Michael

April 30, 2018

Kiran, Gianmarco, Aris and I attended the Web Conference in Lyon last week, where we presented our tutorial (link) on polarization on social media, as well as our recent, large-scale study on echo chamber on Twitter (link).
The tutorial was a particularly good opportunity to present a large number of works from the related literature of various disciplines and discuss with the audience. Part of the discussion was reported on Lemonde by William Audureau, who covered the conference for the newspaper.

Link to the Le Monde article: here (in French).
Link to tutorial material: here.
Link to Aalto press release: here.
Link to University of Helsinki‘s post: here.

Michael

December 22, 2017

Our paper ‘Political Discourse on Social Media: ‘Echo Chambers, Gatekeepers, and the Price of Bipartisanship’, with Kiran, Gianmarco and Aris, will be presented at the Web Conference (WWW2018) in Lyon this spring.

Michael

December 21, 2017

Our paper ‘Efficient Markov-Chain Monitoring’ with Harshal Chaudhari and Evimaria Terzi will appear at the SIAM International Conference on Data Mining (SDM) 2018.

Michael

December 4, 2017

Our tutorial on ‘Polarization on Social Media’ with Kiran, Gianmarco, and Aris will be hosted at the Web Conference (WWW2018) next year, in Lyon, France.

The Ebb and Flow of Controversial Debates on Social Media

Northern Bytes

By Kiran Garimella and Michael Mathioudakis

Our recent paper titled ‘The Effect of Collective Attention on Controversial Debates on Social Media’ (arXiv link) won the best student paper award at the 9th ACM Web Science conference held in Troy, New York.

The paper studies the evolution of long-lived controversial debates on Twitter – i.e., discussions on topics such as ‘gun control’ or ‘abortion’, that reveal a split of opinion between people who support different sides of the argument.

The main goal of this work is to study dynamic aspects of controversial debates — in particular: (i) whether controversy around the debates has increased over time; and (ii) whether controversy increases or decreases when major associated events occur.

Data

The dataset consists of an 1% sample of Twitter of all tweets generated between September 2011 and September 2016, as published by Twitter and stored on the Internet Archive (

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