5,776 research outputs found
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
Dancing to the Partisan Beat: A First Analysis of Political Communication on TikTok
TikTok is a video-sharing social networking service, whose popularity is
increasing rapidly. It was the world's second-most downloaded app in 2019.
Although the platform is known for having users posting videos of themselves
dancing, lip-syncing, or showcasing other talents, user-videos expressing
political views have seen a recent spurt. This study aims to perform a primary
evaluation of political communication on TikTok. We collect a set of US
partisan Republican and Democratic videos to investigate how users communicated
with each other about political issues. With the help of computer vision,
natural language processing, and statistical tools, we illustrate that
political communication on TikTok is much more interactive in comparison to
other social media platforms, with users combining multiple information
channels to spread their messages. We show that political communication takes
place in the form of communication trees since users generate branches of
responses to existing content. In terms of user demographics, we find that
users belonging to both the US parties are young and behave similarly on the
platform. However, Republican users generated more political content and their
videos received more responses; on the other hand, Democratic users engaged
significantly more in cross-partisan discussions.Comment: Accepted as a full paper at the 12th International ACM Web Science
Conference (WebSci 2020). Please cite the WebSci version; Second version
includes corrected typo
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
PopRank: Ranking pages' impact and users' engagement on Facebook
Users online tend to acquire information adhering to their system of beliefs
and to ignore dissenting information. Such dynamics might affect page
popularity. In this paper we introduce an algorithm, that we call PopRank, to
assess both the Impact of Facebook pages as well as users' Engagement on the
basis of their mutual interactions. The ideas behind the PopRank are that i)
high impact pages attract many users with a low engagement, which means that
they receive comments from users that rarely comment, and ii) high engagement
users interact with high impact pages, that is they mostly comment pages with a
high popularity. The resulting ranking of pages can predict the number of
comments a page will receive and the number of its posts. Pages impact turns
out to be slightly dependent on pages' informative content (e.g., science vs
conspiracy) but independent of users' polarization.Comment: 10 pages, 5 figure
Promoting Journalism as Method
The marketplace of ideas has been a centerpiece of free speech jurisprudence for a century. According to the marketplace theory, the vigorous competition of ideas, free from government interference, is the surest path to truth. As our metaphorical marketplace has moved online, the competition has never been so heated. We should be drowning in truth. Yet, in reality, truth has perhaps never been more elusive.
As we struggle to promote democratic debate and surface truth in our chaotic networked public sphere, we are understandably drawn to familiar frames and tools. These include the source of the marketplace of ideas theory—the First Amendment—as well the institutional press, once a key gatekeeper of that marketplace. Yet, both the institutional press and the First Amendment have limitations that hamper their ability to spark transformative change. Instead, this Article proposes that we look to journalism. Journalism is not the press or a journalist. Rather, it is a method and a practice—an evolving system for gathering, curating, and conveying information. Among its aims are accuracy and truth, the checking of power, and the creation of spaces for criticism and compromise.
Seeding and propagating journalism could have numerous benefits. It could help to provide some of the norms desperately needed for our new information environment. It might inject democratic values into an information ecology that is driven by profit-seeking. It could create friction where speed and scale now reign. Finally, it could help reinvigorate and even repopulate an institutional press in desperate need of reinforcement
Pathways to Fragmentation:User Flows and Web Distribution Infrastructures
This study analyzes how web audiences flow across online digital features. We
construct a directed network of user flows based on sequential user
clickstreams for all popular websites (n=1761), using traffic data obtained
from a panel of a million web users in the United States. We analyze these data
to identify constellations of websites that are frequently browsed together in
temporal sequences, both by similar user groups in different browsing sessions
as well as by disparate users. Our analyses thus render visible previously
hidden online collectives and generate insight into the varied roles that
curatorial infrastructures may play in shaping audience fragmentation on the
web
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