Media Sosial TikTok dalam Fenomena Matinya Kepakaran (Studi Netnografi pada Dokumenter “Dirty Vote”)

Social media algorithms allow for information bias, leading to what Tom Nichols calls the “death of expertise”. In social media, expertise tends to be viewed sceptically. Instead, influencers and buzzers are often used as references even though their traces of expertise are not clear and proven. “Dirty Vote”, a political documentary featuring experts as the main actors shows this phenomenon. Experts, in this context, are not considered objective and independent. This research will explain the phenomenon of the “death of expertise” by describing comments reflected in the political documentary “Dirty Vote” on TikTok social media. Through the qualitative netnography method, this research takes comment data from 3 accounts with the top searches related to the short video “Dirty Vote”. The study shows that the phenomenon of “death of expertise” is true on TikTok with several highlights: (1) the inability of metacognition that tends to corner the role of experts and believe in things beyond substance; (2) the lack of digital literacy that uses provocative language, lead opinions and denigrates expertise; (3) the emergence of repeated comments that are indicated to be systematic and come from political buzzers who counter opinions. Openness is needed, and people on social media should be more open when viewing information. This phenomenon can be developed in other social media domains using different methods.

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Publication Details

Journal
Ranah Research Journal of Multidisciplinary Research and Development
Published
2025-03-02
DOI
https://doi.org/10.38035/rrj.v7i3.1400
Primary Topic
Educational Methods and Impacts
Type
article
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article

Media Sosial TikTok dalam Fenomena Matinya Kepakaran (Studi Netnografi pada Dokumenter “Dirty Vote”)

Cut Meutia Karolina, Muhammad Shaddam Rizqullah, Gusmia Arianti, Nanang Haroni
Ranah Research Journal of Multidisciplinary Research and Development
Educational Methods and Impacts
article

Media Sosial TikTok dalam Fenomena Matinya Kepakaran (Studi Netnografi pada Dokumenter “Dirty Vote”)

Cut Meutia Karolina, Muhammad Shaddam Rizqullah, Gusmia Arianti, Nanang Haroni
article en

Abstract

Social media algorithms allow for information bias, leading to what Tom Nichols calls the “death of expertise”. In social media, expertise tends to be viewed sceptically. Instead, influencers and buzzers are often used as references even though their traces of expertise are not clear and proven. “Dirty Vote”, a political documentary featuring experts as the main actors shows this phenomenon. Experts, in this context, are not considered objective and independent. This research will explain the phenomenon of the “death of expertise” by describing comments reflected in the political documentary “Dirty Vote” on TikTok social media. Through the qualitative netnography method, this research takes comment data from 3 accounts with the top searches related to the short video “Dirty Vote”. The study shows that the phenomenon of “death of expertise” is true on TikTok with several highlights: (1) the inability of metacognition that tends to corner the role of experts and believe in things beyond substance; (2) the lack of digital literacy that uses provocative language, lead opinions and denigrates expertise; (3) the emergence of repeated comments that are indicated to be systematic and come from political buzzers who counter opinions. Openness is needed, and people on social media should be more open when viewing information. This phenomenon can be developed in other social media domains using different methods.

Ranah Research Journal of Multidisciplinary Research and DevelopmentVol. 7(3)
Al-Azhar University (ID)
Openalex Percentile: Top 96%
Educational Methods and Impacts
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Media Sosial TikTok dalam Fenomena Matinya Kepakaran (Studi Netnografi pada Dokumenter “Dirty Vote”) — Cut Meutia Karolina, Muhammad Shaddam Rizqullah, et al. · Ranah Research Journal of Multidisciplinary Research and Development (2025) | TGRS Research Map | TGRS