As technology evolves, there are more and more possibilities to disseminate information quickly and cheaply. Many online newspapers and magazines are now globally accessible via the internet. The ease with which information is transmitted brings with it some risks linked to “misinformation”. A clear example of this, is terrorist recruitment, especially by the Islamic State (IS). After years of fighting this radical movement in Europe, the problem has not yet been solved. After the numerous attacks associated with this terrorist group, it is necessary to develop new tools to counter its expansion. Technology is one of the key enablers of such developments. This thesis consists of the creation of a tool for the automatic classification of articles or news, published in different web communication channels (mainly newspapers), against the propagation of radical Islamist ideologies. To this end, the work has been divided into two parts. A first part in which two models based on machine learning and natural language pro- cessing techniques have been developed, by means of which it is possible to carry out the classification. Both models have been compared, in order to finally use the one that gener- ates the best results. The second part consisted of the implementation of a pipeline that allows the extraction of news and articles from different web channels. Afterwards, these articles are processed and classified (the developed model is applied). Finally, they are stored in a database and represented in a graphical interface that allows the visualisation of different results.