Context:
In recent years alternate social
network sites without the more rigorous content moderation restrictions of more
mainstream sites have proliferated across the internet. These sites have arisen
for a variety of reasons, from pure financial profit to a desire to create distinct
online ideological communities. Often, sites with more liberaterian outlooks on
moderation and free speech have been accused of harboring radical far-right elements
that extort racial supremacist and hateful views far outside the mainstream. One
such site is Gab, a “twitter clone” that has been cited for its far-right user
base and willingness to harbor neo-Nazi, White Supremacist, and other hate
based groups[i]. This
is supported by a recent content analysis that noted that Gab users utilize
hate speech at a far higher rate than more mainstream sites such as Twitter, a
fact that was brought into stark view when the Pittsburgh Synagogue Shooter
posted a hate-filled screed on the site before his October 2018 terror attach.
Previous analyses of Gab have focused much more heavily on content analysis,
while past SNA’s of right-wing groups have focused much more on organizational
relationships between varying groups[ii].
Research Question:
This social network analysis will seek to identify the
evolution of alt-right messengers on the Gab Social Network, and identify how
they have changed as the movement has gained prominence. We will conduct an SNA
at three separate instances of time surrounding major right-wing terror
attacks, and identify the changing network characteristics of groups discussing
these events in a positive light. Possible events include the 2017 Unite the
Right Rally in Charlottesville, 2017 Portland Train Attack, and/or the October
2016 arrest of the Garden City bomb plotters.
Data Sources and
Analysis:
The dataset for the 2018 “What is Gab?” analysis is an
excellent existing publicly available data source. Researchers utilized Gab’s
API to crawl the social network and obtain data for the most popular users utilizing
a snowball sampling methodology. The researchers collected data on the most
popular users and their posts, the followers of those users, and their
followings. This should allow us to construct a dataset of the most influential
members of Gab, as well as members of their immediate network.
While it may involve some form of content analysis, we can identify
the most up-voted and shared posts surrounding the selected events. We can then
visualize those networks to see which members of the network are the most
influential and responsible for disseminating the messages most widely. Given
that the dataset is constructed from the most popular accounts on the network,
it stands to reason that the originators of posts would have high degree
measures, other centrality measures for other parts of the network such as
eigenvector could present a more nuanced and detailed story.
What this SNA could
do:
This SNA could provide an interesting snapshot of
a growing social media network and messaging strategy of a growing online
community that has only grown as a concern for policymakers. Identifying the
shifting leadership structure of those responsible for the majority of hateful
messaging on the network can allow us to understand where this movement is
going and how it is changing.
1 comment:
A good idea, but yor overall research question needs to be more meaningful and precise. For instance, you need to think a bit more about what you mean by "evolution of alt-right messengers" and "changing network characteristics." You also need to consider more deeply which network measures will be the most significant. For instance, how do you define popular? Or put a value on an up-vote or a share? And given the biased samples (most popular accounts) what if anyimpact will this have on your conclusions?
All to be teased out during the course. I look forward to seeing it develop.
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