1,143 research outputs found
Clustering and Classification of Like-Minded People from Their Tweets
International audienceSeveral challenges accompanied the growth of online social networks, such as grouping people with similar interest. Grouping like-minded people is of a high importance. Indeed, it leads to many applications like link prediction and friend or product suggestion, and explains various social phenomenon. In this paper, we present two methods of grouping like-minded people based on their textual posts. Compared to three baseline methods K-Means, LDA and the Scalable Multi-stage Clustering algorithm (SMSC), our algorithms achieves relative improvements on two corpora of tweets
Data Portraits and Intermediary Topics: Encouraging Exploration of Politically Diverse Profiles
In micro-blogging platforms, people connect and interact with others.
However, due to cognitive biases, they tend to interact with like-minded people
and read agreeable information only. Many efforts to make people connect with
those who think differently have not worked well. In this paper, we
hypothesize, first, that previous approaches have not worked because they have
been direct -- they have tried to explicitly connect people with those having
opposing views on sensitive issues. Second, that neither recommendation or
presentation of information by themselves are enough to encourage behavioral
change. We propose a platform that mixes a recommender algorithm and a
visualization-based user interface to explore recommendations. It recommends
politically diverse profiles in terms of distance of latent topics, and
displays those recommendations in a visual representation of each user's
personal content. We performed an "in the wild" evaluation of this platform,
and found that people explored more recommendations when using a biased
algorithm instead of ours. In line with our hypothesis, we also found that the
mixture of our recommender algorithm and our user interface, allowed
politically interested users to exhibit an unbiased exploration of the
recommended profiles. Finally, our results contribute insights in two aspects:
first, which individual differences are important when designing platforms
aimed at behavioral change; and second, which algorithms and user interfaces
should be mixed to help users avoid cognitive mechanisms that lead to biased
behavior.Comment: 12 pages, 7 figures. To be presented at ACM Intelligent User
Interfaces 201
Does Relational Polarization Entail Ideological Polarization? The Case of the 2017 Norwegian Election Campaign on Twitter
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