--- 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: 1. Profiled Background – demographics, Big Five traits, locus of control, baseline health opinions. 2. 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. ![image](https://cdn-uploads.huggingface.co/production/uploads/65d7cfbf1b0043dbe0cb053b/4mKKzKMYUjKo7usIseiSP.png) - **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:** [More Information Needed] [More Information Needed] --> ## Dataset Card Contact [More Information Needed]