  Development funds are essential to finance climate change adaptation and are
thus an important part of international climate policy. % However, the absence
of a common reporting practice makes it difficult to assess the amount and
distribution of such funds. Research has questioned the credibility of reported
figures, indicating that adaptation financing is in fact lower than published
figures suggest. Projects claiming a greater relevance to climate change
adaptation than they target are referred to as "overreported". To estimate
realistic rates of overreporting in large data sets over times, we propose an
approach based on state-of-the-art text classification. To date, assessments of
credibility have relied on small, manually evaluated samples. We use such a
sample data set to train a classifier with an accuracy of $89.81\% \pm 0.83\%$
(tenfold cross-validation) and extrapolate to larger data sets to identify
overreporting. Additionally, we propose a method that incorporates evidence of
smaller, higher-quality data to correct predicted rates using Bayes' theorem.
This enables a comparison of different annotation schemes to estimate the
degree of overreporting in climate change adaptation. Our results support
findings that indicate extensive overreporting of $32.03\%$ with a credible
interval of $[19.81\%;48.34\%]$.
