{"title":"CrossSimON: A Novel Probabilistic Approach to Cross-Platform Online Social Network Simulation","authors":"Jinwei Liu, Wingyan Chung, Yifan Huang, Cagri Toraman","doi":"10.1109/ISI.2019.8823276","DOIUrl":null,"url":null,"abstract":"The increasing popularity and diversity of online social networks (OSNs) have attracted more and more people to participate in multiple OSNs. Learning users' behavior and information diffusion across platforms is critical for cyber threat detection, but it is still a challenge due to the surge of users participating in multiple social platforms. Existing research on profile matching requires user identity information to be available, which may not be realistic. Little prior research payed attention to mapping behavioral patterns across platforms. We designed and implemented an efficient two-level probabilistic approach called CrossSimON to mapping user-group behavior across platforms. CrossSimON considers the activity level and network position at both individual user level and group level to correlate activities across social platforms. To evaluate the effectiveness of CrossSimON in modeling social activity across platforms, we conducted experiments on three online social platforms: GitHub, Reddit and Twitter. Our experimental results show that CrossSimON outperformed the Benchmark in 3 out of 5 simulation metrics. CrossSimON achieved better performance in user activity prediction. The research provides new strategy for cross-platform online social network simulation, and new findings on simulating OSNs and predictive analytics for understanding online social network behavior.","PeriodicalId":156130,"journal":{"name":"2019 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE International Conference on Intelligence and Security Informatics (ISI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISI.2019.8823276","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
The increasing popularity and diversity of online social networks (OSNs) have attracted more and more people to participate in multiple OSNs. Learning users' behavior and information diffusion across platforms is critical for cyber threat detection, but it is still a challenge due to the surge of users participating in multiple social platforms. Existing research on profile matching requires user identity information to be available, which may not be realistic. Little prior research payed attention to mapping behavioral patterns across platforms. We designed and implemented an efficient two-level probabilistic approach called CrossSimON to mapping user-group behavior across platforms. CrossSimON considers the activity level and network position at both individual user level and group level to correlate activities across social platforms. To evaluate the effectiveness of CrossSimON in modeling social activity across platforms, we conducted experiments on three online social platforms: GitHub, Reddit and Twitter. Our experimental results show that CrossSimON outperformed the Benchmark in 3 out of 5 simulation metrics. CrossSimON achieved better performance in user activity prediction. The research provides new strategy for cross-platform online social network simulation, and new findings on simulating OSNs and predictive analytics for understanding online social network behavior.
网络社交网络(online social network, osn)的日益普及和多样化,吸引了越来越多的人参与到多个网络社交网络中。了解用户跨平台的行为和信息扩散对网络威胁检测至关重要,但由于参与多个社交平台的用户激增,这仍然是一个挑战。现有的配置文件匹配研究要求用户身份信息可用,这可能不太现实。之前很少有研究关注跨平台的行为模式映射。我们设计并实现了一种称为CrossSimON的有效的两级概率方法来映射跨平台的用户组行为。CrossSimON考虑个人用户和群体级别的活动水平和网络位置,以关联社交平台上的活动。为了评估CrossSimON对跨平台社交活动建模的有效性,我们在三个在线社交平台上进行了实验:GitHub、Reddit和Twitter。我们的实验结果表明,CrossSimON在5个模拟指标中的3个方面优于Benchmark。CrossSimON在用户活动预测方面取得了较好的效果。该研究为跨平台在线社交网络模拟提供了新策略,并为网络社交网络行为的模拟和预测分析提供了新发现。