کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
11002123 | 1434602 | 2018 | 39 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A holistic system for troll detection on Twitter
ترجمه فارسی عنوان
سیستم جامع برای تشخیص ترول در توییتر
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کلمات کلیدی
شناسایی ترول، فراگیری ماشین، شبکه های اجتماعی آنلاین،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزارهای علوم کامپیوتر
چکیده انگلیسی
Various techniques based on artificial intelligence have been proposed for the automatic detection of online anti-social behaviors, both in existing systems and in the scientific literature. In this article, we describe TrollPacifier, a holistic system for troll detection, which analyses many different features of trolls and legitimate users on the popular Twitter platform. In this system, the most known and promising approaches and research lines are applied, along with original new ideas, in a form that fits such a large public platform. In particular, we have identified six groups of features, based respectively on the analysis of writing style, sentiment, behaviors, social interactions, linked media, and publication time. As its main scientific contributions, this work provides: (i) an up-to-date analysis of the state of the art for the problem of troll detection; (ii) the systematic collection and grouping of features, on Twitter; (iii) the description of a working holistic system for troll detection, with a very high accuracy (95.5%); and (iv) a comparison among the different features, with a machine learning approach. Our results demonstrate that automatic classification can be useful in the whole process of identification and management of online anti-social behaviors. However, a multi-faceted approach is required, in order to obtain an adequate accuracy.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers in Human Behavior - Volume 89, December 2018, Pages 258-268
Journal: Computers in Human Behavior - Volume 89, December 2018, Pages 258-268
نویسندگان
Paolo Fornacciari, Monica Mordonini, Agostino Poggi, Laura Sani, Michele Tomaiuolo,