Current deep learning models often achieve excellent results on benchmark image-to-text datasets but fail to generate texts that are useful in practice.We argue that to close this gap, it is vital to distinguish descriptions from captions based on their distinct communicative roles.Descriptions focus on visual features and are meant to replace an image (often to increase accessibility), whereas captions appear alongside an image to supply additional information.To motivate this distinction and help people put it into practice, we introduce the publicly available Wikipedia-based dataset Concadia consisting of 96,918 images with corresponding English-language descriptions, captions, and surrounding context.Using insights from Concadia, models trained on it, and a preregistered human-subjects experiment with human-and model-generated texts, we characterize the commonalities and differences between descriptions and captions.In addition, we show that, for generating both descriptions and captions, it is useful to augment image-totext models with representations of the textual context in which the image appeared.split datapoints unique articles avg length (words) avg word length vocab size train 77,534 31,240 caption: 12.79

Homeless youth are a highly vulnerable population and report highly elevated rates of substance use. Prior work on mitigating substance use among homeless youth has primarily relied on survey data to get information about substance use among homeless youth, which can then be used to inform the design of targeted intervention programs. However, such survey data is often onerous to collect, is limited by its reliance on self-reports and retrospective recall, and quickly becomes dated. The advent of social media has provided us with an important data source for understanding the health behaviors of homeless youth. In this paper, we target this specific population and demonstrate how to detect substance use based on texts from social media. We collect ~135K Facebook posts and comments together with survey responses from a group of homeless youth and use this data to build novel substance use detection systems with machine learning and natural language processing techniques. Experimental results show that our proposed methods achieve ROC-AUC scores of ~0.77 on identifying certain kinds of substance use among homeless youth using Facebook conversations only, and ROC-AUC scores of ~0.83 when combined with answers to four survey questions that are not about their demographic characteristics or substance use. Furthermore, we investigate connections between the characteristics of people's Facebook posts and substance use and provide insights about the problem.