77 research outputs found
Dynamics of Content Quality in Collaborative Knowledge Production
We explore the dynamics of user performance in collaborative knowledge
production by studying the quality of answers to questions posted on Stack
Exchange. We propose four indicators of answer quality: answer length, the
number of code lines and hyperlinks to external web content it contains, and
whether it is accepted by the asker as the most helpful answer to the question.
Analyzing millions of answers posted over the period from 2008 to 2014, we
uncover regular short-term and long-term changes in quality. In the short-term,
quality deteriorates over the course of a single session, with each successive
answer becoming shorter, with fewer code lines and links, and less likely to be
accepted. In contrast, performance improves over the long-term, with more
experienced users producing higher quality answers. These trends are not a
consequence of data heterogeneity, but rather have a behavioral origin. Our
findings highlight the complex interplay between short-term deterioration in
performance, potentially due to mental fatigue or attention depletion, and
long-term performance improvement due to learning and skill acquisition, and
its impact on the quality of user-generated content
Detecing Anti-Vaccine Users on Twitter
Vaccine hesitancy, which has recently been driven by online narratives,
significantly degrades the efficacy of vaccination strategies, such as those
for COVID-19. Despite broad agreement in the medical community about the safety
and efficacy of available vaccines, a large number of social media users
continue to be inundated with false information about vaccines and are
indecisive or unwilling to be vaccinated. The goal of this study is to better
understand anti-vaccine sentiment by developing a system capable of
automatically identifying the users responsible for spreading anti-vaccine
narratives. We introduce a publicly available Python package capable of
analyzing Twitter profiles to assess how likely that profile is to share
anti-vaccine sentiment in the future. The software package is built using text
embedding methods, neural networks, and automated dataset generation and is
trained on several million tweets. We find this model can accurately detect
anti-vaccine users up to a year before they tweet anti-vaccine hashtags or
keywords. We also show examples of how text analysis helps us understand
anti-vaccine discussions by detecting moral and emotional differences between
anti-vaccine spreaders on Twitter and regular users. Our results will help
researchers and policy-makers understand how users become anti-vaccine and what
they discuss on Twitter. Policy-makers can utilize this information for better
targeted campaigns that debunk harmful anti-vaccination myths
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