Organizational intrusion: Organization mining using socialbots

Aviad Elishar, Michael Fire, Dima Kagan, Yuval Elovici

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

21 Scopus citations

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.

Original languageEnglish
Title of host publicationProceedings of the 2012 ASE International Conference on Social Informatics, SocialInformatics 2012
PublisherIEEE Computer Society
Pages7-12
Number of pages6
ISBN (Print)9780769550152
DOIs
StatePublished - 1 Jan 2012
Event2012 ASE International Conference on Social Informatics, SocialInformatics 2012 - Washington, D.C., United States
Duration: 14 Dec 201216 Dec 2012

Publication series

NameProceedings of the 2012 ASE International Conference on Social Informatics, SocialInformatics 2012

Conference

Conference2012 ASE International Conference on Social Informatics, SocialInformatics 2012
Country/TerritoryUnited States
CityWashington, D.C.
Period14/12/1216/12/12

Keywords

  • Community Detection
  • Organization Mining
  • Social Networks
  • Socialbots

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