The researchers find that companies expecting higher levels of machine readership prepare their disclosures in ways that are more readable by this audience. “Machine readability” is measured in terms of how easily the information can be processed and parsed, with a one standard deviation increase in expected machine downloads corresponding to a 0.24 standard deviation increase in machine readability. For example, a table in a disclosure document might receive a low readability score because its formatting makes it difficult for a machine to recognize it as a table. A table in a disclosure document would receive a high readability score if it made effective use of tagging so that a machine could easily identify and analyze the content. Companies also go beyond machine readability and manage the sentiment and tone of their disclosures to induce algorithmic readers to draw favorable conclusions about the content. For example, companies avoid words that are listed as negative in the directions given to algorithms. The researchers show this by contrasting the occurrence of positive and negative words from the Harvard Psychosocial Dictionary — which has long been used by human readers — with those from an alternative, finance-specific dictionary that was published in 2011 and is now used extensively to train machine readers. After 2011, companies expecting high machine readership significantly reduced their use of words labelled as negatives in the finance-specific dictionary, relative to words that might be close synonyms in the Harvard dictionary but were not included in the finance publication. A one standard deviation increase in the share of machine downloads for a company is associated with a 0.1 percentage point drop in negative-sentiment words based on the finance-specific dictionary, as a percentage of total word count.
Another example, from @albrgr
Then quoting from the article: