  Twitter is a social network that offers a rich and interesting source of
information challenging to retrieve and analyze. Twitter data can be accessed
using a REST API. The available operations allow retrieving tweets on the basis
of a set of keywords but with limitations such as the number of calls per
minute and the size of results. Besides, there is no control on retrieved
results and finding tweets which are relevant to a specific topic is a big
issue. Given these limitations, it is important that the query keywords cover
unambiguously the topic of interest in order to both reach the relevant answers
and decrease the number of API calls. In this paper, we introduce a new
crawling algorithm called "SmartTwitter Crawling" (STiC) that retrieves a set
of tweets related to a target topic. In this algorithm, we take an initial
keyword query and enrich it using a set of additional keywords that come from
different data sources. STiC algorithm relies on a DFS search in Twittergraph
where each reached tweet is considered if it is relevant with the query
keywords using a scoring, updated throughout the whole crawling process. This
scoring takes into account the tweet text, hashtags and the users who have
posted the tweet, replied to the tweet, been mentioned in the tweet or
retweeted the tweet. Given this score, STiC is able to select relevant tweets
in each iteration and continue by adding the related valuable tweets. Several
experiments have been achieved for different kinds of queries, the results
showedthat the precision increases compared to a simple BFS search.
