论文标题
机器学习研究基于性别的暴力在新闻媒体中的影响
Machine Learning to study the impact of gender-based violence in the news media
论文作者
论文摘要
尽管它仍然是一个禁忌话题，但基于性别的暴力（GBV）破坏了受害者的健康，尊严，安全和自治。已经研究了许多因素来引起或维持这种暴力，但是，媒体的影响仍然不确定。在这里，我们使用机器学习工具来推断GBV中新闻的影响。通过向新闻喂养神经网络，可以恢复与每篇文章相关的主题信息。我们的发现表明，GBV新闻与公众意识，中介GBV案件的效果以及GBV新闻的内在主题关系之间的关系。因为可以轻松调整所使用的神经模型，所以这也使我们可以将方法扩展到其他媒体来源或主题
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
