Social Data Analytics for Forecasting Electoral Outcomes
Khalid Ait Hadi1, Rafik Lasri2, Abdellatif El Abderrahmani3

1Khalid Ait Hadi: Laboratory of Sciences and Advanced Technologies, Department of Computer Sciences, Polydisciplinary Faculty of Larache, Abdelmalek Essaadi University, Morocco.
2Rafik Lasri: Laboratory of Sciences and Advanced Technologies, Department of Computer Sciences, Polydisciplinary Faculty of Larache, Abdelmalek Essaadi University, Morocco.
3Abdellatif El Abderrahmani: Laboratory of Sciences and Advanced Technologies, Department of Computer Sciences, Polydisciplinary Faculty of Larache, Abdelmalek Essaadi University, Morocco.
Manuscript received on 01 June 2019 | Revised Manuscript received on 05 June 2019 | Manuscript published on 30 June 2019 | PP: 2468-2471 | Volume-8 Issue-8, June 2019 | Retrieval Number: H7085068819/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Social networks are ubiquitous, so much so that they are used to conduct polls on various societal issues. Thus, multiple domains have been subject of studies aimed at making predictions based on signals captured on online social networks. Recently, many works have focused on exploring the potential of social media platforms to capture social trends and forecast voting outcomes in electoral consultations. This paper comes in this wake and aims at presenting a strategy of predicting election results using big data collected from the Twitter platform.
Keywords: Big Data, Social Networks, Elections, Prediction, Bidirectional Gated Recurrent Unit, Convolutional Neural Network.

Scope of the Article: Social Networks