47 research outputs found

    How to Burst the Bubble in Social Networks?

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    Filter bubble has considered as a serious risk for democracy and freedom of information on the internet and social media. This phenomenon can restrict users\u27 access to information sources outside their comfort zone and increase the risk of polarisation of opinions on different topics. This in-progress paper explains our plan for conducting a prescriptive research aiming at decreasing the chance of filter bubbles formation on social networks. The paper explains a gap in the literature which is a prescriptive work considering both human and technology perspectives. To focus on this research gap, a design perspective has been selected covering two different bodies of theory as kernel theories. The paper explains the relevance of these theories, some of the primarily formed requirements derived from them and the future steps in this research. The explained future steps includes various phases of developing an Information Systems Design Theory and our strategy to evaluate the effectiveness of the developed theory

    Managing Temporal Dynamics of Filter Bubbles

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    Filter bubbles have attracted much attention in recent years in terms of their impact on society. Whereas it is commonly agreed that filter bubbles should be managed, the question is still how. We draw a picture of filter bubbles as dynamic, slowly changing constructs that underlie temporal dynamics and that are constantly influenced by both machine and human. Anchored in a research setting with a major public broadcaster, we follow a design science approach on how to design the temporal dynamics in filter bubbles and how to design users' influence over time. We qualitatively evaluate our approach with a smartphone app for personalized radio and found that the adjustability of filter bubbles leads to a better co-creation of information flows between information broadcaster and listener

    Burst the Filter Bubble: Towards an Integrated Tool

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    Formation of filter bubbles is known as a risk for democracy and can bring negative consequences like polarisation of the society, users’ tendency to extremist viewpoints, and the proliferation of fake news. Previous studies, including prescriptive studies, focused on limited aspects of filter bubbles. The current study aims to propose a model for an integrated tool that assists users in avoiding filter bubbles in social networks. To this end, a systematic literature review has been adopted and 571 papers in six top-ranked scientific databases have been identified. After excluding irrelevant studies and an in-depth study of the remaining papers, a classification of research studies is proposed. This classification is then used to propose an overall architecture for an integrated tool that synthesises all previous studies and proposes new features for avoiding filter bubbles. The study explains the components and features of the proposed architecture and describes their focus on content and agents

    A Survey on Popularity Bias in Recommender Systems

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    Recommender systems help people find relevant content in a personalized way. One main promise of such systems is that they are able to increase the visibility of items in the long tail, i.e., the lesser-known items in a catalogue. Existing research, however, suggests that in many situations today's recommendation algorithms instead exhibit a popularity bias, meaning that they often focus on rather popular items in their recommendations. Such a bias may not only lead to limited value of the recommendations for consumers and providers in the short run, but it may also cause undesired reinforcement effects over time. In this paper, we discuss the potential reasons for popularity bias and we review existing approaches to detect, quantify and mitigate popularity bias in recommender systems. Our survey therefore includes both an overview of the computational metrics used in the literature as well as a review of the main technical approaches to reduce the bias. We furthermore critically discuss today's literature, where we observe that the research is almost entirely based on computational experiments and on certain assumptions regarding the practical effects of including long-tail items in the recommendations.Comment: Under review, submitted to UMUA

    Personalization in Social Media: Challenges and Opportunities for Democratic Societies

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    Personalization algorithms perform a fundamental role of knowledge management in order to restrain information overload, reduce complexity and satisfy individuals. Personalization of media content in mainstream social media, however, can be used for micro-target political messages, and can also create filter bubbles and strengthen echo chambers that restrain the exposure to diverse, challenging and serendipitous information. These represent fundamental issues for media law and ethics both seeking to preserve autonomy of choice and media pluralism in democratic societies. As a result, informational empowerment may be reduced and group polarization, audience fragmentation, conspiratorial thinking and other democratically negative consequences could arise. Even though research about the detrimental effects of personalization is more often inconsistent, there is no doubt that in the long run the algorithmic capacity to steer our lives in increasingly sophisticated ways will dramatically expand. Key questions need to be further discussed; for instance, to what extent can profiling account for the complexity of individual identity? To what extent are users, media and algorithms responsible in such practices? What are the main values and trade-offs that inform designers in such a fundamental societal algorithmic arbitrage? How is social media’s personalization directly or indirectly regulated in the European Union? The thesis firstly presents a critical overview of information societies, analyzing social media content personalization practices, dynamics and unintended consequences. Secondly, it explores the role of serendipity as a design and ethical principle for social media. Thirdly, the European legal landscape with regard to personalization is analyzed from a regulatory, governance and ethical perspective. Finally, it is introduced the concept of ‘algorithmic sovereignty’ as a valuable abstraction to begin to frame technical, legal and political preconditions and standards to preserve users’ autonomy, and to minimize the risks arising in the context of personalization

    An aesthetics of touch: investigating the language of design relating to form

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    How well can designers communicate qualities of touch? This paper presents evidence that they have some capability to do so, much of which appears to have been learned, but at present make limited use of such language. Interviews with graduate designer-makers suggest that they are aware of and value the importance of touch and materiality in their work, but lack a vocabulary to fully relate to their detailed explanations of other aspects such as their intent or selection of materials. We believe that more attention should be paid to the verbal dialogue that happens in the design process, particularly as other researchers show that even making-based learning also has a strong verbal element to it. However, verbal language alone does not appear to be adequate for a comprehensive language of touch. Graduate designers-makers’ descriptive practices combined non-verbal manipulation within verbal accounts. We thus argue that haptic vocabularies do not simply describe material qualities, but rather are situated competences that physically demonstrate the presence of haptic qualities. Such competencies are more important than groups of verbal vocabularies in isolation. Design support for developing and extending haptic competences must take this wide range of considerations into account to comprehensively improve designers’ capabilities

    Technology and Australia's Future: New technologies and their role in Australia's security, cultural, democratic, social and economic systems

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    Chapter 1. Introducing technology -- Chapter 2. The shaping of technology -- Chapter 3. Prediction of future technologies -- Chapter 4. The impacts of technology -- Chapter 5. Meanings, attitudes and behaviour -- Chapter 6. Evaluation -- Chapter 7. Intervention -- Conclusion - adapt or wither.This report was commisioned by Australian Council of Learned Academies
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