Carlos Castillo (link) and I presented a poster at StanCon (link) in Helsinki this week. The poster discussed our research-in-progress on our algorithmic fairness project, which looks into university admission criteria.
Aris Gionis and I presented the polarization tutorial at KDD this year (prepared jointly with Kiran and Gianmarco).
Slides are available here.
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.
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.
Our paper ‘Efficient Markov-Chain Monitoring’ with Harshal Chaudhari and Evimaria Terzi will appear at the SIAM International Conference on Data Mining (SDM) 2018.
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.
Attending the Complex Networks conference in Lyon.
During the visit, I gave a presentation on machine learning, which included basic concepts, software, and a hands-on session with scikit-learn and tensorflow. You can find the material of the session on github.
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.
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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