Yoan Dinkov


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A Neighborhood Framework for Resource-Lean Content Flagging
Sheikh Muhammad Sarwar | Dimitrina Zlatkova | Momchil Hardalov | Yoan Dinkov | Isabelle Augenstein | Preslav Nakov
Transactions of the Association for Computational Linguistics, Volume 10

We propose a novel framework for cross- lingual content flagging with limited target- language data, which significantly outperforms prior work in terms of predictive performance. The framework is based on a nearest-neighbor architecture. It is a modern instantiation of the vanilla k-nearest neighbor model, as we use Transformer representations in all its components. Our framework can adapt to new source- language instances, without the need to be retrained from scratch. Unlike prior work on neighborhood-based approaches, we encode the neighborhood information based on query– neighbor interactions. We propose two encoding schemes and we show their effectiveness using both qualitative and quantitative analysis. Our evaluation results on eight languages from two different datasets for abusive language detection show sizable improvements of up to 9.5 F1 points absolute (for Italian) over strong baselines. On average, we achieve 3.6 absolute F1 points of improvement for the three languages in the Jigsaw Multilingual dataset and 2.14 points for the WUL dataset.


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Predicting the Factuality of Reporting of News Media Using Observations about User Attention in Their YouTube Channels
Krasimira Bozhanova | Yoan Dinkov | Ivan Koychev | Maria Castaldo | Tommaso Venturini | Preslav Nakov
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

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. In particular, we design a rich set of features derived from the temporal evolution of the number of views, likes, dislikes, and comments for a video, which we then aggregate to the channel level. We develop and release a dataset for the task, containing observations of user attention on YouTube channels for 489 news media. Our experiments demonstrate both complementarity and sizable improvements over state-of-the-art textual representations.


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What Was Written vs. Who Read It: News Media Profiling Using Text Analysis and Social Media Context
Ramy Baly | Georgi Karadzhov | Jisun An | Haewoon Kwak | Yoan Dinkov | Ahmed Ali | James Glass | Preslav Nakov
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Predicting the political bias and the factuality of reporting of entire news outlets are critical elements of media profiling, which is an understudied but an increasingly important research direction. The present level of proliferation of fake, biased, and propagandistic content online has made it impossible to fact-check every single suspicious claim, either manually or automatically. Thus, it has been proposed to profile entire news outlets and to look for those that are likely to publish fake or biased content. This makes it possible to detect likely “fake news” the moment they are published, by simply checking the reliability of their source. From a practical perspective, political bias and factuality of reporting have a linguistic aspect but also a social context. Here, we study the impact of both, namely (i) what was written (i.e., what was published by the target medium, and how it describes itself in Twitter) vs. (ii) who reads it (i.e., analyzing the target medium’s audience on social media). We further study (iii) what was written about the target medium (in Wikipedia). The evaluation results show that what was written matters most, and we further show that putting all information sources together yields huge improvements over the current state-of-the-art.

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EXAMS: A Multi-subject High School Examinations Dataset for Cross-lingual and Multilingual Question Answering
Momchil Hardalov | Todor Mihaylov | Dimitrina Zlatkova | Yoan Dinkov | Ivan Koychev | Preslav Nakov
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We propose EXAMS – a new benchmark dataset for cross-lingual and multilingual question answering for high school examinations. We collected more than 24,000 high-quality high school exam questions in 16 languages, covering 8 language families and 24 school subjects from Natural Sciences and Social Sciences, among others.EXAMS offers unique fine-grained evaluation framework across multiple languages and subjects, which allows precise analysis and comparison of the proposed models. We perform various experiments with existing top-performing multilingual pre-trained models and show that EXAMS offers multiple challenges that require multilingual knowledge and reasoning in multiple domains. We hope that EXAMS will enable researchers to explore challenging reasoning and knowledge transfer methods and pre-trained models for school question answering in various languages which was not possible by now. The data, code, pre-trained models, and evaluation are available at http://github.com/mhardalov/exams-qa.


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Detecting Toxicity in News Articles: Application to Bulgarian
Yoan Dinkov | Ivan Koychev | Preslav Nakov
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

Online media aim for reaching ever bigger audience and for attracting ever longer attention span. This competition creates an environment that rewards sensational, fake, and toxic news. To help limit their spread and impact, we propose and develop a news toxicity detector that can recognize various types of toxic content. While previous research primarily focused on English, here we target Bulgarian. We created a new dataset by crawling a website that for five years has been collecting Bulgarian news articles that were manually categorized into eight toxicity groups. Then we trained a multi-class classifier with nine categories: eight toxic and one non-toxic. We experimented with different representations based on ElMo, BERT, and XLM, as well as with a variety of domain-specific features. Due to the small size of our dataset, we created a separate model for each feature type, and we ultimately combined these models into a meta-classifier. The evaluation results show an accuracy of 59.0% and a macro-F1 score of 39.7%, which represent sizable improvements over the majority-class baseline (Acc=30.3%, macro-F1=5.2%).