Polarization on Social Media

ICWSM'17 Tutorial


Which topics spark the most heated debates on social media? Identifying those topics is not only interesting from a societal point of view, but also allows the filtering and aggregation of social media content for disseminating news stories. For example, experience suggests that major political events, such as a vote for healthcare law in the US, would spark more debate between opposing sides than other events, such as a concert of a popular music band.

Social media offers a unique tool to explore these topics of discussion. Understanding which topics are controversial is useful for several purposes, such as for journalists to understand what issues divide the public, or for social scientists to understand how controversy is manifested in social interactions.

In this tutorial, we present a systematic methodological review of controversy detection. We review how the content and the network structure of social media can be used to reveal and measure polarization. The tutorial concludes by presenting open challenges and promising research directions for researchers interested in this area.


The slides are available here

For convenience, we also split the slides into different sections:

    Part1: Introduction and social theories behind Polarization

    Part2: Case studies on Polarization on the web

    Part3: Quantifying Polarization

    Part4: Reducing Polarization

    Part5: Conclusions

A complete list of references is available here.

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Kiran Garimella

Kiran Garimella is a PhD student at Aalto University. His research focuses on identifying and combating filter bubbles on social media. Previously he worked as a Research Engineer at Yahoo Research, QCRI and as an Research Intern at LinkedIn and Amazon. His research on reducing polarization on social media received the best student paper award at WSDM 2017.

Twitter: @gvrkiran


Gianmarco De Francisci Morales is a Scientist at QCRI. Previously he worked as a Visiting Scientist at Aalto University in Helsinki, as a Research Scientist at Yahoo Labs in Barcelona, and as a Research Associate at ISTI-CNR in Pisa. He received his Ph.D. in Computer Science and Engineering from the IMT Institute for Advanced Studies of Lucca in 2012. His research focuses on scalable data mining, with an emphasis on Web mining and data-intensive scalable computing systems. He is an active member of the open source community of the Apache Software Foundation, working on the Hadoop ecosystem, and a committer for the Apache Pig project. He is one of the lead developers of Apache SAMOA, an open-source platform for mining big data streams. He co-organizes the workshop series on Social News on the Web (SNOW), co-located with the WWW conference.

Twitter: @gdfm7

Michael Mathioudakis

Michael Mathioudakis is a Postdoctoral Researcher at Aalto University. He received his PhD from the University of Toronto. His research focuses on the analysis of user generated content on social media, with a recent emphasis on urban computing and online polarization. At Aalto University, he organized and taught new courses on 'Modern Database Systems' and 'Social Web Mining'. He also serves as advisor to Master's students and Aalto's representative at the SoBigData EU project. Outside academia, he works as a data scientist at Helvia and Sometrik, two data analytics companies.

Twitter: @mmathioudakis

Aristides Gionis

Aristides Gionis is an associate professor at Aalto University. His research focuses on data mining and algorithmic data analysis. He is particularly interested in algorithms for graphs, social-network analysis, and algorithms for web-scale data. Since 2013 he has been leading the Data Mining Group, in the Department of Com- puter Science of Aalto University. Before coming to Aalto he was a senior research scientist in Yahoo! Research, and previously an Academy of Finland postdoctoral scientist in the University of Helsinki. He obtained his Ph.D. from Stanford University in 2003.

Twitter: @gionis

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  1. An initial set of references can be found here.
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