  In information-rich environments, the competition for users' attention leads
to a flood of content from which people often find hard to sort out the most
relevant and useful pieces. Using Twitter as a case study, we applied an
attention economy solution to generate the most informative tweets for its
users. By considering the novelty and popularity of tweets as objective
measures of their relevance and utility, we used the Huberman-Wu algorithm to
automatically select the ones that will receive the most attention in the next
time interval. Their predicted popularity was confirmed by using Twitter data
collected for a period of 2 months.
