A large amount of human generated information is available online in the form of text exchanged between individuals or groups. Examples include social network sites, on- line forums and emails. The public accessibility of several of these sources allows us to observe our society at various scales, from conversations among small groups of individuals to the effects of misinformation on large communities. To cope with the complexity of online information, researchers have typically focused on either the topology of the network, as commonly done in Network Science, or the text exchanged among individuals, using methods from Computational Linguistics. In both cases time has also been taken into consideration, as in Temporal Networks or Temporal Information Retrieval. In this work, we introduce an attributed multilayer model for temporal text net- works, enabling the application of a wide range of existing methods to this context. Our model can represent all the information contained in the aforementioned data sources, including different types of text interactions, such as direct messages exchanged be- tween individuals, multicast information targeting specific communities or broadcast news.