Nowadays, microblogging sites have completely changed the manner in which people communicate and share information. They are among the most relevant source of knowledge where information is created, exchanged and transformed, as witnessed by the important number of their users and their activities during events or campaigns like the terror attack in Paris in 2015. On Twitter, users post messages (called tweets) in real time about events, natural disasters, news, etc. Tweets are short messages that do not exceed 140 characters. Due to this limitation, an individual tweet it's rarely self-content. However, user cannot effectively understand or consume information. In order, to make tweet understandable to a reader, it is therefore necessary to know their context. In fact, on Twit-ter, context can be derived from users interactions, content streams and friendship. Given that there are rich user interactions on Twitter. In this paper, we propose an approach for tweet contextualization task which combines different types of signals from social users interactions to provide automatically information that explains the tweet. In addition, our approach aims to help users to satisfy any contextual information need. To evaluate our approach, we construct a reference summary by asking assessors to manually select the most informative tweets as a summary. Our experimental results based on this editorial data set offers interesting results and help ensure that context summaries contain adequate correlating information with the given tweet.