Mapping The Digital Movement in The Hashtags #2024AniesPresiden, #GanjarPresiden and #PrabowoPresiden

Restia Tities Asmawarini, Endah Murwani, Bertha Sri Eko Murtiningsih

Abstract


Hashtag mobilization has currently become one of the ways to mobilize opinion. As the 2024 presidential election draws nearer, hashtags which are considered effective to mobilize support for presidential candidates in 2024 have emerged and have been widely discussed on Twitter, using different hashtags, namely #2024AniesPresiden, #GanjarPresiden, and #PrabowoPresiden. This research uses Theory of Digital Movement of Opinion, a type of digital activism characterized by spontaneous and disorganized reactions from social media users on Twitter. This research aims to map those three hashtags: can different hashtags produce different mobilizations? The research method is descriptive and quantitative with social network analysis conducted by taking data from the three hashtags in the period from April 29, 2022 to May 12, 2022. This study found 235 accounts talking about #2024AniesPresiden, 22 accounts talking about #GanjarPresiden, and eight accounts talking about #PrabowoPresiden, where data was processed using NodeXL software. The base and its mappings are related to the diameter, density, and centrality (betweenness, closeness, and degree). The @SMILEINDO24 in the hashtag #2024AniesPresiden, the @ganjar_center in the hashtag #GanjarPresiden, and the @djagger17 in the hashtag #PrabowoPresiden are also found as the central or most influential actors in disseminating information and serving as a liaison. In contrast, the @AniesIndonesia in the hashtag #2024AniesPresiden, the @ganjar_center in the hashtag #GanjarPresiden, and the @prabowo in the hashtag #PrabowoPresiden are the critical actors in the formation of communication networks and information flow. The findings of this study have implications on dominant actors that can be used or exploited by policymakers so that these actors can influence public opinion and mobilize opinion movements.


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DOI: https://doi.org/10.25008/jkiski.v7i2.727



      




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