Élysée2017fr
The 2017 French Presidential Campaign on Twitter
Bibliographic Data
| ID | 7523707 |
|---|---|
| Authors | Ophélie Fraisier (0000-0003-2974-5308, Centre National de la Recherche Scientifique, corresponding author), Guillaume Cabanac (0000-0003-3060-6241, Centre National de la Recherche Scientifique), Yoann Pitarch (0000-0002-1508-5436, Centre National de la Recherche Scientifique), Romaric Besançon (0000-0003-1331-5768, Commissariat à l'Énergie Atomique et aux Énergies Alternatives), Mohand Boughanem (0000-0001-7004-0807, Centre National de la Recherche Scientifique) |
| Year | 2018 |
| Volume | 12 |
| Issue | 1 |
| Publication date | 2018-06-15 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Proceedings of the International AAAI Conference on Web and Social Media (JOURNAL) |
| Journal identifiers | ISSN: 2162-3449 • E-ISSN: 2334-0770 |
| Publisher | Association for the Advancement of Artificial Intelligence (AAAI) (PUBLISHER) |
| DOI | 10.1609/icwsm.v12i1.14984 |
| OpenAlex | W2810266289 |
| Language | FR |
| Citations received | 6 |
| References cited | 9 |
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 datasets 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
Data science · Event (particle physics · Fake news · Internet privacy · Political science · Politics · Presidential campaign · Presidential election · Presidential system · Social media · World Wide Web · Complex Network Analysis Techniques · Computer Science · Law · Opinion Dynamics and Social Influence
Behavioral differences
La controverse de Didier Raoult et de sa proposition thérapeutique contre la Covid-19 sur Twitter
Les téléphones mobiles, un outil de désinformation ? La circulation des informations peu fiables dans Twitter lors de la campagne présidentielle française de 2017
Information-sharing practices on Facebook during the 2017 French presidential campaign
Constrained expectation-maximisation for inference of social graphs explaining online user–user interactions
Structuration des discours au sein de Twitter durant l'élection présidentielle française de 2017
Assortative Mixing in Networks
Predicting Elections with Twitter
Programmed method
Echo Chamber or Public Sphere? Predicting Political Orientation and Measuring Political Homophily in Twitter Using Big Data
Understanding the Political Representativeness of Twitter Users
Why the Pirate Party Won the German Election of 2009 or The Trouble With Predictions
Inter-Coder Agreement for Computational Linguistics
Measuring nominal scale agreement among many raters
| Unique citing works | 6 |
|---|---|
| Citations per year | 0,86 |
| Citation span | 2019 - 2023 (5) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 5 |