Purpose

The proliferation of bots in social networks has profoundly affected the interactions of legitimate users. Detecting and rejecting these unwelcome bots has become part of the collective Internet agenda. Unfortunately, as bot creators use more sophisticated approaches to avoid being discovered, it has become increasingly difficult to distinguish social bots from legitimate users. Therefore, this paper proposes a novel social bot detection mechanism to adapt to new and different kinds of bots.

Design/methodology/approach

This paper proposes a research framework to enhance the generalization of social bot detection from two dimensions: feature extraction and detection approaches. First, 36 features are extracted from four views for social bot detection. Then, this paper analyzes the feature contribution in different kinds of social bots, and the features with stronger generalization are proposed. Finally, this paper introduces outlier detection approaches to enhance the ever-changing social bot detection.

Findings

The experimental results show that the more important features can be more effectively generalized to different social bot detection tasks. Compared with the traditional binary-class classifier, the proposed outlier detection approaches can better adapt to the ever-changing social bots with a performance of 89.23 per cent measured using the F1 score.

Originality/value

Based on the visual interpretation of the feature contribution, the features with stronger generalization in different detection tasks are found. The outlier detection approaches are first introduced to enhance the detection of ever-changing social bots.

中文翻译：

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从两个维度研究社交机器人检测的泛化：特征提取和检测方法

目的

机器人在社交网络中的激增已经深刻地影响了合法用户的交互。检测并拒绝这些不受欢迎的机器人程序已成为互联网集体议程的一部分。不幸的是，随着机器人创建者使用更复杂的方法来避免被发现，将社交机器人与合法用户区分开来变得越来越困难。因此，本文提出了一种新颖的社交机器人检测机制，以适应新的和不同种类的机器人。

设计/方法/途径

本文提出了一个研究框架，从两个维度增强社交机器人检测的泛化：特征提取和检测方法。首先，从四个视图中提取 36 个特征用于社交机器人检测。然后，本文分析了不同类型社交机器人的特征贡献，并提出了具有更强泛化性的特征。最后，本文介绍了异常值检测方法，以增强不断变化的社交机器人检测。

发现

实验结果表明，更重要的特征可以更有效地泛化到不同的社交机器人检测任务。与传统的二元类分类器相比，所提出的异常值检测方法可以更好地适应不断变化的社交机器人，使用 F1 分数测量的性能为 89.23%。

原创性/价值

基于对特征贡献的视觉解释，找到在不同检测任务中具有更强泛化性的特征。首先引入异常值检测方法以增强对不断变化的社交机器人的检测。