Media Coding Dataset for News Content Analysis
Authors/Creators
Description
This dataset accompanies the study Beyond Manual Media Coding: Evaluating Large Language Models and Agents for News Content Analysis.
It provides a reproducible benchmark for evaluating automated content analysis methods against human-annotated ground truth.
The dataset includes:
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articles.csv
Contains the 200 news articles collected for this study, each with:-
id: unique identifier -
url: source URL of the original article -
content: full text of the news article
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codebook.json
A structured JSON file defining the 26-question analysis codebook used for annotation.
Each question entry specifies:-
questionId: question ID (e.g., Q1) -
prompt: annotation question text -
questionAnswerType: type (SINGLE_CHOICE or MULTI_CHOICE) -
eligibleQuestionAnswers: list of possible tags / codes
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annotations.json
Contains the complete human annotation data.
For each articleid, it provides the list of responses to all 26 codebook questions as determined by an expert annotator, establishing the ground truth labels.
Intended use
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Designed for research popuses including natural language understanding, content classification, and LLM evaluation.
- Please request access with your academic email.