dataset_info:
features:
- name: ResponseId
dtype: string
- name: locus_score
dtype: float64
- name: locus
dtype: string
- name: age
dtype: string
- name: gender
dtype: string
- name: gender_text
dtype: float64
- name: assigned_birth
dtype: string
- name: assigned_birth_text
dtype: float64
- name: religion
dtype: string
- name: religion_text
dtype: string
- name: political_aff
dtype: string
- name: political_aff_text
dtype: float64
- name: race_ethnicity
dtype: string
- name: prim_language
dtype: string
- name: first_language
dtype: string
- name: first_language_text
dtype: string
- name: highest_education
dtype: string
- name: employment_status
dtype: string
- name: employment_status_text
dtype: float64
- name: current_profession
dtype: string
- name: current_profession_text
dtype: string
- name: income
dtype: string
- name: marital_status
dtype: string
- name: marital_status_text
dtype: string
- name: family_status
dtype: string
- name: family_status_text
dtype: string
- name: disability_binary
dtype: string
- name: conditional_disability
dtype: string
- name: vaccination
dtype: string
- name: question
dtype: string
- name: response
dtype: string
- name: Creative Imagination
dtype: float64
- name: Responsibility
dtype: float64
- name: Intellectual Curiosity
dtype: float64
- name: Depression
dtype: float64
- name: Emotional Volatility
dtype: float64
- name: Trust
dtype: float64
- name: Productiveness
dtype: float64
- name: Conscientiousness
dtype: float64
- name: Anxiety
dtype: float64
- name: Respectfulness
dtype: float64
- name: Compassion
dtype: float64
- name: Energy Level
dtype: float64
- name: Negative Emotionality
dtype: float64
- name: Aesthetic Sensitivity
dtype: float64
- name: Assertiveness
dtype: float64
- name: Agreeableness
dtype: float64
- name: Extraversion
dtype: float64
- name: Organization
dtype: float64
- name: Sociability
dtype: float64
- name: Open-Mindedness
dtype: float64
- name: topic
dtype: string
- name: media_path
dtype: string
- name: mental_health
dtype: string
- name: sexual_health
dtype: string
- name: copd
dtype: string
- name: chronic_disease
dtype: string
- name: hiv
dtype: string
- name: nutrition
dtype: string
- name: substance_abuse
dtype: string
- name: media_type
dtype: string
- name: target_population
dtype: string
- name: behavior_change
dtype: string
splits:
- name: train
num_bytes: 34943710
num_examples: 36109
download_size: 2719587
dataset_size: 34943710
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc
language:
- en
pretty_name: PHORECAST
The PHORECAST Dataset
Repository: https://github.com/rifaaQ/PHORECAST
Paper: https://arxiv.org/abs/2510.02535
Dataset Details
PHORECAST (Public Health Outreach REceptivity and CAmpaign Signal Tracking) is a multimodal dataset curated to enable fine-grained prediction of both individual-level behavioral responses and community-wide engagement patterns to health messaging. The dataset maps the characteristics of diverse individuals onto their reactions from interacting with health marketing content.
Dataset Description
Each participant:
Profiled Background – demographics, Big Five traits, locus of control, baseline health opinions.
Reviewed Campaigns – free-text and Likert-scale reactions to five curated campaigns.
Campaigns span seven categories:Nutrition & Diabetes, Vaccination / HIV / AIDS, Mental Health, Substance Abuse, Sexual Practices, COPD / Smoking, Chronic Diseases (e.g., Heart Disease, Cystic Fibrosis, Arthritis) and are annotated with target behavior, target population, and message type (Informative, Persuasive-Efficacy, Persuasive-Threat).
Please refer to our paper to learn more about how public health experts collected the campaign database.
- Curated by: Researchers from the University of Maryland, College Park.
- Language(s) (NLP): English
- License: The dataset is available under the Creative Commons NonCommercial (CC BY-NC 4.0).
Uses
The Dataset is provided for the purpose of research and educational use in the field of natural language processing, conversational agents, social science and related areas; and can be used to develop or evaluate artificial intelligence, including Large Vision Language Models (VLMs).
Direct Use
Evaluate vision-language models, study variability in campaign receptivity, guide health message design.
Out-of-Scope Use
The dataset should not be used for applications requiring verified factual accuracy, critical decision-making, or any malicious or unethical activities.
Dataset Structure
Each row consists of an individual's reaction (both numerical and te to one public health campaign, alongside their demographic and personality information.
Dataset Creation
Curation Rationale
The PHORECAST dataset aims to map real human profiles (demographics, personality, and locus of control) to their responses / reactions from interacting with various public health campaigns. The primary purpose is for academic research to study how different people interact with stimuli and simulate how and why different communities respond differently to visuals. The results will be used to build an AI simulator that can mimic real world communities.
Source Data
Data Collection and Processing
All collection and processing stages were done using Python. More information can be found in the paper and on our github.
Who are the source data producers?
Correspondence to rqadri@umd.edu
Annotations [optional]
Annotation process
[More Information Needed]
Who are the annotators?
[More Information Needed]
Personal and Sensitive Information
[More Information Needed]
Bias, Risks, and Limitations
The dataset is primarily in English, limiting global applicability of our method. Sample Representation: While the dataset includes over 1,000 participants across diverse demographics, it is not fully representative of all populations. Certain groups (e.g., older adults, low-literacy populations, or non-English speakers) are underrepresented. Contextual Biases: Responses are shaped by the cultural and temporal context in which the data were collected (e.g., during/after global health crises).
Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
Citation [optional]
BibTeX:
@misc{qadri2025phorecastenablingaiunderstanding, title={PHORECAST: Enabling AI Understanding of Public Health Outreach Across Populations}, author={Rifaa Qadri and Anh Nhat Nhu and Swati Ramnath and Laura Yu Zheng and Raj Bhansali and Sylvette La Touche-Howard and Tracy Marie Zeeger and Tom Goldstein and Ming Lin}, year={2025}, eprint={2510.02535}, archivePrefix={arXiv}, primaryClass={cs.CY}, url={https://arxiv.org/abs/2510.02535}, }
APA:
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Dataset Card Contact
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