Zombie followers, a type of bot, are longstanding entities in Sina Weibo. Although the features and detection of zombie followers have been extensively studied, zombie followers are continuously increasing in social networks and gradually developing into a large-scale industry. In this study, we analyze the features of eight groups of zombie followers from different companies. The findings indicate that although zombie followers controlled by different companies vary greatly, some industries may be controlled by the same organization. Based on the feature analysis, we use multiple machine learning methods to detect zombie followers, and the results show that zombie follower groups with short registration time are more easily detected. The detection accuracy of zombie followers that have been cultivated for a long duration is low. Moreover, the richer the feature sets, the higher the recall, precision, and F₁ of their detection results will be. Under a given rich feature set, the accuracy of the combined-group detection is not as high as that of the single-group detection. The random forest achieves the highest accuracy in both single- and combined-group detections, yielding 99.14% accuracy in the latter case.

中文翻译：

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基于产业链特征分析的僵尸追随者识别

僵尸追随者是一种机器人，是新浪微博中长期存在的实体。尽管已经对僵尸追随者的特征和检测进行了广泛的研究，但是僵尸追随者在社交网络中的数量不断增加，并逐渐发展为大型行业。在这项研究中，我们分析了来自不同公司的八组僵尸追随者的特征。研究结果表明，尽管由不同公司控制的僵尸追随者差异很大，但某些行业可能由同一组织控制。基于特征分析，我们使用多种机器学习方法来检测僵尸追随者，结果表明，注册时间短的僵尸追随者群体更容易被检测到。长期耕作的僵尸追随者的检测准确性低。此外，F ₁将其检测结果。在给定的丰富功能集下，组合组检测的准确性不如单个组检测的准确性高。随机森林在单组和组合组检测中均达到最高的准确性，在后一种情况下，其准确性为99.14％。