  Internet memes have emerged as a novel format for communication and
expressing ideas on the web. Their fluidity and creative nature are reflected
in their widespread use, often across platforms and occasionally for unethical
or harmful purposes. While computational work has already analyzed their
high-level virality over time and developed specialized classifiers for hate
speech detection, there have been no efforts to date that aim to holistically
track, identify, and map internet memes posted on social media. To bridge this
gap, we investigate whether internet memes across social media platforms can be
contextualized by using a semantic repository of knowledge, namely, a knowledge
graph. We collect thousands of potential internet meme posts from two social
media platforms, namely Reddit and Discord, and develop an
extract-transform-load procedure to create a data lake with candidate meme
posts. By using vision transformer-based similarity, we match these candidates
against the memes cataloged in IMKG -- a recently released knowledge graph of
internet memes. We leverage this grounding to highlight the potential of our
proposed framework to study the prevalence of memes on different platforms, map
them to IMKG, and provide context about memes on social media.
