. Agora, todos os dias, as pessoas usam as redes sociais para partilhar notícias, interesses e discutir temas que de alguma forma são importantes para elas. Juntamente com as redes sociais, as plataformas de aprendizagem baseadas em tecnologias, conhecidas como plataformas E-learning têm evoluído muito nos últimos anos. Ambas as plataformas e tecnologias (redes sociais e E-learning) fornecem acesso a informações específicas e são capazes de redirecionar determinado conteúdo para um ou vários indivíduos (personalização). O tema desta dissertação é motivado pela mineração do conteúdo das redes sociais em plataformas E-learning. Neste sentido, foram selecionadas quatro redes sociais, Facebook, Twitter, Google Plus, e Delicious para servir de estudo de caso à solução proposta. A fim de adquirir, analisar e concretizar uma aplicação correta e precisa dos dados, duas abordagens diferentes foram seguidas: enriquecimento de uma plataforma E-learning atual e melhoria dos motores de busca. A primeira abordagem propõe e elaboração de uma ferramenta de recomendação de documentos Web usando, como principal critério, a informação social para apoiar um sistema de gestão de aprendizagem (LMS). Desta forma, foram construídas três aplicações distintas, designadas por Crawler, SocialRank e Recommender. As informações extraídas serão incorporadas num sistema E-learning, tendo sido escolhida a PLEBOX (Personal Learning Environment Box). A PLEBOX é uma plataforma personalizada baseada numa interface inspirada nos sistemas operativos, fornecendo um conjunto de ferramentas (os conhecidos SDK - software development kit), para a criação e gestão de módulos. Dez unidades curriculares foram avaliadas e os resultados do sistema de recomendação são apresentados. A segunda abordagem apresenta uma proposta para melhorar um motor de busca com base no conteúdo das redes sociais. Subsequentemente, uma análise profunda é apresentada, justificando os procedimentos de avaliação, afim de criar o ranking de resultados (o SocialRank). Por último, os resultados são apresentados e validados em conjunto com um motor de busca. Assim, foi proposta, construída, demonstrada e avaliada uma solução para integrar e oferecer uma melhoria na ordenação de conteúdos Web dentro de um motor de busca. A solução está pronta para ser utilizad