We propose a novel framework for predicting the factuality of reporting of news media outlets by studying the user attention cycles in their YouTube channels.
The framework is based on a nearest-neighbour architecture.
no code implementations • 27 Feb 2021 • Preslav Nakov, Vibha Nayak, Kyle Dent, Ameya Bhatawdekar, Sheikh Muhammad Sarwar, Momchil Hardalov, Yoan Dinkov, Dimitrina Zlatkova, Guillaume Bouchard, Isabelle Augenstein
Abusive language on online platforms is a major societal problem, often leading to important societal problems such as the marginalisation of underrepresented minorities.
We perform various experiments with existing top-performing multilingual pre-trained models and we show that EXAMS offers multiple challenges that require multilingual knowledge and reasoning in multiple domains.
Alternatively, we can profile entire news outlets and look for those that are likely to publish fake or biased content.
Our analysis shows that the use of acoustic signal helped to improve bias detection by more than 6% absolute over using text and metadata only.