An Empirical Study on Learning to Rank of Tweets

  • Duan Y
  • Jiang L
  • Qin T
 et al. 
  • 92


    Mendeley users who have this article in their library.
  • N/A


    Citations of this article.


Twitter, as one of the most popular micro-blogging services, provides large quantities of fresh information including real-time news, comments, conversation, pointless babble and advertisements. Twitter presents tweets in chronological order. Recently, Twitter introduced a new ranking strategy that considers popularity of tweets in terms of number of retweets. This ranking method, however, has not taken into account content relevance or the twitter account. Therefore a large amount of pointless tweets inevitably flood the relevant tweets. This paper proposes a new ranking strategy which uses not only the content relevance of a tweet, but also the account authority and tweet-specific features such as whether a URL link is included in the tweet. We employ learning to rank algorithms to determine the best set of features with a series of experiments. It is demonstrated that whether a tweet contains URL or not, length of tweet and account authority are the best conjunction.

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document


  • Yajuan Duan

  • Long Jiang

  • Tao Qin

  • Ming Zhou

  • Heung-yeung Shum

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free