Characterizing the interactions between politicians and voters in the era of social media.

Keywords: Twitter | Natural Language Processing | Deep Learning | Graph theory
Project coordinator: Stefanie Bailer
CeDA collaborator: Sébastien Boyer

Project objectives

• Characterizing the different flavors of interactions (positive, negative, offensive, etc...) between politicians and the general public on social media.

Approaches

• Multilingual context dependent word embedding for sentences classification.
• Graph representation of social media interaction.

Analytical methods

• Transformer-based Deep Neural Networks: Multilingual BERT or XML-RoBERTa.
• Graph Convolutional Neural Networks.

Figure 1. Tweets sentiment analysis based on context dependent word embedding, allows us to draw with confidence, directed edges representing emotional flow between politician tweets.¶