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Register a new userUsing Noisy Self-Reports to Predict Twitter User Demographics
باستخدام تقارير ذاتية صاخبة للتنبؤ برطب مستخدم Twitter
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غالبا ما تكون دراسات العلوم الاجتماعية الحاسوبية تحليل المحتوى في كثير من الأحيان داخل التركيبة السكانية القياسية.نظرا لأن التركيبة السكانية غير متوفرة على العديد من منصات وسائل التواصل الاجتماعي (E.G. Twitter)، فقد استنتجت الدراسات العديد من الدراسات التركيبة السكانية تلقائيا.على الرغم من العديد من الدراسات التي تقدم أداء مفهوم العرق والعرق، لا يزال تدريب النظم العملية بعيد المنال لأن هناك بعض البيانات المشروح.مجموعات البيانات الحالية صغيرة وغير دقيقة، أو تفشل في تغطية المجموعات العرقية والأعرقية الأربعة الأكثر شيوعا في الولايات المتحدة.نقدم طريقة لتحديد التقارير الذاتية عن العرق والعرق من أوصاف الملف الشخصي Twitter.على الرغم من ضجيج الإشراف الآلي، فإن مجموعات بيانات التقرير الذاتي لدينا تمكن التحسينات في أداء التصنيف على بيانات مسح التقارير الذاتية القياسية الذهبية.والنتيجة هي طريقة استنساخ لإنشاء موارد تدريبية واسعة النطاق للسباق والعرق.
Computational social science studies often contextualize content analysis within standard demographics. Since demographics are unavailable on many social media platforms (e.g. Twitter), numerous studies have inferred demographics automatically. Despite many studies presenting proof-of-concept inference of race and ethnicity, training of practical systems remains elusive since there are few annotated datasets. Existing datasets are small, inaccurate, or fail to cover the four most common racial and ethnic groups in the United States. We present a method to identify self-reports of race and ethnicity from Twitter profile descriptions. Despite the noise of automated supervision, our self-report datasets enable improvements in classification performance on gold standard self-report survey data. The result is a reproducible method for creating large-scale training resources for race and ethnicity.
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