  Recent developments in generative AI have shone a spotlight on
high-performance synthetic text generation technologies. The now wide
availability and ease of use of such models highlights the urgent need to
provide equally powerful technologies capable of identifying synthetic text.
With this in mind, we draw inspiration from psychological studies which suggest
that people can be driven by emotion and encode emotion in the text they
compose. We hypothesize that pretrained language models (PLMs) have an
affective deficit because they lack such an emotional driver when generating
text and consequently may generate synthetic text which has affective
incoherence i.e. lacking the kind of emotional coherence present in
human-authored text. We subsequently develop an emotionally aware detector by
fine-tuning a PLM on emotion. Experiment results indicate that our
emotionally-aware detector achieves improvements across a range of synthetic
text generators, various sized models, datasets, and domains. Finally, we
compare our emotionally-aware synthetic text detector to ChatGPT in the task of
identification of its own output and show substantial gains, reinforcing the
potential of emotion as a signal to identify synthetic text. Code, models, and
datasets are available at https: //github.com/alanagiasi/emoPLMsynth
