README.md

metadata

license: cc-by-nc-sa-4.0
language:
  - en
task_categories:
  - text-classification
  - zero-shot-classification
  - multiple-choice
  - visual-question-answering
tags:
  - emotion detection
  - news analysis
  - personalization
  - psychology
  - individual differences
  - affective computing
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: train
        path: train.csv
      - split: dev
        path: dev.csv
      - split: test_personalization_public
        path: test_personalization_public.csv
      - split: test_generalization_public
        path: test_generalization_public.csv
      - split: test_cold_start_public
        path: test_cold_start_public.csv

iNews: A Multimodal Dataset for Personalized Affective News Responses

This is the public version of the 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.
💾 Full (Gated) Dataset	https://huggingface.co/datasets/pitehu/inews	Gated version with persona variables.

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}
}

🌍 This is the public version which does not contain any annotator persona information.

Need persona information? Use our full dataset instead https://huggingface.co/datasets/pitehu/inews

Dataset Overview

• Same 12,276 annotations as the full 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.

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 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).

Use as a General Emotion Dataset

While iNews is designed for personalization affective response, it can also be used as a standard multimodal emotion dataset. To do this, you can aggregate the individual annotations for each news post. For example, by calculating the mean, median, or mode of the Valence and Arousal scores for a given post, you can derive a single, generalized emotion label. This makes the dataset suitable for traditional emotion classification or regression models that do not require personalization.

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.

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_public")

❌ Prohibited Uses

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