A fundamental part of conducting cross-disciplinary web science research is having useful, high-quality datasets that provide value to studies across disciplines. In this paper, we introduce a large, handcoded corpus of online harassment data. A team of researchers collaboratively developed a codebook using grounded theory and labeled 35,000 tweets. Our resulting dataset has roughly 15% positive harassment examples and 85% negative examples. This data is useful for training machine learning models, identifying textual and linguistic features of online harassment, and for studying the nature of harassing comments and the culture of trolling.

Adverse childhood experiences (ACEs) and social determinants of health (SDOH) negatively affect health outcomes. This program was developed to screen for ACEs and SDOH in the primary care setting in families with children 9 months to 5 years of age at well-child checks and provide interventions that support families and build resiliency. Programmatic criteria were identified, referral resources were developed, and a database was implemented, with 246 families enrolled in year 1; 56.9% of caregivers reported 1 or more ACEs for their child, 63% of caregivers reported an SDOH need, and 39.4% of caregivers reported both. The average number of ACEs was 0.94. This program was created to address ACEs and SDOH, to empower families, build resiliency, and provide buffers to mitigate and prevent ACEs. It provides a model that can be implemented in a primary care setting while providing wraparound resources, including integrated mental health resources and referrals, to measure the success of these interventions.

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