  We analysed a dataset of scientific manuscripts that were submitted to
various conferences in artificial intelligence. We performed a combination of
semantic, lexical and psycholinguistic analyses of the full text of the
manuscripts and compared them with the outcome of the peer review process. We
found that accepted manuscripts scored lower than rejected manuscripts on two
indicators of readability, and that they also used more scientific and
artificial intelligence jargon. We also found that accepted manuscripts were
written with words that are less frequent, that are acquired at an older age,
and that are more abstract than rejected manuscripts. The analysis of
references included in the manuscripts revealed that the subset of accepted
submissions were more likely to cite the same publications. This finding was
echoed by pairwise comparisons of the word content of the manuscripts (i.e. an
indicator or semantic similarity), which were more similar in the subset of
accepted manuscripts. Finally, we predicted the peer review outcome of
manuscripts with their word content, with words related to machine learning and
neural networks positively related with acceptance, whereas words related to
logic, symbolic processing and knowledge-based systems negatively related with
acceptance.
