کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
484832 703295 2015 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Automated Detection of Human Users in Twitter
ترجمه فارسی عنوان
تشخیص خودکار کاربران انسانی در توییتر
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر علوم کامپیوتر (عمومی)
چکیده انگلیسی

This paper compares Suppport Vector Machine (SVM) classification and a number of clustering approaches to separate human from not human users in Twitter in order to identify normal human activity. These approaches have similar F1 accuracy scores of 90% with both experienc- ing difficulties in classifying human users behaving abnormally. A second stage classification step was then used to further separate not human users into brands, celebrities and promoters / information achieving an average F1 accuracy of 74%. These accuracies were achieved by reducing the size of the feature space using stepwise feature selection and category balancing from manual inspection of classification results.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Procedia Computer Science - Volume 53, 2015, Pages 224-231