In the current era, microblogging sites have completely changed the manner in which people communicate and share information. They give users the ability to communicate, interact, create conversations with each other and share information in real time about events, natural disasters, news, etc. On Twitter, users post messages called tweets. Tweets are short messages that do not exceed 140 characters. Due to this limitation, an individual tweet it's rarely self-content. However, users cannot effectively understand or consume information. In order, to make tweets understandable to a reader, it is therefore necessary to know their context. In fact, on Twitter, 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. 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 ensure that context summaries contain adequate correlating information with the given tweet.