3,746 research outputs found
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
Language, Twitter and Academic Conferences
Using Twitter during academic conferences is a way of engaging and connecting
an audience inherently multicultural by the nature of scientific collaboration.
English is expected to be the lingua franca bridging the communication and
integration between native speakers of different mother tongues. However,
little research has been done to support this assumption. In this paper we
analyzed how integrated language communities are by analyzing the scholars'
tweets used in 26 Computer Science conferences over a time span of five years.
We found that although English is the most popular language used to tweet
during conferences, a significant proportion of people also tweet in other
languages. In addition, people who tweet solely in English interact mostly
within the same group (English monolinguals), while people who speak other
languages tend to show a more diverse interaction with other lingua groups.
Finally, we also found that the people who interact with other Twitter users
show a more diverse language distribution, while people who do not interact
mostly post tweets in a single language. These results suggest a relation
between the number of languages a user speaks, which can affect the interaction
dynamics of online communities.Comment: 4 pages, 3 figures, 4 tables, submitted to ACM Hypertext and Social
Media 201
Automated construction and analysis of political networks via open government and media sources
We present a tool to generate real world political networks from user provided lists of politicians and news sites. Additional output includes visualizations, interactive tools and maps that allow a user to better understand the politicians and their surrounding environments as portrayed by the media. As a case study, we construct a comprehensive list of current Texas politicians, select news sites that convey a spectrum of political viewpoints covering Texas politics, and examine the results. We propose a ”Combined” co-occurrence distance metric to better reflect the relationship between two entities. A topic modeling technique is also proposed as a novel, automated way of labeling communities that exist within a politician’s ”extended” network.Peer ReviewedPostprint (author's final draft
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