  While it remains a taboo topic, gender-based violence (GBV) undermines the
health, dignity, security and autonomy of its victims. Many factors have been
studied to generate or maintain this kind of violence, however, the influence
of the media is still uncertain. Here, we use Machine Learning tools to
extrapolate the effect of the news in GBV. By feeding neural networks with
news, the topic information associated with each article can be recovered. Our
findings show a relationship between GBV news and public awareness, the effect
of mediatic GBV cases, and the intrinsic thematic relationship of GBV news.
Because the used neural model can be easily adjusted, this also allows us to
extend our approach to other media sources or topics
