Analysing and evaluating the task of automatic tweet generation: Knowledge to business

• A study on users’ perception of natural language tweet generation is conducted.
• Two relevant issues of tweets are considered: informativeness and interestingness.
• We show informative and interesting tweets are generated using text summarisation.
• We determine the linguistic features characterising good and bad tweets.
• Our findings can help companies improve their positioning and reputation on the Web.

In this paper a study concerning the evaluation and analysis of natural language tweets is presented. Based on our experience in text summarisation, we carry out a deep analysis on user's perception through the evaluation of tweets manual and automatically generated from news. Specifically, we consider two key issues of a tweet: its informativeness and its interestingness. Therefore, we analyse: (1) do users equally perceive manual and automatic tweets?; (2) what linguistic features a good tweet may have to be interesting, as well as informative? The main challenge of this proposal is the analysis of tweets to help companies in their positioning and reputation on the Web. Our results show that: (1) automatically informative and interesting natural language tweets can be generated as a result of summarisation approaches; and (2) we can characterise good and bad tweets based on specific linguistic features not present in other types of tweets.