218,288 research outputs found
Jointly they edit: examining the impact of community identification on political interaction in Wikipedia
In their 2005 study, Adamic and Glance coined the memorable phrase "divided
they blog", referring to a trend of cyberbalkanization in the political
blogosphere, with liberal and conservative blogs tending to link to other blogs
with a similar political slant, and not to one another. As political discussion
and activity increasingly moves online, the power of framing political
discourses is shifting from mass media to social media. Continued examination
of political interactions online is critical, and we extend this line of
research by examining the activities of political users within the Wikipedia
community. First, we examined how users in Wikipedia choose to display (or not
to display) their political affiliation. Next, we more closely examined the
patterns of cross-party interaction and community participation among those
users proclaiming a political affiliation. In contrast to previous analyses of
other social media, we did not find strong trends indicating a preference to
interact with members of the same political party within the Wikipedia
community. Our results indicate that users who proclaim their political
affiliation within the community tend to proclaim their identity as a
"Wikipedian" even more loudly. It seems that the shared identity of "being
Wikipedian" may be strong enough to triumph over other potentially divisive
facets of personal identity, such as political affiliation.Comment: 33 pages, 5 figure
Searching for superspreaders of information in real-world social media
A number of predictors have been suggested to detect the most influential
spreaders of information in online social media across various domains such as
Twitter or Facebook. In particular, degree, PageRank, k-core and other
centralities have been adopted to rank the spreading capability of users in
information dissemination media. So far, validation of the proposed predictors
has been done by simulating the spreading dynamics rather than following real
information flow in social networks. Consequently, only model-dependent
contradictory results have been achieved so far for the best predictor. Here,
we address this issue directly. We search for influential spreaders by
following the real spreading dynamics in a wide range of networks. We find that
the widely-used degree and PageRank fail in ranking users' influence. We find
that the best spreaders are consistently located in the k-core across
dissimilar social platforms such as Twitter, Facebook, Livejournal and
scientific publishing in the American Physical Society. Furthermore, when the
complete global network structure is unavailable, we find that the sum of the
nearest neighbors' degree is a reliable local proxy for user's influence. Our
analysis provides practical instructions for optimal design of strategies for
"viral" information dissemination in relevant applications.Comment: 12 pages, 7 figure
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