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Quantifying Controversy on Social Media
A systematic methodological study of controversy detection by using the content and the network structure of social media and a new random-walk-based measure outperforms existing ones in capturing the intuitive notion of controversy and shows that content features are vastly less helpful in this task.
Political Discourse on Social Media: Echo Chambers, Gatekeepers, and the Price of Bipartisanship
It is found that Twitter users are, to a large degree, exposed to political opinions that agree with their own, and users who try to bridge the echo chambers have to pay a »price of bipartisanship» in terms of their network centrality and content appreciation.
A Long-Term Analysis of Polarization on Twitter
The analysis of a large longitudinal Twitter dataset of 679,000 users shows that online polarization has indeed increased over the past eight years and that, depending on the measure, the relative change is 10%-20%.
Quantifying Controversy in Social Media
This paper performs a systematic methodological study of controversy detection using social media network structure and content, and finds that a new random-walk-based measure outperforms existing ones in capturing the intuitive notion of controversy.
Reducing Controversy by Connecting Opposing Views
This paper presents a simple model based on a recently-developed user-level controversy score, that is competitive with state-of-the-art link-prediction algorithms and proposes an efficient algorithm that considers only a fraction of all the possible combinations of edges.
Images and Misinformation in Political Groups: Evidence from WhatsApp in India
A large collection of politically-oriented WhatsApp groups in India is studied, focusing on the period leading up to the 2019 Indian national elections, to find that around 13% of shared images are known misinformation and most fall into three types of images.
WhatsApp, Doc? A First Look at WhatsApp Public Group Data
This paper presents a generalisable data collection methodology, and a publicly available dataset for use by other researchers, to explore the feasibility of collecting and using WhatsApp data for social science research.
Secular vs. Islamist polarization in Egypt on Twitter
This work uses public data from Twitter, both in English and Arabic, to study the phenomenon of secular vs. Islamist polarization in Twitter and provides a quantitative and data-driven analysis of online communication in this dynamic and politically charged part of the world.
Political hashtag hijacking in the U.S.
We study the change in polarization of hashtags on Twitter over time and show that certain jumps in polarity are caused by "hijackers" engaged in a particular type of hashtag war.
A Dataset of Fact-Checked Images Shared on WhatsApp During the Brazilian and Indian Elections
An extensive data collection from a large set of WhatsApp publicly accessible groups and fact-checking agency websites is performed for two distinct scenarios known for the spread of fake news on the platform: the 2018 Brazilian elections and the 2019 Indian elections.