Georgi Karadzhov


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SOLID: A Large-Scale Semi-Supervised Dataset for Offensive Language Identification
Sara Rosenthal | Pepa Atanasova | Georgi Karadzhov | Marcos Zampieri | Preslav Nakov
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Generating Answer Candidates for Quizzes and Answer-Aware Question Generators
Kristiyan Vachev | Momchil Hardalov | Georgi Karadzhov | Georgi Georgiev | Ivan Koychev | Preslav Nakov
Proceedings of the Student Research Workshop Associated with RANLP 2021

In education, quiz questions have become an important tool for assessing the knowledge of students. Yet, manually preparing such questions is a tedious task, and thus automatic question generation has been proposed as a possible alternative. So far, the vast majority of research has focused on generating the question text, relying on question answering datasets with readily picked answers, and the problem of how to come up with answer candidates in the first place has been largely ignored. Here, we aim to bridge this gap. In particular, we propose a model that can generate a specified number of answer candidates for a given passage of text, which can then be used by instructors to write questions manually or can be passed as an input to automatic answer-aware question generators. Our experiments show that our proposed answer candidate generation model outperforms several baselines.


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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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SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)
Marcos Zampieri | Preslav Nakov | Sara Rosenthal | Pepa Atanasova | Georgi Karadzhov | Hamdy Mubarak | Leon Derczynski | Zeses Pitenis | Çağrı Çöltekin
Proceedings of the Fourteenth Workshop on Semantic Evaluation

We present the results and the main findings of SemEval-2020 Task 12 on Multilingual Offensive Language Identification in Social Media (OffensEval-2020). The task included three subtasks corresponding to the hierarchical taxonomy of the OLID schema from OffensEval-2019, and it was offered in five languages: Arabic, Danish, English, Greek, and Turkish. OffensEval-2020 was one of the most popular tasks at SemEval-2020, attracting a large number of participants across all subtasks and languages: a total of 528 teams signed up to participate in the task, 145 teams submitted official runs on the test data, and 70 teams submitted system description papers.


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SemEval-2019 Task 8: Fact Checking in Community Question Answering Forums
Tsvetomila Mihaylova | Georgi Karadzhov | Pepa Atanasova | Ramy Baly | Mitra Mohtarami | Preslav Nakov
Proceedings of the 13th International Workshop on Semantic Evaluation

We present SemEval-2019 Task 8 on Fact Checking in Community Question Answering Forums, which features two subtasks. Subtask A is about deciding whether a question asks for factual information vs. an opinion/advice vs. just socializing. Subtask B asks to predict whether an answer to a factual question is true, false or not a proper answer. We received 17 official submissions for subtask A and 11 official submissions for Subtask B. For subtask A, all systems improved over the majority class baseline. For Subtask B, all systems were below a majority class baseline, but several systems were very close to it. The leaderboard and the data from the competition can be found at

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Tanbih: Get To Know What You Are Reading
Yifan Zhang | Giovanni Da San Martino | Alberto Barrón-Cedeño | Salvatore Romeo | Jisun An | Haewoon Kwak | Todor Staykovski | Israa Jaradat | Georgi Karadzhov | Ramy Baly | Kareem Darwish | James Glass | Preslav Nakov
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations

We introduce Tanbih, a news aggregator with intelligent analysis tools to help readers understanding what’s behind a news story. Our system displays news grouped into events and generates media profiles that show the general factuality of reporting, the degree of propagandistic content, hyper-partisanship, leading political ideology, general frame of reporting, and stance with respect to various claims and topics of a news outlet. In addition, we automatically analyse each article to detect whether it is propagandistic and to determine its stance with respect to a number of controversial topics.

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Multi-Task Ordinal Regression for Jointly Predicting the Trustworthiness and the Leading Political Ideology of News Media
Ramy Baly | Georgi Karadzhov | Abdelrhman Saleh | James Glass | Preslav Nakov
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

In the context of fake news, bias, and propaganda, we study two important but relatively under-explored problems: (i) trustworthiness estimation (on a 3-point scale) and (ii) political ideology detection (left/right bias on a 7-point scale) of entire news outlets, as opposed to evaluating individual articles. In particular, we propose a multi-task ordinal regression framework that models the two problems jointly. This is motivated by the observation that hyper-partisanship is often linked to low trustworthiness, e.g., appealing to emotions rather than sticking to the facts, while center media tend to be generally more impartial and trustworthy. We further use several auxiliary tasks, modeling centrality, hyper-partisanship, as well as left-vs.-right bias on a coarse-grained scale. The evaluation results show sizable performance gains by the joint models over models that target the problems in isolation.


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Predicting Factuality of Reporting and Bias of News Media Sources
Ramy Baly | Georgi Karadzhov | Dimitar Alexandrov | James Glass | Preslav Nakov
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We present a study on predicting the factuality of reporting and bias of news media. While previous work has focused on studying the veracity of claims or documents, here we are interested in characterizing entire news media. This is an under-studied, but arguably important research problem, both in its own right and as a prior for fact-checking systems. We experiment with a large list of news websites and with a rich set of features derived from (i) a sample of articles from the target news media, (ii) its Wikipedia page, (iii) its Twitter account, (iv) the structure of its URL, and (v) information about the Web traffic it attracts. The experimental results show sizable performance gains over the baseline, and reveal the importance of each feature type.


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We Built a Fake News / Click Bait Filter: What Happened Next Will Blow Your Mind!
Georgi Karadzhov | Pepa Gencheva | Preslav Nakov | Ivan Koychev
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017

It is completely amazing! Fake news and “click baits” have totally invaded the cyberspace. Let us face it: everybody hates them for three simple reasons. Reason #2 will absolutely amaze you. What these can achieve at the time of election will completely blow your mind! Now, we all agree, this cannot go on, you know, somebody has to stop it. So, we did this research, and trust us, it is totally great research, it really is! Make no mistake. This is the best research ever! Seriously, come have a look, we have it all: neural networks, attention mechanism, sentiment lexicons, author profiling, you name it. Lexical features, semantic features, we absolutely have it all. And we have totally tested it, trust us! We have results, and numbers, really big numbers. The best numbers ever! Oh, and analysis, absolutely top notch analysis. Interested? Come read the shocking truth about fake news and clickbait in the Bulgarian cyberspace. You won’t believe what we have found!

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Fully Automated Fact Checking Using External Sources
Georgi Karadzhov | Preslav Nakov | Lluís Màrquez | Alberto Barrón-Cedeño | Ivan Koychev
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017

Given the constantly growing proliferation of false claims online in recent years, there has been also a growing research interest in automatically distinguishing false rumors from factually true claims. Here, we propose a general-purpose framework for fully-automatic fact checking using external sources, tapping the potential of the entire Web as a knowledge source to confirm or reject a claim. Our framework uses a deep neural network with LSTM text encoding to combine semantic kernels with task-specific embeddings that encode a claim together with pieces of potentially relevant text fragments from the Web, taking the source reliability into account. The evaluation results show good performance on two different tasks and datasets: (i) rumor detection and (ii) fact checking of the answers to a question in community question answering forums.