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Register a new userCHoRaL: Collecting Humor Reaction Labels from Millions of Social Media Users
كورالي: جمع ملصقات رد الفعل الفكاهة من ملايين مستخدمي وسائل التواصل الاجتماعي
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اكتسب الكشف عن فكاهة الاهتمام في السنوات الأخيرة بسبب الرغبة في فهم المحتوى الذي تم إنشاؤه من قبل المستخدم بلغة مجازية. ومع ذلك، فإن الخلافات الفردية والثقافية الكبيرة في التصور الفكاهي تجعل من الصعب للغاية جمع مجموعة بيانات الفكاهة على نطاق واسع مع علامات فكاهة موثوقة. نقترح كورالي، وهو إطار لتوليد ملصقات الفكاهة المتصورة على Facebook Works، باستخدام ردود الفعل المستخدم المتاحة بشكل طبيعي على هذه الوظائف مع عدم وجود شرح يدوي مطلوبا. يوفر Choral كل من الملصقات الثنائية والعشرات المستمرة من الفكاهة وغير الفكاهة. نقدم أكبر مجموعة بيانات حتى الآن مع الفكاهة المسمى على المشاركات 785K ذات الصلة إلى Covid-19. بالإضافة إلى ذلك، نقوم بتحليل التعبير عن الفكاهة المرتبطة بالسيارة في وسائل التواصل الاجتماعي عن طريق استخراج ميزات المعمير الدلالية والعاطفية من المشاركات، وبناء نماذج الكشف عن الفكاهة مع أداء مشابه للبشر. يتيح كورالي تطوير نماذج الكشف عن فكاهة واسعة النطاق على أي موضوع ويفتح طريقا جديدا لدراسة الفكاهة على وسائل التواصل الاجتماعي.
Humor detection has gained attention in recent years due to the desire to understand user-generated content with figurative language. However, substantial individual and cultural differences in humor perception make it very difficult to collect a large-scale humor dataset with reliable humor labels. We propose CHoRaL, a framework to generate perceived humor labels on Facebook posts, using the naturally available user reactions to these posts with no manual annotation needed. CHoRaL provides both binary labels and continuous scores of humor and non-humor. We present the largest dataset to date with labeled humor on 785K posts related to COVID-19. Additionally, we analyze the expression of COVID-related humor in social media by extracting lexico-semantic and affective features from the posts, and build humor detection models with performance similar to humans. CHoRaL enables the development of large-scale humor detection models on any topic and opens a new path to the study of humor on social media.
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