THE IMPACT OF SOCIAL MEDIA ALGORITHMS ON THE AMPLIFICATION OF #END-BADGOVERNANCE PROTESTERS' VOICES IN NIGERIA
Social media platforms such as WhatsApp, Facebook, X (formerly Twitter), and Instagram have become significant tools for global communication, self-expression, and collective action. They providee spaces where citizens can voice demands for social change. In Nigeria, August 2024 #EndBadGovernance protests underscored the role of social media in galvanizing mass movements. This research investigates the impact of social media algorithms on the amplified voices of #EndBadGovernance protesters. The study specifically aims to evaluate the extent of social media engagement during the protests, analyze how algorithmic amplification influenced the visibility of protest-related content, and identify the key messages that contributed to mobilizing the protesters. The theoretical framework is grounded in public sphere theory, alongside the logic of collective and connective action, to better understand how social media platforms foster collective dissent. A quantitative research design was adopted, with data collected through structured questionnaires from participants in the Karu and Jos North Local Government Areas in Nasarawa and Plateau States, respectively. The findings revealed that social media was instrumental in mobilizing protesters, with algorithm-driven amplification significantly increasing the visibility and reach of protest-related posts. Key messages that galvanized the protests focused on issues such as the high cost of governance, corruption, inadequate educational and health care systems, widespread poverty, and hunger. This study highlights social media’s critical role as a powerful tool for citizens to demand accountability and good governance. It calls for responsible and ethical use of these platforms to ensure that legitimate concerns are effectively communicated while adhering to civic responsibility
Institutions
- Plateau State University (NG)
- Nasarawa State University (NG)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2024-12-02
- DOI
- https://doi.org/10.5281/zenodo.14261180
- Primary Topic
- Genomics and Phylogenetic Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00