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Register a new user``Don't discuss'': Investigating Semantic and Argumentative Features for Supervised Propagandist Message Detection and Classification
"لا تناقش": التحقيق في ميزات الدلالية والجدبية للكشف عن الرسائل والتصنيف والإشراف
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واحدة من الآليات التي ينتشر فيها التضليل عبر الإنترنت، لا سيما من خلال وسائل التواصل الاجتماعي، من خلال توظيف تقنيات الدعاية. وتشمل هذه الاستراتيجيات الخطابية والنفسية المحددة، تتراوح من الاستفادة من العواطف لاستغلال المداخل المنطقية. في هذه الورقة، هدفنا هو دفع البحث إلى الأمام على اكتشاف الدعاية بناء على تحليل النص، بالنظر إلى الدور الحاسم قد تلعب هذه الأساليب لمعالجة هذه القضية المجتمعية الرئيسية. وبشكل أكثر دقة، نقترح نهجا مختلفا لتصنيف مقتطفات نصية كرسائل دعائية ووفقا لتقنية الدعاية التطبيقية الدقيقة، بالإضافة إلى تحليل لغوي مفصل للميزات التي تميز معلومات الدعاية في النص (مثل ميزات الدلالية والمعنويات والحجة) وبعد تجارب واسعة النطاق التي أجريت على موارد دعائية متوفرة (IE، NLP4IF'19 و Semeval'y-Task Task 11) تشير إلى أن النهج المقترح، والاستفادة من طرازات لغة مختلفة والسمات اللغوية التي تم التحقيق فيها، تحقق نتائج واعدة للغاية حول تصنيف الدعاية، سواء - وفي مستوى الشظية.
One of the mechanisms through which disinformation is spreading online, in particular through social media, is by employing propaganda techniques. These include specific rhetorical and psychological strategies, ranging from leveraging on emotions to exploiting logical fallacies. In this paper, our goal is to push forward research on propaganda detection based on text analysis, given the crucial role these methods may play to address this main societal issue. More precisely, we propose a supervised approach to classify textual snippets both as propaganda messages and according to the precise applied propaganda technique, as well as a detailed linguistic analysis of the features characterising propaganda information in text (e.g., semantic, sentiment and argumentation features). Extensive experiments conducted on two available propagandist resources (i.e., NLP4IF'19 and SemEval'20-Task 11 datasets) show that the proposed approach, leveraging different language models and the investigated linguistic features, achieves very promising results on propaganda classification, both at sentence- and at fragment-level.
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