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Free keywords:
cs.SI, Physics, Physics and Society, physics.soc-ph
Abstract:
Users of social media sites like Facebook and Twitter rely on crowdsourced
content recommendation systems (e.g., Trending Topics) to retrieve important
and useful information. Contents selected for recommendation indirectly give
the initial users who promoted (by liking or posting) the content an
opportunity to propagate their messages to a wider audience. Hence, it is
important to understand the demographics of people who make a content worthy of
recommendation, and explore whether they are representative of the media site's
overall population. In this work, using extensive data collected from Twitter,
we make the first attempt to quantify and explore the demographic biases in the
crowdsourced recommendations. Our analysis, focusing on the selection of
trending topics, finds that a large fraction of trends are promoted by crowds
whose demographics are significantly different from the overall Twitter
population. More worryingly, we find that certain demographic groups are
systematically under-represented among the promoters of the trending topics. To
make the demographic biases in Twitter trends more transparent, we developed
and deployed a Web-based service 'Who-Makes-Trends' at
twitter-app.mpi-sws.org/who-makes-trends.
