Autocratization and Media Responses to Government Repression of Journalism: Machine Learning Evidence from Tanzania.

(With Serkant Adiguzel and Erik Wibbels). Working paper.

Recommended citation: One crucial feature of the ongoing global wave of democratic backsliding is that aspiring autocrats seek to influence the media, oftentimes through legal restrictions on the press and social media. Yet little research has examined how formal and social media respond to those legal restrictions targeting the free flow of information. We develop an original argument linking key characteristics of media sources to the regulatory environment and examine how the content and sentiment of their coverage responds to restrictive media laws. We test our claims using an enormous corpus of electronic media in Tanzania and employ two state-of-the-art neural network models to classify the topics and sentiment of news stories. We then estimate diff-in-diff models exploiting a significant legal change that targeted media houses. We find that critical news sources censor the tone of their coverage, even as they continue to cover the same issues; we also find that international news sources are unable to fill the hole left by a critical domestic press. The paper sheds light on the conditions under which the press can be resilient in the face of legal threats.