通过分析用户资料与内容特征，提升社交网络中机器人账号的检测精度。
Exploring social bots: A feature-based approach to improve bot detection in social networks
- 基于用户资料和内容特征构建检测模型
- 在多个指标上超越现有最佳水平
- 揭示对检测最关键的特征类型
社交媒体在日常生活中日益重要，却也导致虚假信息、政治宣传和恶意链接传播加剧。自动化账户（即机器人）是实现这些活动的主要手段，因此检测此类账户至关重要。本文通过研究基于用户账户资料和内容的特征，探究各特征在改进未来机器人检测器中的有效性。经过全面的调研、推断与特征选择，我们采用经典机器学习算法，在多个指标上超越了当前最优方法，并识别出对检测自动化账户最为关键的特征类型。
原文摘要 · Abstract (English)
The importance of social media in our daily lives has unfortunately led to an increase in the spread of misinformation, political messages and malicious links. One of the most popular ways of carrying out those activities is using automated accounts, also known as bots, which makes the detection of such accounts a necessity. This paper addresses that problem by investigating features based on the user account profile and its content, aiming to understand the relevance of each feature as a basis for improving future bot detectors. Through an exhaustive process of research, inference and feature selection, we are able to surpass the state of the art on several metrics using classical machine learning algorithms and identify the types of features that are most important in detecting automated accounts.
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