Bibtex de la publication

@InProceedings{ Fr2017.3,
author = {Fraisier, Ophélie and Cabanac, Guillaume and Pitarch, Yoann and Besancon, Romaric and Boughanem, Mohand},
title = "{Uncovering Like-minded Political Communities on Twitter (short paper)}",
booktitle = "{International Conference on the Theory of Information Retrieval (ICTIR 2017), Amsterdam, 01/10/17-04/10/17}",
editor = {Kamps, Jaap and Kanoulas, Evangelos and de Rijke, Maarten},
year = {2017},
month = {octobre},
publisher = {ACM : Association for Computing Machinery},
address = {},
pages = {261--264},
language = {anglais},
URL = {,%20,%20 -},
keywords = {Stance detection, Social media, Benchmarking},
note = {Taux de sélection des articles courts: 58,0% (25/43 soumissions).},
abstract = {Stance detection systems often integrate social clues in their algorithms. While the influence of social groups on stance is known, there is no evaluation of how well state-of-the-art community detection algorithms perform in terms of detecting like-minded communities, i.e. communities that share the same stance on a given subject. We used Twitter's social interactions to compare the results of community detection algorithms on datasets on the Scottish Independence Referendum and US Midterm Elections. Our results show that algorithms relying on information diffusion perform better for this task and confirm previous observations about retweets being better vectors of stance than mentions.}