With the large number of users connected to social networks, screenname duplication is a rising problem, which leads to interference when trying to recognize users. A number of algorithms have been proposed to distinguish user profiles on one or multiple social networks. The main task in this context is to have robust features. According to the state-of-the-art approaches, features can be: content and behavioural based features, that compare content similarity between posts or behaviour similarity (timestamps between posts (behavioural), or overlapping between content (content) for example). Attribute-based features that compare profiles attributes, such as gender, age, location or image. In this paper, we tackle this problem and propose SocialMatching++ a novel approach that leverages: (1) user life events such as graduation, marriage or new job, which used to enhance the behavioural approaches (2) profile biographies, which consist in small paragraphs that users write to comprise arbitrary information about themselves. These are used to enhance the attribute approaches. To evaluate our approach, we conducted experiments on 2,263 different profiles from Facebook matched with 5,694 Twitter users, and compared them with two baseline approaches. Our results show that SocialMatching++ achieves better results compared to the baselines approaches, showing that our system successfully bridges the gap between behavioural and attribute based approaches.