  As social media becomes increasingly prominent in our day to day lives, it is
increasingly important to detect informative content and prevent the spread of
disinformation and unverified rumours. While many sophisticated and successful
models have been proposed in the literature, they are often compared with older
NLP baselines such as SVMs, CNNs, and LSTMs. In this paper, we examine the
performance of a broad set of modern transformer-based language models and show
that with basic fine-tuning, these models are competitive with and can even
significantly outperform recently proposed state-of-the-art methods. We present
our framework as a baseline for creating and evaluating new methods for
misinformation detection. We further study a comprehensive set of benchmark
datasets, and discuss potential data leakage and the need for careful design of
the experiments and understanding of datasets to account for confounding
variables. As an extreme case example, we show that classifying only based on
the first three digits of tweet ids, which contain information on the date,
gives state-of-the-art performance on a commonly used benchmark dataset for
fake news detection --Twitter16. We provide a simple tool to detect this
problem and suggest steps to mitigate it in future datasets.
