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The emergence of the radical right signals that social norms and values are changing. Existing literature suggests that citizens choose to voice their concerns when faced with the erosion of democracy. In this paper, we look at the consequences of citizens using quoted tweets to express negative sentiments to denounce and discredit the radical right. Using Twitter data from Portugal, we use node embeddings to map out interactions on social media. Subsequently, we estimate a deep-learning automated sentiment analysis of quoted tweets and use a vector auto-regression model to forecast who contributes the most to the growth of the radical right on Twitter. Our findings show that users amplify the radical right’s original message via weak ties and cascade effects in making negative quoted tweets. Ultimately, denouncing the radical right backfires and helps nascent illiberal parties to reach out to more users in the network and gain more users.

The Unintended Consequences of Amplifying the Radical Right on Twitter