Now showing 1 - 5 of 5
  • Publication
    Mining Affective Context in Short Films for Emotion-Aware Recommendation
    Emotion is fundamental to human experience and impactsour daily activities and decision-making processes where,e.g., the affective state of a user influences whether or notshe decides to consume a recommended item movie, book,product or service. However, information retrieval and recommendationtasks have largely ignored emotion as a sourceof user context, in part because emotion is difficult to measureand easy to misunderstand. In this paper we explore therole of emotions in short films and propose an approach thatautomatically extracts affective context from user commentsassociated to short films available in YouTube, as an alternativeto explicit human annotations. We go beyond the traditionalpolarity detection (i.e., positive/negative), and extractfor each film four opposing pairs of primary emotions:joysadness, angerfear, trustdisgust, and anticipationsurprise. Finally, in our empirical evaluation, we show howthe affective context extracted automatically can be leveragedfor emotion-aware film recommendation.
      741Scopus© Citations 25
  • Publication
    Modeling and Predicting News Consumption on Twitter
    Relatively little is known about the news consumption amongst social media users, despite the proliferation of news sharing, distribution platforms and news aggregators. In this paper, we present the Twitter News Model (TNM), a computational data-driven approach to elucidate the dynamics of news consumption on Twitter. We apply the TNM to a dataset of interactions between users and journalists/ newspapers to reveal what drives users’ consumption of news on Twitter, and predictively relate users’ news beliefs, motivations, and attitudes to their consumption of news.
  • Publication
    Spreading the News: How Can Journalists Gain More Engagement for their Tweets
    News media face many serious concerns as their distribution channels are gradually being taken over by third parties (e.g., people sharing news on Twitter and Facebook, and GoogleNews acting as a news aggregator). If traditional media is to survive at all, it needs to develop innovative strategies around these channels, to maximize audience engagement with the news they provide. In this paper, we focus on the issue of developing one such strategy for spreading news on Twitter. Using a corpus of 1M tweets from 200 journalist Twitter accounts and audience responses to these tweets, we develop predictive models to identify the features of both journalists and news tweets that impact audience attention. These analyses reveal that different combinations of features influence audience engagement differentially from one news category to the next (e.g., sport versus business). From these findings, we propose a set of guidelines for journalists, designed to maximize engagement with the news they tweet. Finally, we discuss how such analyses can inform innovative dissemination strategies in digital media.
      660Scopus© Citations 12
  • Publication
    On Supporting Digital Journalism: Case Studies in Co-Designing Journalistic Tools
    Since 2013 researchers at University College Dublin in the Insight Centre for Data Analytics have been involved in a significant research programme in digital journalism, specifically targeting tools and social media guidelines to support the work of journalists. Most of this programme was undertaken in collaboration with The Irish Times. This collaboration involved identifying key problems currently faced by digital journalists, developing tools as solutions to these problems, and then iteratively co-designing these tools with feedback from journalists. This paper reports on our experiences and learnings from this research programme, with a view to informing similar efforts in the future.
  • Publication
    Helping News Editors Write Better Headlines: A Recommender to Improve the Keyword Contents and Shareability of News Headlines
    We present a software tool that employs state-of- the-art natural language processing (NLP) and ma- chine learning techniques to help newspaper editors compose effective headlines for online publication. The system identifies the most salient keywords in a news article and ranks them based on both their overall popularity and their direct relevance to the article. The system also uses a supervised regres- sion model to identify headlines that are likely to be widely shared on social media. The user inter- face is designed to simplify and speed the editor’s decision process on the composition of the head- line. As such, the tool provides an efficient way to combine the benefits of automated predictors of engagement and search-engine optimization (SEO) with human judgments of overall headline quality.