  The prevalence and harms of online misinformation is a perennial concern for
internet platforms, institutions and society at large. Over time, information
shared online has become more media-heavy and misinformation has readily
adapted to these new modalities. The rise of generative AI-based tools, which
provide widely-accessible methods for synthesizing realistic audio, images,
video and human-like text, have amplified these concerns. Despite intense
public interest and significant press coverage, quantitative information on the
prevalence and modality of media-based misinformation remains scarce. Here, we
present the results of a two-year study using human raters to annotate online
media-based misinformation, mostly focusing on images, based on claims assessed
in a large sample of publicly-accessible fact checks with the ClaimReview
markup. We present an image typology, designed to capture aspects of the image
and manipulation relevant to the image's role in the misinformation claim. We
visualize the distribution of these types over time. We show the rise of
generative AI-based content in misinformation claims, and that its commonality
is a relatively recent phenomenon, occurring significantly after heavy press
coverage. We also show "simple" methods dominated historically, particularly
context manipulations, and continued to hold a majority as of the end of data
collection in November 2023. The dataset, Annotated Misinformation, Media-Based
(AMMeBa), is publicly-available, and we hope that these data will serve as both
a means of evaluating mitigation methods in a realistic setting and as a
first-of-its-kind census of the types and modalities of online misinformation.
