Given the wide spread of social networks, research efforts to retrieve information using tagging from social networks communications have increased. In particular, in Twitter social network,
are widely used to define a shared context for events or topics. While this is a common practice often the
freely introduced by the user become easily biased. In this paper, we propose to deal with this bias defining semantic meta-hashtags by clustering similar messages to improve the classification. First, we use the user-defined
as the Twitter message class labels. Then, we apply the meta-hashtag approach to boost the performance of the message classification.
The meta-hashtag approach is tested in a Twitter-based dataset constructed by requesting public
to the Twitter API. The experimental results yielded by comparing a baseline model based on user-defined
with the clustered meta-hashtag approach show that the overall classification is improved. It is concluded that by incorporating semantics in the meta-hashtag model can have impact in different applications, e.g. recommendation systems, event detection or crowdsourcing.