Search Results for author: Yoan Dinkov

Found 7 papers, 4 papers with code

Detecting Toxicity in News Articles: Application to Bulgarian

1 code implementation RANLP 2019 Yoan Dinkov, Ivan Koychev, Preslav Nakov

Online media aim for reaching ever bigger audience and for attracting ever longer attention span.

Predicting the Leading Political Ideology of YouTube Channels Using Acoustic, Textual, and Metadata Information

1 code implementation20 Oct 2019 Yoan Dinkov, Ahmed Ali, Ivan Koychev, Preslav Nakov

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.

Bias Detection Multimodal Deep Learning

EXAMS: A Multi-Subject High School Examinations Dataset for Cross-Lingual and Multilingual Question Answering

2 code implementations EMNLP 2020 Momchil Hardalov, Todor Mihaylov, Dimitrina Zlatkova, Yoan Dinkov, Ivan Koychev, Preslav Nakov

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.

Question Answering Transfer Learning

Detecting Harmful Content On Online Platforms: What Platforms Need Vs. Where Research Efforts Go

no code implementations27 Feb 2021 Arnav Arora, Preslav Nakov, Momchil Hardalov, Sheikh Muhammad Sarwar, Vibha Nayak, Yoan Dinkov, Dimitrina Zlatkova, Kyle Dent, Ameya Bhatawdekar, Guillaume Bouchard, Isabelle Augenstein

The proliferation of harmful content on online platforms is a major societal problem, which comes in many different forms including hate speech, offensive language, bullying and harassment, misinformation, spam, violence, graphic content, sexual abuse, self harm, and many other.

Abusive Language Misinformation

Predicting the Factuality of Reporting of News Media Using Observations About User Attention in Their YouTube Channels

no code implementations RANLP 2021 Krasimira Bozhanova, Yoan Dinkov, Ivan Koychev, Maria Castaldo, Tommaso Venturini, Preslav Nakov

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.

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