Fear-anger cycles: Governmental and populist politics of emotion

Unlike public intellectuals and academic scholars who claim that populists thrive on fear of minority groups (e.g. Michael Moore, Martha Nussbaum), we suggest that what they thrive on is anger against governmental actors. It is governmental actors who increasingly rely on fear, be it to pursue policy objectives or to keep populists at bay. The Brexit referendum, election of Donald Trump, and COVID-19 are cases in point. In a battle for the hearts and minds of the people, governmental and populist actors send fear and anger signals, respectively. We theorize this contest of fear and anger as the fear-anger cycle and trace it in concrete manifestations.

By way of example: In the case of COVID-19, governmental actors, which may include politicians from oppositional parties such as Labour in the UK, sent fear signals related to real and constructed danger. These fear signals resonated percolated through mainstream media and resonated with citizens, translating into more support for governmental actors. To the extent that fear-driven policies have induced significant dislocation and frustration among the populace (economic recession, job losses etc.), the politics of anger has been hitting back with a vengeance.

We apply machine learning and sentiment analysis to social and public media data obtained from Twitter and GDELT, as well as events and public opinion data, to investigate the following set of hypotheses.

  • 1a. Governmental actors predominantly send fear signals.
  • 1b. Populist actors predominantly send anger signals.
  • 2. In the public sphere, there is a negative correlation between receptiveness to fear signals on the one hand and anger signals on the other.
  • 3a. Public receptiveness to fear translates into citizen support for governmental actors
  • 3b. Public receptiveness to anger translates into citizen support for populist actors.

 

 

Researchers
Jörg Friedrichs
Associate Professor of Politics
Niklas Stoehr
Virtual Academic Visitor, ODID, and PhD Candidate, Institute for Machine Learning, ETH Zurich
Funder(s):