Natural language is the main means of interaction in human societies. When these societies move to the digital world, the interaction between elements generates digital content, also mostly made up of natural language. Digital media and social networks have enhanced this interaction, creating an ecosystem of spaces where content is created and consumed in increasing quantity and speed. However, this ubiquitous environment of interaction has two opposing perspectives. 

On the one hand, it is the repository of certain pervasive content that negatively affects the quality and freedom of information. Digital media have become a space where hoaxes, hate speech or abusive behaviour proliferate, among other content types that directly and negatively harm users of this space in particular and society in general. On the other hand, there is also a benefit generated by the sharing of valuable, quality and reliable information that generates a digital collective intelligence that can be used to the advantage of society as a whole. 

Therefore, in this context, our main hypothesis is that human language technologies via the modelling the behaviour of digital content can contribute, on the one hand, to the detection, mitigation and prevention of harmful digital content, in pursuit of a sanitisation of social media on the Internet, and on the other hand, the characterisation of beneficial and trustworthy content, thereby making a valid and necessary contribution to the goal of ensuring a respectful, safe and trustworthy communication environment.