William Brannon

Learning on text-attributed graphs (TAGs), in which nodes are associated with one or more texts, has been the subject of much recent work. However, most approaches tend to make strong assumptions about the downstream task of interest, are reliant on hand-labeled data, or fail to equally balance the importance of both text and graph representations. In this work, we propose Contrastive Graph-Text pretraining (ConGraT), a general, self-supervised approach for jointly learning separate representations of texts and nodes in a TAG. Our method trains a language model (LM) and a graph neural network (GNN) to align their representations in a common latent space using a batch-wise contrastive learning objective inspired by CLIP. We further propose an extension to the CLIP objective that leverages graph structure to incorporate information about inter-node similarity. Extensive experiments demonstrate that ConGraT outperforms baselines on various downstream tasks, including node and text category classification, link prediction, and language modeling. Finally, we present an application of our method to community detection in social graphs, which enables finding more textually grounded communities, rather than purely graph-based ones. Code and certain datasets are available at https://github.com/wwbrannon/congrat.

New capabilities in foundation models are owed in large part to massive, widely-sourced, and under-documented training data collections. Existing practices in data collection have led to challenges in tracing authenticity, verifying consent, preserving privacy, addressing representation and bias, respecting copyright, and overall developing ethical and trustworthy foundation models. In response, regulation is emphasizing the need for training data transparency to understand foundation models’ limitations. Based on a large-scale analysis of the foundation model training data landscape and existing solutions, we identify the missing infrastructure to facilitate responsible foundation model development practices. We examine the current shortcomings of common tools for tracing data authenticity, consent, and documentation, and outline how policymakers, developers, and data creators can facilitate responsible foundation model development by adopting universal data provenance standards.

The rapid evolution of the Internet is reshaping the media landscape, with frequent claims of an accelerated and increasingly outraged news cycle. We test these claims empirically, investigating the dynamics of news spread, decay, and sentiment on Twitter (now known as X) compared to talk radio. Analyzing 2019–2021 data including 517,000 hour of radio content and 26.6 million tweets by elite journalists, politicians, and general users, we identified 1694 news events. We find that news on Twitter circulates faster, fades faster, and is more negative and outraged compared to radio, with Twitter outrage also more short-lived. These patterns are consistent across various user types and robustness checks. Our results illustrate an important way social media may influence traditional media: framing and agenda-setting simply by speaking first. As journalism evolves with these media, news audiences may encounter faster shifts in focus, less attention to each news event, and much more negativity and outrage.