We May Disagree, but We All Love the BJP: Populists’ Networks and Targeting Opportunities on Twitter
Bibliographic record
Abstract
With the rise of social media, politicians face new challenges and opportunities in presenting their messages online to large and diverse constituencies. Populists especially have capitalized on social media to achieve wide electoral success. This paper explores populists’ opportunities to target their constituencies online using the case of India’s Bharatiya Janata Party (BJP) on Twitter. First, the structure of the BJP’s network on Twitter is analyzed through social network analysis. Second, a content analysis is used to examine the messages of important accounts in different networks clusters. The findings suggest the ruling party in India has the opportunity to employ microtargeting to siloed clusters within its network of supporters. This practice could damage democratic discourse by reducing the possibility of debate in the public square.
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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 | 0.012 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.013 | 0.032 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.010 | 0.026 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".