Bibliographic record
Abstract
Studies have found that artificial intelligence (AI) bots and cookies automate fake news in zones of social conflict such as race, religion, gender, and class. In this background, this paper investigates whether fake news is automated with the social structure unique to India. The research collected campaigning activities of political parties and politicians on the Internet but was limited to a select number of Facebook profiles, websites, hashtags, and Twitter profiles during India’s 2014 and 2019 general elections. Politicians and political parties on Twitter, Facebook and other websites formed the contact points where empirical data were collected in the research design. By reviewing hashtags such as #Nationwantsrammandir; #NaamVaapsi; #RamMandir; #AntiNationals; #caste; and #Hindutva, as well as fake social media accounts; discussion forums; and profiles of followers of politicians, the paper corroborated that bots, AI, and trolls serve fake news in the conflict zones of India and some forces are using it to perpetuate social divisions based on caste, class, religion, gender, and region. This paper argues that automated social media accounts spread false information that likely polarizes social conflicts in India.
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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 | 0.004 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".