Aris Gionis and I presented the polarization tutorial at KDD this year (prepared jointly with Kiran and Gianmarco).
Slides are available here.
Tag Archives: polarization
The Ebb and Flow of Controversial Debates on Social Media
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 (
View original post 526 more words
Best Student Paper award at Websci 2017
We did it again! 🙂
We received the Best Student Paper award at the Web Science 2017 conference.
The paper is ““The Effect of Collective Attention on Controversial Debates on Social Media”, by Kiran, Gianmarco, Aris, and myself.
The paper is publicly available on Arxiv (link).
Invited Talk at MLLN, Anchorage, Alaska
I just gave a talk on ‘Measuring polarization on social media’ at the MLLN Workshop in Anchorage, Alaska, collocated with IJCNN. Many thanks to Izabela Moise and Nino Antulov-Fantulin for the invitation.
The slides are here.
‘Polarization’ demo accepted at WWW 2017.
At PyCon Finland
Yesterday, I attended PyCon Finland as a speaker. Kiran and I gave a talk on using networkx to visualize interactions on Twitter. The talk was aimed at beginner / intermediate-level programmers and we described, essentially, how we produced the plots that appear in our work¹ on polarization on social media.
The Jupyter notebook for our slides is here.
Update: The video of our talk is posted on YouTube.
In a parallel session, Clemens also presented coding material from his masters thesis on text classification.
¹Garimella K, De Francisci Morales G, Gionis A, Mathioudakis M., Quantifying controversy in social media. InProceedings of the Ninth ACM International Conference on Web Search and Data Mining 2016 Feb 8 (pp. 33-42). ACM.
michael mathioudakis 