מיכאל פייר

אקדמי בכיר

Organizational intrusion

Organization mining using socialbots

Aviad Elishar, Michael Fire, Dima Kagan, Yuval Elovici

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.

שפת פרסום אנגלית
דפים 7-12
סטטוס פרסום פורסם - 01.01.2012
מספר מאמר 6542415

Keywords

Community Detection
Organization Mining
Social Networks
Socialbots

ASJC Scopus subject areas

Information Systems
Information Systems and Management
קבצים וקישורים אחרים
Link to publication in Scopus