@inproceedings{a9e78a67bcf4432ea3eb6bb8f0ee52d3,
title = "Organizational intrusion: Organization mining using socialbots",
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 = "Community Detection, Organization Mining, Social Networks, Socialbots",
author = "Aviad Elishar and Michael Fire and Dima Kagan and Yuval Elovici",
year = "2012",
month = jan,
day = "1",
doi = "10.1109/SocialInformatics.2012.39",
language = "American English",
isbn = "9780769550152",
series = "Proceedings of the 2012 ASE International Conference on Social Informatics, SocialInformatics 2012",
pages = "7--12",
booktitle = "Proceedings of the 2012 ASE International Conference on Social Informatics, SocialInformatics 2012",
note = "2012 ASE International Conference on Social Informatics, SocialInformatics 2012 ; Conference date: 14-12-2012 Through 16-12-2012",
}