{"title":"为什么社交机器人在Twitter上是有效的?统计见解","authors":"Mohd Fazil, M. Abulaish","doi":"10.1109/COMSNETS.2017.7945454","DOIUrl":null,"url":null,"abstract":"Twitter, a popular microblogging platform, facilitates users to express views and thoughts on any topic of discussion using short messaging texts limited to 140 characters. Due to its open and real-time information sharing and dissemination nature, it is abused by socialbots for political astroturfing, advertising, spamming, and other illicit activities. To this end, we injected an army of 98 socialbots associated to top six Twitter using countries to study socialbots’ infiltration behaviour. In this paper, we present a statistical insight derived through the analysis of the captured data by our socialbots. Socialbots’ profile features, such as age, gender, etc. and their behavioural impact on infiltration performance are studied and presented, wherein a user's following activity to a socialbot is considered as an infiltration. Experimental results and subsequent statistical analyses show that socialbots’ profiles belonging to India were the successful in duping highest number of users, whereas Indonesian socialbots were least infiltrative. Moreover, among various Twitter activities, following is found to be the most effective activity for infiltrating a user. Among the intruded users, trace of the presence of botnets, spammers, and other malicious users have also been observed and presented in this paper.","PeriodicalId":168357,"journal":{"name":"2017 9th International Conference on Communication Systems and Networks (COMSNETS)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"19","resultStr":"{\"title\":\"Why a socialbot is effective in Twitter? A statistical insight\",\"authors\":\"Mohd Fazil, M. Abulaish\",\"doi\":\"10.1109/COMSNETS.2017.7945454\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Twitter, a popular microblogging platform, facilitates users to express views and thoughts on any topic of discussion using short messaging texts limited to 140 characters. Due to its open and real-time information sharing and dissemination nature, it is abused by socialbots for political astroturfing, advertising, spamming, and other illicit activities. To this end, we injected an army of 98 socialbots associated to top six Twitter using countries to study socialbots’ infiltration behaviour. In this paper, we present a statistical insight derived through the analysis of the captured data by our socialbots. Socialbots’ profile features, such as age, gender, etc. and their behavioural impact on infiltration performance are studied and presented, wherein a user's following activity to a socialbot is considered as an infiltration. Experimental results and subsequent statistical analyses show that socialbots’ profiles belonging to India were the successful in duping highest number of users, whereas Indonesian socialbots were least infiltrative. Moreover, among various Twitter activities, following is found to be the most effective activity for infiltrating a user. Among the intruded users, trace of the presence of botnets, spammers, and other malicious users have also been observed and presented in this paper.\",\"PeriodicalId\":168357,\"journal\":{\"name\":\"2017 9th International Conference on Communication Systems and Networks (COMSNETS)\",\"volume\":\"91 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"19\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 9th International Conference on Communication Systems and Networks (COMSNETS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/COMSNETS.2017.7945454\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 9th International Conference on Communication Systems and Networks (COMSNETS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/COMSNETS.2017.7945454","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Why a socialbot is effective in Twitter? A statistical insight
Twitter, a popular microblogging platform, facilitates users to express views and thoughts on any topic of discussion using short messaging texts limited to 140 characters. Due to its open and real-time information sharing and dissemination nature, it is abused by socialbots for political astroturfing, advertising, spamming, and other illicit activities. To this end, we injected an army of 98 socialbots associated to top six Twitter using countries to study socialbots’ infiltration behaviour. In this paper, we present a statistical insight derived through the analysis of the captured data by our socialbots. Socialbots’ profile features, such as age, gender, etc. and their behavioural impact on infiltration performance are studied and presented, wherein a user's following activity to a socialbot is considered as an infiltration. Experimental results and subsequent statistical analyses show that socialbots’ profiles belonging to India were the successful in duping highest number of users, whereas Indonesian socialbots were least infiltrative. Moreover, among various Twitter activities, following is found to be the most effective activity for infiltrating a user. Among the intruded users, trace of the presence of botnets, spammers, and other malicious users have also been observed and presented in this paper.