It is very common in todays’ social networks that several discussion threads around similar topics are opened at the same time in different distinct or overlapping communities. Being aware about these different threads may be difficult. Moreover, when new threads are created, it may be useful to provide the user with linked past tweets instead of generating new threads. Information linkage is the pro- cess by which different pieces of information are put together ac- cording to criteria and constraints to form a new information which is richer (i.e. increased) and which can be consumed by an user or automatically by another process. This linkage can: (i) ease the digestion of information, i.e. its perception by users, (ii) enable a better information management from the system perspective, and (iii) allow other third-party applications to draw more benefits from a social content which, in a disparate form, is useless. The problem we are tackling can be formulated as follows: Having a broad set of interactions between users of a social network with disparate messages and connections, how to link these interactions so that they are correlated consistently and significantly for either an end user or an automatic processor to navigate easier in this large content. We propose in this paper to handle the problem of overload in social interactions by grouping messages according to three important dimensions: (i) content (textual and hashtags), (ii) users, and (iii) time difference. This process will also allow to perform well other tasks, such as query recommendation, text understanding (i.e. summarization), and event detection. We evaluated our approach on a Twitter data set and we compared it to other existing approaches and the results are promising and encouraging.