Covering the Campaign: Computational Tools for Measuring Differences in Candidate and Party News Coverage With Application to an Emerging Democracy
Bibliographic record
Abstract
How does media coverage of electoral campaigns distinguish parties and candidates in emerging democracies? To answer, we present a multi-step procedure that we apply in South Africa. First, we develop a theoretically informed classification of election coverage as either “narrow” or “broad” from within the entire corpus of news coverage during an electoral campaign. Second, to deploy our classification scheme, we use a supervised machine learning approach to classify news as “broad,” “narrow,” or “not election-related.” Finally, we combine our supervised classification with a topic modeling algorithm (BERTTopic) that is based on Bidirectional Encoder Representations from Transformers (BERT), in addition to other statistical and machine learning methods. The combination of our classification scheme, BERTTopic, and associated methods allows us to identify the main election-related themes among broad and narrow election-related coverage, and how different candidates and parties are associated with these themes. We provide an in-depth discussion of our method for interested users in the social sciences. We then apply our proposed techniques on text from nearly 100,000 news articles during South Africa’s 2014 campaign and test our empirical predictions about candidate and party coverage of corruption, the economy, health, public infrastructure, and security. The application of our method highlights a nuanced campaign environment in South Africa; candidates and parties frequently receive distinct and substantive coverage on key campaign themes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".