ألقى النمو الأسي للإنترنت والوسائط الاجتماعية في العقد الماضي الطريق إلى زيادة نشر المعلومات الخاطئة أو المضللة. منذ الانتخابات الرئاسية الأمريكية لعام 2016، أصبحت مصطلح أخبار وهمية "أصبحت شعبية متزايدة وقد تلقت هذه الظاهرة اهتماما أكبر. في السنوات الماضية، تم إنشاء العديد من وكالات فحص الحقائق، ولكن بسبب عدد كبير من الوظائف اليومية على وسائل التواصل الاجتماعي، والفحص اليدوي غير كاف. حاليا، هناك حاجة ملحة لأدوات الكشف عن الأخبار التلقائي، إما لمساعدة قوائم الداما اليدوية أو التشغيل كأدوات قائمة بذاتها. هناك العديد من المشاريع جارية حول هذا الموضوع، لكن معظمهم يركزون على اللغة الإنجليزية. تناقش ورقة البحث في البحث هذه توظيف أساليب التعلم العميق، وتطوير أداة، للكشف عن الأخبار الخاطئة باللغة البرتغالية. كخطوة أولى، سنقوم بمقارنة الهيغات الراسخة التي تم اختبارها بلغات أخرى وتحليل أدائها على بياناتنا البرتغالية. بناء على النتائج الأولية لهذه المصنفات، يجب أن نختار نموذجا للتعلم العميق أو الجمع بين العديد من نماذج التعلم العميق التي تعاني من وعد لتعزيز أداء نظام الكشف عن الأخبار المزيف.
The exponential growth of the internet and social media in the past decade gave way to the increase in dissemination of false or misleading information. Since the 2016 US presidential election, the term fake news'' became increasingly popular and this phenomenon has received more attention. In the past years several fact-checking agencies were created, but due to the great number of daily posts on social media, manual checking is insufficient. Currently, there is a pressing need for automatic fake news detection tools, either to assist manual fact-checkers or to operate as standalone tools. There are several projects underway on this topic, but most of them focus on English. This research-in-progress paper discusses the employment of deep learning methods, and the development of a tool, for detecting false news in Portuguese. As a first step we shall compare well-established architectures that were tested in other languages and analyse their performance on our Portuguese data. Based on the preliminary results of these classifiers, we shall choose a deep learning model or combine several deep learning models which hold promise to enhance the performance of our fake news detection system.
References used
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