  Deepfake detection, the task of automatically discriminating
machine-generated text, is increasingly critical with recent advances in
natural language generative models. Existing approaches to deepfake detection
typically represent documents with coarse-grained representations. However,
they struggle to capture factual structures of documents, which is a
discriminative factor between machine-generated and human-written text
according to our statistical analysis. To address this, we propose a
graph-based model that utilizes the factual structure of a document for
deepfake detection of text. Our approach represents the factual structure of a
given document as an entity graph, which is further utilized to learn sentence
representations with a graph neural network. Sentence representations are then
composed to a document representation for making predictions, where consistent
relations between neighboring sentences are sequentially modeled. Results of
experiments on two public deepfake datasets show that our approach
significantly improves strong base models built with RoBERTa. Model analysis
further indicates that our model can distinguish the difference in the factual
structure between machine-generated text and human-written text.
