Now showing 1 - 10 of 78
  • Publication
    Tracking the evolution of communities in dynamic social networks
    Real-world social networks from a variety of domains can naturally be modelled as dynamic graphs. However, approaches to detecting communities have largely focused on identifying communities in static graphs. Recently, researchers have begun to consider the problem of tracking the evolution of groups of users in dynamic scenarios. Here we describe a model for tracking the progress of communities over time in a dynamic network, where each community is characterised by a series of significant evolutionary events. This model is used to motivate a community-matching strategy for efficiently identifying and tracking dynamic communities. Evaluations on synthetic graphs containing embedded events demonstrate that this strategy can successfully track communities over time in volatile networks. In addition, we describe experiments exploring the dynamic communities detected in a real mobile operator network containing millions of users.
      4907Scopus© Citations 435
  • Publication
    Identifying representative textual sources in blog networks
    (University College Dublin. School of Computer Science and Informatics, 2011-02) ; ; ; ;
    We apply methods from social network analysis and visualization to facilitate a study of the Irish blogosphere from a cultural studies perspective. We focus on solving the practical issues that arise when the goal is to perform textual analysis of the corpus produced by a network of bloggers. Previous studies into blogging networks have noted difficulties arising when trying to identify the extent and boundaries of these networks. As a response to calls for increasingly data-led approaches in media and cultural studies, we discuss a variety of social network analysis methods that can be used to identify which blogs can be seen as members of a posited "Irish blogging network". We identify hub blogs, communities of sites corresponding to different topics, and representative bloggers within these communities. Based on this study, we propose a set of analysis guidelines for researchers who wish to map out blogging networks.
  • Publication
    Detecting Attention Dominating Moments Across Media Types
    (CEUR Workshop Proceedings, 2016-03-20) ; ;
    In this paper we address the problem of identifying attention dominating moments in online media. We are interested in discovering moments when everyone seems to be talking about the same thing. We investigate one particular aspect of breaking news: the tendency of multiple sources to concentrate attention on a single topic, leading to a collapse in diversity of content for a period of time. In this work we show that diversity at a topic level is effective for capturing this effect in blogs, in news articles, and on Twitter. The phenomenon is present in three distinctly different media types, each with their own unique features. We describe the phenomenon using case studies relating to major news stories from September 2015.
  • Publication
    Development of the Ground Segment Communication System for the EIRSAT-1 CubeSat
    The Educational Irish Research Satellite (EIRSAT-1) is a student-led project to design, build and test Ireland’s first satellite. As part of the development, a ground segment (GS) has also been designed alongside the spacecraft. The ground segment will support two-way communications with the spacecraft throughout the mission. Communication with the satellite will occur in the very high frequency (VHF) and the ultra high frequency (UHF) bands for the uplink and downlink respectively. Different modulation schemes have been implemented for both uplink and downlink as part of the GS system. Uplink incorporates an Audio Frequency Shift-Keying (AFSK) scheme, while downlink incorporates a Gaussian Minimum Shift-Keying (GMSK) scheme. In order for the spacecraft to successfully receive a telecommand (TC) transmitted from the ground station, a framing protocol is required. AX.25 was selected as the data link layer protocol. A hardware terminal node controller (TNC) executes both the AX.25 framing and the AFSK modulation. Keep It Simple Stupid (KISS) framing software was developed to allow data to be accepted by the TNC. A software defined radio (SDR) approach has been chosen for the downlink. GNURadio is software that allows flowcharts to be built to undertake the required signal processing of the received signal, the demodulation of the signal and the decoding of data. This paper provides a detailed account of the software developed for the ground segment communication system. A review of the AX.25 and KISS framing protocols is presented. The GNURadio flowcharts that handle the signal processing and data decoding are broken down and each constituent is explained. To ensure the reliability and robustness of the system, a suite of tests was undertaken, the results of which are also presented.
  • Publication
    Using crowdsourcing and active learning to track sentiment in online media
    Tracking sentiment in the popular media has long been of interest to media analysts and pundits. With the availability of news content via online syndicated feeds, it is now possible to automate some aspects of this process. There is also great potential to crowdsource much of the annotation work that is required to train a machine learning system to perform sentiment scoring. We describe such a system for tracking economic sentiment in online media that has been deployed since August 2009. It uses annotations provided by a cohort of non-expert annotators to train a learning system to classify a large body of news items. We report on the design challenges addressed in managing the effort of the annotators and in making annotation an interesting experience.
      2929Scopus© Citations 67
  • Publication
    Active semi-supervised overlapping community finding with pairwise constraints
    (Springer, 2019-08-23) ;
    Algorithms for finding communities in complex networks are generally unsupervised, relying solely on the structure of the network. However, these methods can often fail to uncover meaningful groupings that reflect the underlying communities in the data, particularly when they are highly overlapping. One way to improve these algorithms is by incorporating human expertise or background knowledge in the form of pairwise constraints to direct the community detection process. In this work, we explore the potential of semi-supervised strategies to improve algorithms for finding overlapping communities in networks. We propose a method, based on label propagation, for finding communities using pairwise constraints. Furthermore, we introduce a new strategy, inspired by active learning, for intelligent constraint selection, which is designed to minimize the level of human annotation required. Extensive evaluations on synthetic and real-world datasets demonstrate the potential of this strategy for effectively uncovering meaningful overlapping community structures, using a limited amount of supervision.
      377Scopus© Citations 5
  • Publication
    TweetCric: A Twitter-based Accountability Mechanism for Cricket
    This paper demonstrates a Web service called TweetCric touncover cricket insights from Twitter with the aim of facilitating sportsanalysts and journalists. It essentially arranges crowdsourced Twitterdata about a team in comprehensive visualizations by incorporatingdomain-specic approaches to sentiment analysis.
  • Publication
    Spectral co-clustering for dynamic bipartite graphs
    (Sun SITE Central Europe (CEUR), 2010-09-24) ;
    A common task in many domains with a temporal aspect involves identifying and tracking clusters over time. Often dynamic data will have a feature-based representation. In some cases, a direct mapping will exist for both objects and features over time. But in many scenarios, smaller subsets of objects or features alone will persist across successive time periods. To address this issue, we propose a dynamic spectral co-clustering method for simultaneously clustering objects and features over time, as represented by successive bipartite graphs. We evaluate the method on a benchmark text corpus and Web 2.0 tagging data.
  • Publication
    Exploring the Role of Gender in 19th Century Fiction Through the Lens of Word Embeddings
    Within the last decade, substantial advances have been made in the field of computational linguistics, due in part to the evolution of word embedding algorithms inspired by neural network models. These algorithms attempt to derive a set of vectors which represent the vocabulary of a textual corpus in a new embedded space. This new representation can then be used to measure the underlying similarity between words. In this paper, we explore the role an author's gender may play in the selection of words that they choose to construct their narratives. Using a curated corpus of forty-eight 19th century novels, we generate, visualise, and investigate word embedding representations using a list of gender-encoded words. This allows us to explore the different ways in which male and female authors of this corpus use terms relating to contemporary understandings of gender and gender roles.
  • Publication
    Temporal Alignment of Reddit Network Embeddings
    Motivated by the concepts and findings being developed for diachronic word embeddings, in this paper, we explore how the application of the same principles can be leveraged to study structural roles from a temporal perspective. In the same way words with a similar meaning will repetitively appear in the same contexts, structural roles in graphs are also defined by the topological company that they keep. However, structurally equivalent roles may or may not occur in close proximity within a graph. Our goal is to map the participants of the popular social media website Reddit1, into an embedding space that best represents the similarity of the structural roles that they occupy and to then measure how their roles change over time.