Machine Learning to study the impact of gender-based violence in the news media
This research addresses the uncertain influence of news media on gender-based violence for victims and society, providing insights into public awareness and thematic connections.
This paper investigates the relationship between gender-based violence (GBV) news and public awareness using machine learning. By analyzing news articles with neural networks, the study found a connection between GBV news, the impact of specific mediatic GBV cases, and the thematic relationships within GBV news.
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