Một bài đánh giá phạm vi về việc sử dụng xử lý ngôn ngữ tự nhiên trong nghiên cứu về phân cực chính trị: xu hướng và triển vọng nghiên cứu

Journal of Computational Social Science - Tập 6 - Trang 289-313 - 2022
Renáta Németh1
1Research Center for Computational Social Science, Faculty of Social Sciences, ELTE Eötvös Loránd University, Budapest, Hungary

Tóm tắt

Là một phần của phong trào “văn bản như dữ liệu”, Xử lý Ngôn ngữ Tự nhiên (NLP) cung cấp một phương pháp tính toán để kiểm tra phân cực chính trị. Chúng tôi đã tiến hành một đánh giá phương pháp học thuật về các nghiên cứu được công bố từ năm 2010 (n = 154) để làm sáng tỏ cách mà nghiên cứu NLP đã khái niệm hóa và đo lường phân cực chính trị, và để xác định mức độ hội tụ của hai khuynh hướng nghiên cứu khác nhau trong lĩnh vực nghiên cứu này. Chúng tôi đã phát hiện ra sự thiên lệch đối với bối cảnh Mỹ (59%), dữ liệu Twitter (43%) và phương pháp học máy (33%). Nghiên cứu bao phủ nhiều lớp khác nhau của lĩnh vực công cộng chính trị (các chính trị gia, chuyên gia, truyền thông, hoặc công chúng nói chung), tuy nhiên, rất ít nghiên cứu tham gia vào hơn một lớp. Kết quả cho thấy chỉ một vài nghiên cứu sử dụng kiến thức chuyên ngành và một tỷ lệ cao các nghiên cứu không liên ngành. Những nghiên cứu đã cố gắng diễn giải các kết quả cho thấy rằng các đặc điểm của văn bản chính trị phụ thuộc không chỉ vào vị trí chính trị của tác giả mà còn vào các yếu tố khác thường bị bỏ qua. Bỏ qua những yếu tố này có thể dẫn đến các chỉ số hiệu suất quá lạc quan. Hơn nữa, các kết quả giả có thể được thu được khi các mối quan hệ nguyên nhân được suy ra từ dữ liệu văn bản. Bài báo của chúng tôi đưa ra các lập luận cho việc tích hợp các mô hình giải thích và dự đoán, và cho một cách tiếp cận liên ngành hơn trong nghiên cứu về phân cực.

Từ khóa

#Xử lý ngôn ngữ tự nhiên #phân cực chính trị #nghiên cứu liên ngành #mô hình hóa giải thích #mô hình hóa dự đoán

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