187 research outputs found
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Recommender Systems and Misinformation: The Problem or the Solution?
Recommender Systems have been pointed as one of the major culprits of misinformation spreading in the digital sphere. These systems have recently gone under heavy criticism for promoting the creation of filter bubbles, lowering the diversity of information users are exposed to and the social contacts they create. This influences the dynamics of social news sharing, and particularly the ways misinformation initiates and propagates. However, while Recommender Systems have been accused of fuelling the spread of misinformation, it is still unclear which particular types of recommender algorithms are more prone to recommend misinforming news, and if, and how, existing recommendation algorithms and evaluation metrics, can be modified or adapted to mitigate the misinformation spreading effect. In this position paper, we describe some of the key challenges behind assessing and measuring the effect of existing recommendation algorithms on the recommendation of misinforming articles and how such algorithms could be adapted, modified, and evaluated to counter this effect based on existing social science and psychology research
Data Science, Machine learning and big data in Digital Journalism: A survey of state-of-the-art, challenges and opportunities
Digital journalism has faced a dramatic change and media companies are challenged to use data science algo-rithms to be more competitive in a Big Data era. While this is a relatively new area of study in the media landscape, the use of machine learning and artificial intelligence has increased substantially over the last few years. In particular, the adoption of data science models for personalization and recommendation has attracted the attention of several media publishers. Following this trend, this paper presents a research literature analysis on the role of Data Science (DS) in Digital Journalism (DJ). Specifically, the aim is to present a critical literature review, synthetizing the main application areas of DS in DJ, highlighting research gaps, challenges, and op-portunities for future studies. Through a systematic literature review integrating bibliometric search, text min-ing, and qualitative discussion, the relevant literature was identified and extensively analyzed. The review reveals an increasing use of DS methods in DJ, with almost 47% of the research being published in the last three years. An hierarchical clustering highlighted six main research domains focused on text mining, event extraction, online comment analysis, recommendation systems, automated journalism, and exploratory data analysis along with some machine learning approaches. Future research directions comprise developing models to improve personalization and engagement features, exploring recommendation algorithms, testing new automated jour-nalism solutions, and improving paywall mechanisms.Acknowledgements This work was supported by the FCT-Funda?a ? o para a Ciência e Tecnologia, under the Projects: UIDB/04466/2020, UIDP/04466/2020, and UIDB/00319/2020
The Datafication of Public Service Media Dreams, Dilemmas and Practical Problems:A Case Study of the Implementation of Personalized Recommendations at the Danish Public Service Media ‘DR’
Historically, public service broadcasting had no quantifiable knowledge about audiences, nor a great interest in knowing them. Today, the competitive logic of the media markets encourage public service media (PSM) organizations to increase datafication. In this paper we examine how a PSM organization interprets the classic public service obligations of creating societal cohesion and diversity in the new world of key performance indicators, business rules and algorithmic parameters.The paper presents a case study of the implementation of a personalization system for the video on demand service of the Danish PSM ‘DR’. Our empirical findings, based on longitudinal in-depth interviewing, indicate a long and difficult process of datafication of PSM, shaped by both the organizational path dependencies of broadcasting production and the expectations of public service broadcasting
The datafication of Public Service Media: Dreams, Dilemmas and Practical Problems A Case Study of the Implementation of Personalized Recommendations at the Danish Public Service Media ‘DR’
Historically, public service broadcasting had no quantifiable knowledge about audiences, nor a great interest in knowing them. Today, the competitive logic of the media markets encourage public service media (PSM) organizations to increase datafication. In this paper we examine how a PSM organization interprets the classic public service obligations of creating societal cohesion and diversity in the new world of key performance indicators, business rules and algorithmic parameters.The paper presents a case study of the implementation of a personalization system for the video on demand service of the Danish PSM ‘DR’. Our empirical findings, based on longitudinal in-depth interviewing, indicate a long and difficult process of datafication of PSM, shaped by both the organizational path dependencies of broadcasting production and the expectations of public service broadcasting
Personalised Universalism in the Age of Algorithms
In this chapter, I address a complex relationship in linking the principles of universalism and personalisation as a tension of considerable importance in contemporary media use. The paradoxical aspects of this relationship are especially evident when treated in the light of ideal types and praxis in legacy public service broadcasting (PSB) and digital public service media (PSM). The relationship is viewed from five angles, culminating in discussion about the materiality produced by shifting technologies in the digital environment and its bearing on the ideological concept of public service in media. The author introduces a new orientation for PSM: personalised enlightenment.Go to the full book to find a version of this chapter tagged for accessibility
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