维基百科中协作破坏行为的可解释可视化

S. Subramanian, P. Pushparaj, Zerong Liu, Aidong Lu
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引用次数: 2

摘要

在线社交网络很容易成为各种欺诈和攻击的目标,这些欺诈和攻击由于其复杂性和多样性而难以检测。挑战在于为不同的用户群体使用合适的探索工具来理解所有的信息。这个项目专注于一个可解释的可视化方法来研究维基百科上破坏者的协作行为。我们的方法使用制图和统计图形中常用的技术来创建可视化,这些技术为公众所熟悉,具有有效性和可解释性。我们建立了一个大规模的可视化系统,该系统支持一个具有多种数据查询、过滤、分析和交互式探索功能的说明性界面。提供了示例和案例研究来证明我们的方法可以有效地用于一组维基百科行为分析任务。
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Explainable Visualization of Collaborative Vandal Behaviors in Wikipedia
Online social networks are prone to be targeted by various frauds and attacks, which are difficult to detect due to their complexity and variations. The challenge is to make sense of all information with suitable exploration tools for different groups of users. This project focuses on an explainable visualization approach to study collaborative behaviors of vandal users on Wikipedia. Our approach creates visualization with commonly used techniques from cartography and statistical graphics that are familiar to the general public for effectiveness and explainability. We have built a large-scale visualization system which supports an illustrative interface with multiple data query, filtering, analysis, and interactive exploration functions. Examples and case studies are provided to demonstrate that our approach can be used effectively for a set of Wikipedia behavior analysis tasks.
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VizSec 2019 Foreword VizSec 2019 Committees [Copyright notice] VizSec 2019 Keynote A Study on Labeling Network Hostile Behavior with Intelligent Interactive Tools
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