Hegemonic Media and Polarized Discourses: An Analysis of Disinformation Narratives in Indian Media
Bibliographic record
Abstract
There is a growing concern that disinformation, particularly transmitted through mobile phones, television, and social media, is fuelling social tension and even violence at times. Ample evidence, along with anecdotal interpretations, is available to illustrate how digital and mainstream media platforms in India have turned into pernicious channels for spreading hatred and violence. The proposed study investigates how propaganda, disinformation and fake news are used as polarization tools to elevate a particular political ideology or practice in a democracy like India. The study highlights a series of events and hashtags used on X (earlier Twitter) and other social platforms using derogatory language or agendas against communities and people of dissent. The media is wielded as a tool to spread polarized discourses that serve the interest of the ruling dispensation. Using some examples of fake news and propaganda, this paper draws attention to media propaganda and its effects on minorities, Muslims in particular, and human intellect in general. The research looks at how political goals shape propaganda definitions and signs, symbols, or languages used against minorities, who are largely portrayed as ‘others’ in the country. Additionally, it delves into the strategies and techniques employed by prejudiced media to shape reality, as well as how they undermine cultural norms and promote divisive policies of the ruling regime. The investigation of specific texts, videos, and audios will support the study. The findings reveal that such propagandised media or sources of information can have both positive and negative effects on the democratic process. The study uses some cases as examples to show how disinformation or media manipulation restricts the freedom of expression, food habits, mobility, and affiliation of the country's minorities. This is troubling because stigma can lead to more hostile attitudes toward integration or weaken one's sense of belonging in democracies.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".