Online civic engagement through social media: An analysis of twitter big data

Budi Mulyono, Universitas Pendidikan Indonesia, Indonesia
Idrus Affandi, Universitas Pendidikan Indonesia, Indonesia
Karim Suryadi, Universitas Pendidikan Indonesia, Indonesia
Cecep Darmawan, Universitas Pendidikan Indonesia, Indonesia

 10.21831/cp.v42i1.54201

Abstract


The development of social media and internet networks has stimulated new scientific interest in terms of online civic engagement, resulting in a redefinition of deliberative politics and the public sphere. On the other hand, all aspects of human life today have been translated into big data and recorded in their activities on social media. The shift from using humans as the unit of study to using algorithms creates a new problem for academics in social science. This online civic engagement research aims to find out the topic of discourse for digital citizens in the discourse of moving the nation's capital city (IKN) and what the ideology behind the discourse is by utilizing Twitter big data. This study used a mixed method with a sequential explanatory design type. The quantitative method used is topic modeling with the Latent Dirichlet Allocation (LDA) technique, while the qualitative method used is the Critical Discourse Analysis (CDA) method from Norman Fairclough, who sees language as a practice of power. Findings from the topic modeling analysis revealed that there were four themes in the IKN discourse: agreeing to move the state capital, objecting to move the state capital, supporting the discourse on moving the state capital, and the fourth theme, law and eradication of corruption. From the results of critical discourse analysis, it is known that there is a diametrical division in the IKN issue as a result of identity politics that emerged after the 2014 Presidential Election. In every discussion on the IKN discourse, there is a conflict between Islamic populism and Nationalist-Secular populism.

Keywords


online civic engagement; digital citizenship; social media; big data

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References


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DOI: https://doi.org/10.21831/cp.v42i1.54201

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