Achieving Online and Scalable Information Integrity by Harnessing Social Spam Correlations
Achieving Online and Scalable Information Integrity by Harnessing Social Spam Correlations
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DOI：
10.1109/access.2023.3236604
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发表时间：
2023
期刊：
影响因子：
3.9
通讯作者：
Hailu Xu;Pinchao Liu;Boyuan Guan;Qingyang Wang;Dilma Da Silva;Liting Hu
中科院分区：
文献类型：
--
作者：
Hailu Xu;Pinchao Liu;Boyuan Guan;Qingyang Wang;Dilma Da Silva;Liting Hu
恶意的链接，社交谣言，欺诈性广告，伪造的评论和有偏见的宣传，主要影响在线社交网络。检测命名为螺旋式的系统，通过利用地理位置的不同社交数据来源之间的相关性，我们的方法中的关键洞察力。部署在多个在线社交网络社区中的收获和欺骗性信息；社交数据表明，在社交网络的在线垃圾邮件检测中，螺旋的可扩展性，有效的负载平衡以及效率。
Malicious web links, social rumors, fraudulent advertisements, faked comments, and biased propaganda are overwhelmingly influencing online social networks. Enabling information integrity is a hot topic in both academia and industry. Traditional social spam detection techniques rely on centralized processing, focusing only on one specific set of data sources, thereby ignoring the social spam correlations between distributed data sources. In this paper, we propose an online and scalable misinformation detection system, named Spiral, to uncover social spam by leveraging the correlations between different social data sources in geo-distributed sites. The key insight in our approach is to amplify the effectiveness of state-of-the-art techniques to detect inappropriate posts by enabling the efficient large-scale propagation of detection information across domains. The novelty of our design lies in three key components: (1) a decentralized distributed hash-table-based tree overlay deployment for harvesting and uncovering deceptive information spreading in multiple online social networks communities; (2) a progressive aggregation tree for collecting the properties of these posts and creating new classifiers to actively filter out the propagation of inappropriate posts; and (3) a group communication structure that allows multiple groups to exchange the correlations among distributed social data sources. We designed and implemented a prototype of the Spiral system. Our large-scale experiments, using real-world social data, demonstrate Spiral’s scalability, effective load-balancing, and efficiency in online spam detection for social networks.