Title :
Organizational Intrusion: Organization Mining Using Socialbots
Author :
Elishar, A. ; Fire, Michael ; Kagan, Dima ; Elovici, Yuval
Author_Institution :
Inf. Syst. Eng. Dept., Ben-Gurion Univ. of the Negev, Beer-Sheva, Israel
Abstract :
In the recent years we have seen a significant growth in the usage of online social networks. Common networks like Facebook, Twitter, Pinterest, and Linked In have become popular all over the world. In these networks users write, share, and publish personal information about themselves, their friends, and their workplace. In this study we present a method for the mining of information of an organization through the use of social networks and social bots. Our social bots sent friend requests to Facebook users who work in a targeted organization. Upon accepting a socialbot´s friend request, users unknowingly expose information about themselves and about their workplace. We tested the proposed method on two real organizations and successfully infiltrated both. Compared to our previous study, our method was able to discover up to 13.55% more employees and up to 18.29% more informal organizational links. Our results demonstrate once again that organizations which are interested in protecting themselves should instruct their employees not to disclose information in social networks and to be cautious of accepting friendship requests from unknown persons.
Keywords :
data mining; information management; security of data; social networking (online); Facebook; Linked In; Pinterest; Twitter; information mining; information publishing; information sharing; information writing; online social network; organization mining; organizational intrusion; organizational link; social bots; Community Detection; Organization Mining; Social Networks; Socialbots;
Conference_Titel :
Social Informatics (SocialInformatics), 2012 International Conference on
Conference_Location :
Lausanne
Print_ISBN :
978-1-4799-0234-7
DOI :
10.1109/SocialInformatics.2012.39