Newsroom Fact-Checking Strategies to Counter Electoral Disinformation: Evidence from Indonesia’s 2024 Presidential Race

Abstract

Electoral disinformation threatens democracy integrity, particularly where public trust in the media is low. Indonesia exemplifies this vulnerability. This study investigates newsroom fact-checking strategies to counter disinformation during Indonesia’s 2024 presidential election campaign period. It focuses on two Indonesian news outlets, Tempo.co and Liputan6.com, both are signatories of the International Fact-Checking Network (IFCN) and founding members of the Indonesian Fact-Checking Coalition, Cek Fakta. Existing studies on fact-checking practices during elections predominantly focused on the US and Europe, and comparative studies on newsroom fact-checking practices during elections are rare. This study addresses a significant scholarly gap by examining how newsrooms in the Global South, particularly Southeast Asia employ fact-checking strategies and adopt Artificial Intelligence (AI) tools to counter electoral disinformation. A mixed-method approach was employed, combining quantitative content analysis to examine 194 election-related fact checks published by Tempo.co and Liputan6.com, as well as reflexive thematic analysis to interpret 18 in-depth interviews with fact-checkers from both news outlets. The analysis integrates Framing Theory and Adaptive Structuration Theory to construct its theoretical framework. This research lends further weight to existing critiques that expose the epistemological issues in fact-checking initiatives. The lack of standards for conclusion-drawing by newsroom fact-checkers calls into question the consistency and accountability of fact-checking practices. The finding also reflects a tendency of fact-checkers to prioritise elite approval over substantive policy debate. Substantial challenges arose from time constraints and the complexity in drawing definitive verdicts during live fact-checking events. Furthermore, the use of AI is limited to the initial stages of the fact-checking process, particularly in disinformation detection and AI-assisted verification. Human expertise remains central to the analysis and writing up of the fact-checking results. Finally, the author proposes a newsroom fact-checking debunking model which can be transferable to any newsroom context—local, national, or cross-border—working to counter disinformation during elections.