  Web authors frequently embed social media to support and enrich their
content, creating the potential to derive web-based, cross-platform social
media representations that can enable more effective social media retrieval
systems and richer scientific analyses. As step toward such capabilities, we
introduce a novel language modeling framework that enables automatic annotation
of roles that social media entities play in their embedded web context. Using
related communication theory, we liken social media embeddings to quotes,
formalize the page context as structured natural language signals, and identify
a taxonomy of roles for quotes within the page context. We release
SocialQuotes, a new data set built from the Common Crawl of over 32 million
social quotes, 8.3k of them with crowdsourced quote annotations. Using
SocialQuotes and the accompanying annotations, we provide a role classification
case study, showing reasonable performance with modern-day LLMs, and exposing
explainable aspects of our framework via page content ablations. We also
classify a large batch of un-annotated quotes, revealing interesting
cross-domain, cross-platform role distributions on the web.
