3,602 research outputs found
Vulnerability in Social Epistemic Networks
Social epistemologists should be well-equipped to explain and evaluate the growing vulnerabilities associated with filter bubbles, echo chambers, and group polarization in social media. However, almost all social epistemology has been built for social contexts that involve merely a speaker-hearer dyad. Filter bubbles, echo chambers, and group polarization all presuppose much larger and more complex network structures. In this paper, we lay the groundwork for a properly social epistemology that gives the role and structure of networks their due. In particular, we formally define epistemic constructs that quantify the structural epistemic position of each node within an interconnected network. We argue for the epistemic value of a structure that we call the (m,k)-observer. We then present empirical evidence that (m,k)-observers are rare in social media discussions of controversial topics, which suggests that people suffer from serious problems of epistemic vulnerability. We conclude by arguing that social epistemologists and computer scientists should work together to develop minimal interventions that improve the structure of epistemic networks
Measuring Online Social Bubbles
Social media have quickly become a prevalent channel to access information,
spread ideas, and influence opinions. However, it has been suggested that
social and algorithmic filtering may cause exposure to less diverse points of
view, and even foster polarization and misinformation. Here we explore and
validate this hypothesis quantitatively for the first time, at the collective
and individual levels, by mining three massive datasets of web traffic, search
logs, and Twitter posts. Our analysis shows that collectively, people access
information from a significantly narrower spectrum of sources through social
media and email, compared to search. The significance of this finding for
individual exposure is revealed by investigating the relationship between the
diversity of information sources experienced by users at the collective and
individual level. There is a strong correlation between collective and
individual diversity, supporting the notion that when we use social media we
find ourselves inside "social bubbles". Our results could lead to a deeper
understanding of how technology biases our exposure to new information
Quantifying Biases in Online Information Exposure
Our consumption of online information is mediated by filtering, ranking, and
recommendation algorithms that introduce unintentional biases as they attempt
to deliver relevant and engaging content. It has been suggested that our
reliance on online technologies such as search engines and social media may
limit exposure to diverse points of view and make us vulnerable to manipulation
by disinformation. In this paper, we mine a massive dataset of Web traffic to
quantify two kinds of bias: (i) homogeneity bias, which is the tendency to
consume content from a narrow set of information sources, and (ii) popularity
bias, which is the selective exposure to content from top sites. Our analysis
reveals different bias levels across several widely used Web platforms. Search
exposes users to a diverse set of sources, while social media traffic tends to
exhibit high popularity and homogeneity bias. When we focus our analysis on
traffic to news sites, we find higher levels of popularity bias, with smaller
differences across applications. Overall, our results quantify the extent to
which our choices of online systems confine us inside "social bubbles."Comment: 25 pages, 10 figures, to appear in the Journal of the Association for
Information Science and Technology (JASIST
Illuminating an Ecosystem of Partisan Websites
This paper aims to shed light on alternative news media ecosystems that are
believed to have influenced opinions and beliefs by false and/or biased news
reporting during the 2016 US Presidential Elections. We examine a large,
professionally curated list of 668 hyper-partisan websites and their
corresponding Facebook pages, and identify key characteristics that mediate the
traffic flow within this ecosystem. We uncover a pattern of new websites being
established in the run up to the elections, and abandoned after. Such websites
form an ecosystem, creating links from one website to another, and by `liking'
each others' Facebook pages. These practices are highly effective in directing
user traffic internally within the ecosystem in a highly partisan manner, with
right-leaning sites linking to and liking other right-leaning sites and
similarly left-leaning sites linking to other sites on the left, thus forming a
filter bubble amongst news producers similar to the filter bubble which has
been widely observed among consumers of partisan news. Whereas there is
activity along both left- and right-leaning sites, right-leaning sites are more
evolved, accounting for a disproportionate number of abandoned websites and
partisan internal links. We also examine demographic characteristics of
consumers of hyper-partisan news and find that some of the more populous
demographic groups in the US tend to be consumers of more right-leaning sites.Comment: Published at The Web Conference 2018 (WWW 2018). Please cite the WWW
versio
Polarization in Online Social Networks: A Review of Mechanisms and Dimensions
The extensive use of social media has sparked a controversial debate regarding interactions on social media platforms and their influence on network polarization. Still, the complex phenomenon lacks conceptual clarity. Conducting a systematic literature review on existing empirical findings, we take first steps to a more systematic conceptualization of polarization by identifying (a) the dimensions on which polarization is manifesting and (b) relevant influence factors associated with the emergence of polarization phenomena in online social networks. Further, we derive an integrated theory-driven framework offering a comprehensive set of mechanisms associated with polarization on social media and its concrete manifestation. We identified Attitude Extremity, Topic Diversity, Social Fragmentation, and Language Usage as four dimensions of how polarization is manifesting. The framework is a relevant starting point to attain coherence in future research on polarization phenomena in IS research and contributes to a more systematic discussion of unintended consequences of ICT usage
Burst the Filter Bubble: Towards an Integrated Tool
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
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Conformity in scientific networks
Scientists are generally subject to social pressures, including pressures to conform with others in their communities, that affect achievement of their epistemic goals. Here we analyze a network epistemology model in which agents, all else being equal, prefer to take actions that conform with those of their neighbors. This preference for conformity interacts with the agents’ beliefs about which of two (or more) possible actions yields the better result. We find a range of possible outcomes, including stable polarization in belief and action. The model results are sensitive to network structure. In general, though, conformity has a negative effect on a community’s ability to reach accurate consensus about the world
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