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Alyssa Smith

Publications and source records attributed to Alyssa Smith.

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A Blue Start: A large-scale pairwise and higher-order social network dataset

Large-scale networks have been instrumental in shaping how we think about social systems, and have undergirded many foundational results in mathematical epidemiology, computational social science, and biology. However, many of the social systems through which diseases spread, information disseminates, and individuals interact are inherently mediated through groups, known as higher-order interactions. A gap exists between higher-order models of group formation and spreading processes and the data necessary to validate these mechanisms. Similarly, few datasets bridge the gap between pairwise and higher-order network data. The Bluesky social media platform is an ideal laboratory for observing social ties at scale through its open API. Not only does Bluesky contain pairwise following relationships, but it also contains higher-order social ties known as "starter packs" which are user-curated lists designed to promote social network growth. We introduce "A Blue Start", a large-scale network dataset comprising 39.7M user accounts, 2.4B pairwise following relationships, and 365.8K groups representing starter packs. This dataset will be an essential resource for the study of higher-order networks.

physics.soc-ph

OpenFraming: We brought the ML; you bring the data. Interact with your data and discover its frames

When journalists cover a news story, they can cover the story from multiple angles or perspectives. A news article written about COVID-19 for example, might focus on personal preventative actions such as mask-wearing, while another might focus on COVID-19's impact on the economy. These perspectives are called "frames," which when used may influence public perception and opinion of the issue. We introduce a Web-based system for analyzing and classifying frames in text documents. Our goal is to make effective tools for automatic frame discovery and labeling based on topic modeling and deep learning widely accessible to researchers from a diverse array of disciplines. To this end, we provide both state-of-the-art pre-trained frame classification models on various issues as well as a user-friendly pipeline for training novel classification models on user-provided corpora. Researchers can submit their documents and obtain frames of the documents. The degree of user involvement is flexible: they can run models that have been pre-trained on select issues; submit labeled documents and train a new model for frame classification; or submit unlabeled documents and obtain potential frames of the documents. The code making up our system is also open-sourced and well-documented, making the system transparent and expandable. The system is available on-line at http://www.openframing.org and via our GitHub page https://github.com/davidatbu/openFraming .

cs.CL