Providing Awareness, Understanding and Control of Personalized Stream Filtering in a P2P Social Network

Nagulendra, Sayooran; Vassileva, Julita

doi:10.1007/978-3-642-41347-6_6

Cited by 9 publications

(5 citation statements)

References 14 publications

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“…Eleven (11) graduate students from the MADMUC research lab used the MADMICA system with the filter bubble visualization instead of Facebook and shared interesting and research-related links over a period of three weeks in March 2013. The results of the study showed that the filter bubble visualization made the users aware of the filtering mechanism, engaged them in actions to correct and change it, and as a result, increased the users' trust in the system [5]. In order to gain a deeper understanding of the ways users perceive and understand the visualization, we carried out a large scale quantitative study with 163 Mechanical Turk participants, described in the next section.…”

Section: Figure 1 Anna's "Category View" Of Her Filter Bubble Relatementioning

confidence: 98%

“…We presented in [5] a detailed justification of the visualization design decisions and a pilot user study to evaluate the usability and user acceptance of the visualization and whether it achieves its goals of providing awareness, control and trust in the filtering mechanism in MADMICA. Eleven (11) graduate students from the MADMUC research lab used the MADMICA system with the filter bubble visualization instead of Facebook and shared interesting and research-related links over a period of three weeks in March 2013.…”

Section: Figure 1 Anna's "Category View" Of Her Filter Bubble Relatementioning

confidence: 99%

“…We proposed an approach [5] to make the user aware of the filtering mechanism and take control over it. It is based on an interactive visualization that shows the filter bubble and some features of the hidden filtered data (its semantics and origin).…”

Section: Introductionmentioning

confidence: 99%

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Understanding and controlling the filter bubble through interactive visualization

Nagulendra

Vassileva

2014

Proceedings of the 25th ACM Conference on Hypertext and Social Media

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The "filter bubble" is a term which refers to people getting encapsulated in streams of data such as news or social network updates that are personalized to their interests. While people need protection from information overload and maybe prefer to see content they feel familiar or agree with, there is the danger that important issues that should be of concern for everyone will get filtered away and people will lack exposure to different views, living in "echo-chambers", blissfully unaware of the reality. We have proposed a design of an interactive visualization, which provides the user of a social networking site with awareness of the personalization mechanism (the semantics and the source of the content that is filtered away), and with means to control the filtering mechanism. The visualization has been implemented in a peer-to-peer social network, called MADMICA, and we present here the results of a large scale lab study with 163 crowd-sourced participants. The results demonstrate that the visualization leads to increased users' awareness of the filter bubble, understandability of the filtering mechanism and to a feeling of control over their data stream.

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Section: Figure 1 Anna's "Category View" Of Her Filter Bubble Relatementioning

confidence: 98%

Section: Figure 1 Anna's "Category View" Of Her Filter Bubble Relatementioning

confidence: 99%

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Understanding and controlling the filter bubble through interactive visualization

Nagulendra

Vassileva

2014

Proceedings of the 25th ACM Conference on Hypertext and Social Media

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“…This can be cultivated with regular updates on personalization research, visualization tools, and dashboards. Some paradigmatic experiments have already been done: (1) in general, through browser extensions (Reviglio, 2019a), (2) with interactive sliders (e.g., gobo.social), or (3) showing users their filter bubbles and help to "burst" them (Nagulendra & Vassileva, 2016). Clearly, to gain more control, algorithmic literacy need to go hand in hand with digital and media literacy.…”

Section: Algorithmic Sovereignty In Theorymentioning

confidence: 99%

Thinking Outside the Black-Box: The Case for “Algorithmic Sovereignty” in Social Media

Reviglio

Agosti

2020

Social Media + Society

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This article is an interdisciplinary critical analysis of personalization systems and the gatekeeping role of current mainstream social media. The first section presents a literature review of data-driven personalization and its challenges in social media. The second section sheds light on increasing concerns regarding algorithms’ ability to overtly persuade—and covertly manipulate—users for the sake of engagement, introducing the emergence of the exclusive ownership of behavioral modification through hyper-nudging techniques. The third section empirically analyzes users’ expectations and behaviors regarding such data-driven personalization to frame a conceptualization of users’ agency. The fourth section introduces the concept of “algorithmic sovereignty.” Current projects that aim to grant this algorithmic sovereignty highlight some potential applications. Together this novel theoretical framework and empirical applications suggest that, to preserve trust, social media should open their personalization algorithms to a social negotiation as the first step toward a more sustainable social media landscape. To decentralize the immense power of mainstream social media, guarantee a democratic oversight, and mitigate the unintended undesirable consequences of their algorithmic curation, public institutions and civil society could help in developing and researching public algorithms, fostering a collective awareness so as to eventually ensure a fair and accountable “algorithmic sovereignty.”

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“…This paper investigates how exploration can be supported in a way that improves serendipity, and maintains a sense of inspectability and control. The intuitions guiding the studies in this paper are based on findings in the area of social recommendations, i.e., based on people's relationships in online social networks (e.g., [17]) in addition to more classical recommendation algorithms.…”

Section: Community-based Content Discoverymentioning

confidence: 99%

What am I not Seeing? An Interactive Approach to Social Content Discovery in Microblogs

Kang

Tintarev

Höllerer

et al. 2016

Lecture Notes in Computer Science

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Abstract. In this paper, we focus on the informational and user experience benefits of user-driven topic exploration in microblog communities, such as Twitter, in an inspectable, controllable and personalized manner. To this end, we introduce "HopTopics" -a novel interactive tool for exploring content that is popular just beyond a user's typical information horizon in a microblog, as defined by the network of individuals that they are connected to. We present results of a user study (N=122) to evaluate HopTopics with varying complexity against a typical microblog feed in both personalized and non-personalized conditions. Results show that the HopTopics system, leveraging content from both the direct and extended network of a user, succeeds in giving users a better sense of control and transparency. Moreover, participants had a poor mental model for the degree of novel content discovered when presented with non-personalized data in the Inspectable interface.

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Providing Awareness, Understanding and Control of Personalized Stream Filtering in a P2P Social Network

Cited by 9 publications

References 14 publications

Understanding and controlling the filter bubble through interactive visualization

Understanding and controlling the filter bubble through interactive visualization

Thinking Outside the Black-Box: The Case for “Algorithmic Sovereignty” in Social Media

What am I not Seeing? An Interactive Approach to Social Content Discovery in Microblogs

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