Social Media and Malaysia’s 2022 Election: The Growth and Impact of Video Campaigning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article argues that Malaysia's 2022 General Election (GE15) amplified negative campaigning via new techniques associated with platform and technological advancements, led by creative innovations in campaign tactics, including livestreaming and video content. GE15 was the freest election campaign in Malaysia's history. All political parties and coalitions enjoyed access to a wide range of mainstream and online media to disseminate content, and new platforms like TikTok emerged as influential conduits of campaign messages. Yet serious problems in this digital public sphere remain a feature of the country's media landscape. These include cybertroopers, fake news peddlers, and those creating polarizing content around race and religious issues. This article explains how social media campaigning in Malaysia is becoming more professionalized and better resourced, inspiring some diversity and creativity, while at the same time enabling groups who spread narratives intended to incite and enrage, particularly via video content. The Malaysian case exemplifies the growing problems within the contemporary digital public sphere, showing how the professionalization of social media campaigning can lead to disinformation and, ultimately, polarization.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it