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Register a new userMeasuring the Impact of Readability Features in Fake News Detection
اكتشاف الأخبار المزيفة اعتماداً على معيار سهولة القراءة (مقروئية)
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LREC 2020 ورقة بحثية
Publication date
2020
fields
Informatics Engineering
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English
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يعد انتشار الأخبار المزيفة من القضايا الحالية التي تؤثر على عدد من المجالات المهمة في المجتمع ، مثل السياسة والاقتصاد والصحة. في مجال معالجة اللغة الطبيعية ، حاولت المبادرات الأخيرة الكشف عن الأخبار المزيفة بطرق مختلفة، بدءًا من الأساليب القائمة على اللغة إلى التحقق القائم على المحتوى. في مثل هذه الأساليب ، يعد اختيار ميزات تصنيف الأخبار الكاذبة والحقيقية أحد أهم أجزاء العملية. تقدم هذه الورقة دراسة حول تأثير ميزات سهولة القراءة للكشف عن الأخبار المزيفة للغة البرتغالية البرازيلية. تظهر النتائج أن هذه الميزات ذات صلة بالمهمة (تحقق بمفردها دقة تصنيف تصل إلى 92٪) وقد تحسن نتائج التصنيف السابقة. (بحث انكليزي)
The proliferation of fake news is a current issue that influences a number of important areas of society, such as politics, economy and health. In the Natural Language Processing area, recent initiatives tried to detect fake news in different ways, ranging from language-based approaches to content-based verification. In such approaches, the choice of the features for the classification of fake and true news is one of the most important parts of the process. This paper presents a study on the impact of readability features to detect fake news for the Brazilian Portuguese language. The results show that such features are relevant to the task (achieving, alone, up to 92% classification accuracy) and may improve previous classification results.
References used
Perez-Rosas, V., Kleinberg, B., Lefevre, A., and Mihalcea, ´ R. (2017). Automatic detection of fake news. CoRR, abs/1708.07104.
Perez-Rosas, V. and Mihalcea, R. (2015). Experiments in ´ open domain deception detection. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 1120–1125.
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