Collective attention in online social networks

Social media is an ever-present tool in modern society, and its widespread usage positions it as a valuable source of insights into society at large. The study of collective attention in particular is one application that benefits from the scale of social media data. In this thesis we will investigate how collective attention manifests on social media and how it can be understood. We approach this challenge from several perspectives across network and data science. We first focus on a period of increased media attention to climate change to see how robust the previously observed polarised structures are under a collective attention event. Our experiments will show that while the level of engagement with the climate change debate increases, there is little disruption to the existing polarised structure in the communication network. Understanding the climate media debate requires addressing a methodological concern about the most effective method for weighting bipartite network projections with respect to the accuracy of community detection. We test seven weighting schemes on constructed networks with known community structure and then use the preferred methodology we identify to study collective attention in the climate change debate on Twitter. Following on from this, we will investigate how collective attention changes over the course of a single event over a longer period, namely the COVID-19 pandemic. We measure how the disruption to in-person social interactions as a consequence of attempts to limit the spread of COVID-19 in England and Wales have affected social interaction patterns as they appear on Twitter. Using a dataset of tweets with location tags, we will see how the spatial attention to locations and collective attention to discussion topics are affected by social distancing and population movement restrictions in different stages of the pandemic. Finally we present a new analysis framework for collective attention events that allows direct comparisons across different time and volume scales, such as those seen in the climate change and COVID-19 experiments. We demonstrate that this approach performs better than traditional approaches that rely on binning the timeseries at certain resolutions and comment on the mechanistic properties highlighted by our new methodology.Engineering and Physical Sciences Research Council (EPSRC

Intents-based Service Discovery and Integration

With the proliferation of Web services, when developing a new application, it makes sense to seek and leverage existing Web services rather than implementing the corresponding components from scratch. Therefore, significant research efforts have been devoted to the techniques for service discovery and integration. However, most of the existing techniques are based on the ternary participant classification of the Web service architecture which only takes into consideration the involvement of service providers, service brokers, and application developers. The activities of application end users are usually ignored. This thesis presents an Intents-based service discovery and integration approach at the conceptual level inspired by two industrial protocols: Android Intents and Web Intents. The proposed approach is characterized by allowing application end users to participate in the process of service seeking. Instead of directly binding with remote services, application developers can set an intent which semantically represents their service goal. An Intents user agent can resolve the intent and generate a list of candidate services. Then application end users can choose a service as the ultimate working service. This thesis classifies intents into explicit intents, authoritative intents, and naïve intents, and examines in depth the issue of naïve intent resolution analytically and empirically. Based on the empirical analysis, an adaptive intent resolution approach is devised. This thesis also presents a design for the Intents user agent and demonstrates its proof-of-concept prototype. Finally, Intents and the Intents user agent are applied to integrate Web applications and native applications on mobile devices

Recommender Systems

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports

Web Service Recommender Systems: Methodologies, Merits and Demerits

Web services nowadays are considered a consolidated reality of the modern Web with remarkable, increasing influence on everyday computing tasks. Following Service-Oriented Architecture (SOA) paradigm, corporations are increasingly offering their services within and between organizations either on intranets or the cloud. Recommender Systems are the software agents guiding the web services to reach the end user. The aim of this paper is to present the survey of advancements in assisting end users and corporations to benefit from Web service technology by facilitating the recommendation and integration of Web services into composite services

TweeProfiles4: a weighted multidimensional stream clustering algorithm

O aparecimento das redes sociais abriu aos utilizadores a possibilidade de facilmente partilharem as suas ideias a respeito de diferentes temas, o que constitui uma fonte de informação enriquecedora para diversos campos. As plataformas de microblogging sofreram um grande crescimento e de forma constante nos últimos anos. O Twitter é o site de microblogging mais popular, tornando-se uma fonte de dados interessante para extração de conhecimento. Um dos principais desafios na análise de dados provenientes de redes sociais é o seu fluxo, o que dificulta a aplicação de processos tradicionais de data mining. Neste sentido, a extração de conhecimento sobre fluxos de dados tem recebido um foco significativo recentemente. O TweeProfiles é a uma ferramenta de data mining para análise e visualização de dados do Twitter sobre quatro dimensões: espacial (a localização geográfica do tweet), temporal (a data de publicação do tweet), de conteúdo (o texto do tweet) e social (o grafo dos relacionamentos). Este é um projeto em desenvolvimento que ainda possui muitos aspetos que podem ser melhorados. Uma das recentes melhorias inclui a substituição do algoritmo de clustering original, o qual não suportava o fluxo contínuo dos dados, por um método de streaming. O objetivo desta dissertação passa pela continuação do desenvolvimento do TweeProfiles. Em primeiro lugar, será proposto um novo algoritmo de clustering para fluxos de dados com o objetivo de melhorar o existente. Para esse efeito será desenvolvido um algoritmo incremental com suporte para fluxos de dados multi-dimensionais. Esta abordagem deve permitir ao utilizador alterar dinamicamente a importância relativa de cada dimensão do processo de clustering. Adicionalmente, a avaliação empírica dos resultados será alvo de melhoramento através da identificação e implementação de medidas adequadas de avaliação dos padrões extraídos. O estudo empírico será realizado através de tweets georreferenciados obtidos pelo SocialBus.The emergence of social media made it possible for users to easily share their thoughts on different topics, which constitutes a rich source of information for many fields. Microblogging platforms experienced a large and steady growth over the last few years. Twitter is the most popular microblogging site, making it an interesting source of data for pattern extraction. One of the main challenges of analyzing social media data is its continuous nature, which makes it hard to use traditional data mining. Therefore, mining stream data has also received a lot of attention recently.TweeProfiles is a data mining tool for analyzing and visualizing Twitter data over four dimensions: spatial (the location of the tweet), temporal (the timestamp of the tweet), content (the text of the tweet) and social (relationship graph). This is an ongoing project which still has many aspects that can be improved. For instance, it was recently improved by replacing the original clustering algorithm which could not handle the continuous flow of data with a streaming method. The goal of this dissertation is to continue the development of TweeProfiles. First, the stream clustering process will be improved by proposing a new algorithm. This will be achieved by developing an incremental algorithm with support for multi-dimensional streaming data. Moreover, it should make it possible for the user to dynamically change the relative importance of each dimension in the clustering. Additionally, the empirical evaluation of the results will also be improved.Suitable measures to evaluate the extracted patterns will be identified and implemented. An empirical study will be done using data consisting of georeferenced tweets from SocialBus

A Novel and Domain-Specific Document Clustering and Topic Aggregation Toolset for a News Organisation

Large collections of documents are becoming increasingly common in the news gathering industry. A review of the literature shows there is a growing interest in datadriven journalism and specifically that the journalism profession needs better tools to understand and develop actionable knowledge from large document sets. On a daily basis, journalists are tasked with searching a diverse range of document sets including news gathering services, emails, freedom of information requests, court records, government reports, press releases and many other types of generally unstructured documents. Document clustering techniques can help address problems of understanding the ever expanding quantities of documents available to journalists by finding patterns within documents. These patterns can be used to develop useful and actionable knowledge which can contribute to journalism. News articles in particular are fertile ground for document clustering principles. Term weighting schemes assign importance to terms within a document and are central to the study of document clustering methods. This study contributes a review of the dominant and most commonly used term frequency weighting functions put forward in research, establishes the merits and limitations of each approach, and proposes modifications to develop a news-centric document clustering and topic aggregation approach. Experimentation was conducted on a large unstructured collection of newspaper articles from the Irish Times to establish if the newly proposed news-centric term weighting and document similarity approach improves document clustering accuracy and topic aggregation capabilities for news articles when compared to the traditional term weighting approach. Whilst the experimentation shows that that the developed approach is promising when compared to the manual document clustering effort undertaken by the three journalist expert users, it also highlights the challenges of natural language processing and document clustering methods in general. The results may suggest that a blended approach of complimenting automated methods with human-level supervision and guidance may yield the best results