Bibliographic record
Abstract
Abstract Since approximately 2003−2004, social media and technology platforms have become central infrastructures for political processes around the world. In many countries, technology and social media platforms such as Facebook, Google, Instagram, YouTube, and TikTok play a central role in public debate, social movements, governmental communication, public diplomacy, terrorism, war, campaigns, elections, governance, and policymaking, in addition to many other domains. As such, how these companies are governed, monetize content and users, set policies, and design technologies has outsized implications for global and national public spheres and political processes, including electoral institutions. These platforms are embedded in political, social, and economic systems that influence how they are governed and used—and which they in turn shape. Platforms have a complicated past when it comes to regulating political content. The role of mis/disinformation on social media platforms in the wake of the international Cambridge Analytica scandals and the rise of populist, authoritarian, or anti-democratic leaders and parties led to new global calls for increased regulation of these technologies once thought to be tools of liberation and democracy. Between 2016 and 2020, platforms responded by instituting a number of different content-moderation policies, strategies, and partnerships to address issues surrounding their role in democratic politics. Platforms took more seriously the propagation of conspiratorial content, the correction of misleading information, and the policing of content that undermined electoral processes. Since that time, platforms have generally rolled back trust and safety efforts, with stated justifications of freedom of expression. This article provides an overview of what is known about social media platforms and democratic processes, including their social and psychological effects. It analyzes platform policies belonging to Meta (Facebook, Instagram, and WhatsApp), X, YouTube, and TikTok regarding political and election-related content in six countries (Australia, Canada, India, Mexico, the United Kingdom, and the United States) as well as the European Union—all places holding elections in 2024 or 2025. The article focuses on the civic integrity, artificial intelligence, fact-checking, and political advertising policies of these platforms, given their importance to global politics and elections.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.000 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 0.004 |
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".