Social Media and Malaysia’s 2022 Election: The Growth and Impact of Video Campaigning
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".