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Improved statistical machine translation using monolingual paraphrases ...
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Slav-NER: the 3rd Cross-lingual Challenge on Recognition, Normalization, Classification, and Linking of Named Entities across Slavic languages ...
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Slav-NER: the 3rd Cross-lingual Challenge on Recognition, Normalization, Classification, and Linking of Named Entities across Slavic languages ...
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A Neighbourhood Framework for Resource-Lean Content Flagging ...
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Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-Training ...
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SemEval-2021 Task 6: Detection of Persuasion Techniques in Texts and Images ...
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SOLID: A Large-Scale Semi-Supervised Dataset for Offensive Language Identification ...
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SUper Team at SemEval-2016 Task 3: Building a feature-rich system for community question answering ...
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Feature-Rich Named Entity Recognition for Bulgarian Using Conditional Random Fields ...
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RuleBERT: Teaching Soft Rules to Pre-Trained Language Models ...
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SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020) ...
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On a Novel Application of Wasserstein-Procrustes for Unsupervised Cross-Lingual Learning ...
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EXAMS: A Multi-Subject High School Examinations Dataset for Cross-Lingual and Multilingual Question Answering ...
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SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020) ...
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SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020) ...
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What Was Written vs. Who Read It: News Media Profiling Using Text Analysis and Social Media Context ...
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Abstract:
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. Alternatively, we can profile entire news outlets and look for those that are likely to publish fake or biased content. This approach 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 on Twitter) vs. (ii) who read it (i.e., analyzing the readers of the target medium on Facebook, Twitter, ... : Factuality of reporting, fact-checking, political ideology, media bias, disinformation, propaganda, social media, news media ...
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Keyword:
68T50; Computation and Language cs.CL; FOS Computer and information sciences; I.2.7; Information Retrieval cs.IR; Machine Learning cs.LG
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URL: https://dx.doi.org/10.48550/arxiv.2005.04518 https://arxiv.org/abs/2005.04518
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SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles ...
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