Bibtex de la publication

@InProceedings{ Fr2018.1,
author = {Fraisier, Ophélie and Cabanac, Guillaume and Pitarch, Yoann and Besancon, Romaric and Boughanem, Mohand},
title = "{#Élysée2017fr: The 2017 French Presidential Campaign on Twitter (regular paper)}",
booktitle = "{International Conference on Weblogs and Social Media (ICWSM), Stanford, California, États-Unis, 25/06/2018-28/06/2018}",
year = {2018},
month = {juin},
to_appear = {to appear},
publisher = {AAAI Press},
address = {http://www.aaai.org/Press/press.php},
pages = {},
language = {anglais},
URL = {https://www.irit.fr/publis/IRIS/2018_ICWSM_FCPBB.pdf},
keywords = {Twitter, dataset, stance, political expression, French presidential election},
abstract = {The French presidential election was one of the main political event of 2017, and triggered a lot of activity on Twitter. The campaign was highly unpredictable and led to the rise of 5 main parties instead of the historical bipartite (left-right) confrontation, ranging from far-left to far-right. This dataset paper proposes #Élysée2017fr, a large and complex dataset of 22853 Twitter profiles active during the campaign (from November 2016 to May 2017), and their corresponding tweets and retweets, plus the retweet and mention networks related to these profiles. The profiles were manually annotated with their political affiliations (up to 2 political parties per profile), their nature (individual or collective), and the sex of the profile's owner when available. This is one of the rare dataset that considers a non-binary stance classification and, to our knowledge, the first one with a large number of profiles and the first one proposing overlapping political communities. This dataset can be used as-is to study the campaign mechanisms on Twitter, or used to test stance detection models or network analysis tools. Mining these data might reveal new insights on current issues like echo chambers or fake news diffusion.}
}