كشف العداء عبر الإنترنت: تحليل وتخفيف خطاب الكراهية في وسائل التواصل الاجتماعي

المؤلفون

  • Jawaid Ahmed Siddiqui كلية رزاك للتكنولوجيا والمعلوماتية، جامعة التكنولوجيا ماليزيا، كوالالمبور، ماليزيا. https://orcid.org/0009-0005-7882-4776
  • Siti Sophiayati Yuhaniz كلية رزاك للتكنولوجيا والمعلوماتية، جامعة التكنولوجيا ماليزيا، كوالالمبور، ماليزيا.
  • Zulfiqar Ali Memon المدرسة السريعة للحوسبة، الجامعة الوطنية للحاسوب والعلوم الناشئة، كراتشي، باكستان.

DOI:

https://doi.org/10.21123/bsj.2024.10743

الكلمات المفتاحية:

كشف خطاب الكراهية، التعلم الالي؛ معالجة اللغة الطبيعية، وسائل التواصل الاجتماعي، تصنيف النص

الملخص

تعمل منصات التواصل الاجتماعي على توليد كمية هائلة من البيانات في كل ثانية. تويتر، من الناحية العملية، ينتج الأفراد أكثر من ستمائة تغريدة في كل ثانية. أثناء نشر آراء المستخدمين وتعبيراتهم بحرية، من الصعب جدًا حصر خطاب الكراهية الذي يتم مشاركته ضد أي فرد أو دين أو أي مجموعة عرقية. وبالتالي، فإن الأشخاص المستهدفين بمثل هذا المحتوى الذي يحض على الكراهية يشعرون بالإحباط. وفي هذا الصدد، قامت الأساليب المختلفة بحل هذه المشكلة الخطيرة، ولكنها في بعض الأحيان لم تتمكن من تحقيق نتائج مرضية. ولذلك، نقترح نماذج مختلفة للتعلم الآلي لتصنيف البيانات المعطاة إلى فئتين، مسيئة أو غير مسيئة. تم إجراء التجارب على بيانات تويتر التي أنشأناها بأنفسنا باستخدام Twitter API ومكتبة Tweepy بواسطة Python. تم تقييم النتائج الناتجة بناءً على مقاييس مختلفة مثل الدقة والدقة والاستدعاء وقياس F1 واختبار MCNEMAR. بالمقارنة مع خوارزميات التعلم الآلي المختلفة، تفوق مصنف مجموعة الغابات العشوائية على الخوارزميات الأخرى، فإن حداثة ومساهمة ورقتنا البحثية هي: تطوير مجموعة بيانات تويتر التي تتكون من عدة تغريدات تحتوي على 11 متغير كائن مع أربعة متغيرات فئة مختلفة تظهر الهجوم المختلف المستويات، وتطبيق خوارزميات التعلم الآلي للكشف عن خطاب الكراهية، والتحليل المقارن لخوارزميات التعلم الآلي المختلفة مقابل مقاييس تقييم مختلفة بما في ذلك اختبار ماكنيمار. يتم شرح أهمية التقنية المقترحة جيدًا من خلال مجموعات بيانات Twitter التي تم إنشاؤها من خلال Twitter API ومكتبة Tweepy بواسطة Python.

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كيفية الاقتباس

1.
كشف العداء عبر الإنترنت: تحليل وتخفيف خطاب الكراهية في وسائل التواصل الاجتماعي. Baghdad Sci.J [انترنت]. [وثق 21 نوفمبر، 2024];22(6). موجود في: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/10743