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Datasets:
pitehu
/
inews

Tasks:
Text Classification
Zero-Shot Classification
Multiple Choice
Modalities:
Tabular
Text
Formats:
csv
Languages:
English
Size:
10K - 100K
ArXiv:
Tags:
emotion detection
news analysis
personalization
psychology
individual differences
affective computing
Libraries:
Datasets
pandas
License:
Dataset card Data Studio Files
xet
Community

Request Access to Full Dataset with Persona Information

This dataset contains sensitive personal information. We will evaluate your application and you will receive email notification once processed.

This dataset contains sensitive demographic and personality information collected from human participants. Access is restricted to ensure participant privacy and ethical use. By requesting access, you agree to use this data responsibly.

Log in or Sign Up to review the conditions and access this dataset content.

iNews: A Multimodal Dataset for Personalized Affective News Responses

This is the full, gated dataset for the paper: iNews: A Multimodal Dataset for Modeling Personalized Affective Responses to News (ACL 2025).

iNews is a large-scale, multimodal dataset designed to model personalized emotional responses to news content. Unlike datasets with aggregated labels, iNews captures the rich individual variability in affect, providing the granular data needed for developing human-centered AI. The comprehensive persona profiles in this dataset explain 15.2% of annotation variance, and their inclusion improves zero-shot affective prediction accuracy by up to 7%.

🔗 Official Links

Resource Link Description
📄 Paper https://arxiv.org/abs/2503.03335 The full research paper on arXiv.
💻 Code https://github.com/pitehu/inews Official GitHub repo.
💾 Public Dataset https://huggingface.co/datasets/pitehu/inews_public Non-gated version with emotion labels only (no persona information).

Citation

If you use this dataset in your research, please cite our paper:

@article{hu2025inews,
  title={iNews: A multimodal dataset for modeling personalized affective responses to news},
  author={Hu, Tiancheng and Collier, Nigel},
  journal={arXiv preprint arXiv:2503.03335},
  year={2025}
}

⚠️ GATED ACCESS REQUIRED - Contains sensitive persona data

🌍 Public Version Available

Just need emotion labels? Use our public repository - no application required.

This gated version adds demographic and personality features for personalization research.

Dataset Overview

Complete dataset with individual differences data:

  • Same 12,276 annotations as public version (Note: A subset of annotations is withheld for a future workshop shared task.)
Feature Category Description Examples
News Content Multimodal posts from major UK news outlets. Post text, url, headline, outlet information.
Affective Annotations Fine-grained emotional responses from each user. Valence, Arousal, Dominance (1-7), Discrete Emotions, Relevance, Sharing Likelihood.
👤 Persona Profiles (This Gated Version Only) Comprehensive annotator persona. Demographics: Age, gender, income, education.
Personality: Big Five (BFI-10), PANAS, PERS.
Cognitive: Cognitive Reflection Test (CRT).
Habits & Beliefs: News trust, consumption patterns, political affiliation.

Data Fields and Codebook

For a detailed explanation of each column in the dataset, please refer to the survey_codebook.json file included in this repository. For most use cases, just use the System_Prompt column as the system persona prompt and the User_Prompt as the user prompt, before diving into the specific columns.

A Note on Multimodal Data

Due to copyright restrictions, the news post images/screenshots are not directly included in the dataset. However, we provide:

  1. The original post_url linking to the Facebook post.
  2. The associated text of the posts
  3. All metadata about the posts (engagement information as well as topics)

Researchers interested in multimodal analysis will need to retrieve the images themselves (see github repo linked above).

Main Splits

Split Samples Users Posts Purpose
train 7,350 202 2,028 Model training
dev 155 30 128 Hyperparameter tuning
test_personalization_public 1,641 202 580 Personalization: Known users, new content.
test_generalization_public 1,676 59 1,410 Generalization: New users, seen content.
test_cold_start_public 498 59 401 Cold-Start: New users, new content.

Test Set Scenarios Explained

🎯 Personalization (test_personalization)

  • Scenario: A user from the training set rates a completely new news post.
  • Tests: The model's ability to learn and adapt to a specific individual's preferences and response patterns.
  • Real-world Analog: Recommending a new article to an existing user of your platform.

🌍 Generalization (test_generalization)

  • Scenario: A completely new user rates a news post that was already seen in the training set.
  • Tests: The model's ability to generalize from persona profiles to predict reactions without any prior behavioral data from that user.
  • Real-world Analog: Predicting how a new user will react to trending or popular content.

❄️ Cold Start (test_cold_start)

  • Scenario: A completely new user rates a completely new news post.
  • Tests: The model's most fundamental reasoning capability, relying solely on the relationship between a persona profile and novel content.
  • Real-world Analog: The first-time interaction for a new user on a platform with new content. This is the hardest and most realistic "zero-shot" scenario.

Note: For reproducing our paper's baseline results: please use (paper_few_shot_dataset, paper_test_dataset).

Usage Example 

from datasets import load_dataset

# Requires approved access token
dataset = load_dataset("pitehu/inews", use_auth_token=True)

# Access demographic features
sample = dataset['train'][0]
print(f"Age: {sample['Age']}")
print(f"Extraversion: {sample['Extraversion']}")
print(f"Political affiliation: {sample['Political.affiliation..uk.']}")

# Personalization example
demographic_features = ['Age', 'Sex', 'Extraversion', 'Agreeableness']
personality_data = {feat: sample[feat] for feat in demographic_features}

For most use cases, just use the System_Prompt column as the system persona prompt and the User_Prompt as the user prompt, before diving into the specific columns.

Key Research Applications

  • Personalized Content Systems: User-specific emotion prediction
  • Algorithmic Bias: Demographic differences in AI systems
  • Political Psychology: News perception across political groups
  • Cross-cultural Studies: Emotion perception differences

❌ Prohibited Uses

  • Re-identification attempts
  • Commercial applications
  • Political targeting or profiling
  • Sharing data with third parties
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