A broad benchmark finds that general-purpose LLMs often outperform dedicated moderation APIs and prior CNN/LSTM baselines on text, image, and video content detection.
Machine Learning to study the impact of gender-based violence in the news media
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abstract
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
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cs.CV 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
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Advancing Content Moderation: Evaluating Large Language Models for Detecting Sensitive Content Across Text, Images, and Videos
A broad benchmark finds that general-purpose LLMs often outperform dedicated moderation APIs and prior CNN/LSTM baselines on text, image, and video content detection.