Do Wedge Narratives Persuade? Experimental Evidence from the Hong Kong Diaspora in the UK

Authoritarian actors increasingly employ transnational communication to influence politics abroad. Targeting diasporas, wedge narratives---emphasizing the routine discrimination and exclusion migrants face in democratic host societies---are one documented strategy for weakening diasporas’ host-country attachment. How do they shape political attitudes? We present the first experimental investigation using a unique sample of the Hong Kong diaspora based in the UK. Respondents were randomly assigned to view real-world wedge narratives---or politically neutral placebos. Contrary to expectations of wedge narratives' persuasive power, results showed neither reduced political or psychological attachment to the host society nor strengthened attachment to the homeland. Instead, respondents exposed to wedge narratives reported lower rather than higher levels of perceived social exclusion. Qualitative evidence suggests that persuasion failed because the diaspora community actively challenged the messages' content and their source credibility by drawing from their pre-migration political experiences and prior exposure to authoritarian communication. Our findings elucidate the conditions under which transnational propaganda may be resisted and demonstrate the resilience of diasporas after migration.

 

Maggie Shum is an Assistant Professor of Political Science at Penn State Behrend (US). Her scholarship in comparative political behavior and transnational politics examines the consequence of autocratization within East Asia and across borders through the study of diaspora and migration politics. Shum is the principal investigator for the Hong Kong Diaspora Studies Initiative (HKDSI). Her research has been published in Democratization, Japanese Journal of Political Science, Journal of Asian and African Studies and forthcoming in Political Behavior. She is also the Deputy Coordinator of Global Research Association of Political in Hong Kong (GRAPH).