The Influence of Mass Communication Intensity on the Formation of Public Opinion Among Voters in the 2024 Presidential Election in Indonesia
Bibliographic record
Abstract
This study examines the influence of mass communication intensity on the formation of public opinion among voters in Indonesia’s presidential election. In the context of rapid digital media development, political communication no longer relies solely on conventional media but is increasingly shaped by social media platforms that enable fast and widespread information dissemination. This research adopts a quantitative approach to explain the relationship between media exposure and public opinion formation. Data were collected through an online questionnaire distributed to voters who actively follow political information through mass media. The findings indicate that the intensity of mass communication has a strong and significant influence on how public opinion is formed. Frequent and sustained exposure to political information through media contributes to clearer attitudes, stronger political perceptions, and greater confidence in political choices. These results highlight the strategic role of mass media as a key agent in shaping political awareness and public discourse within a democratic society. The study emphasizes the importance of media literacy to help the public critically evaluate information, reduce the spread of misinformation, and prevent excessive polarization in political opinion.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".