NDLI: Privacy Detective: Detecting Private Information and Collective Privacy Behavior in a Large Social Network

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Privacy Detective: Detecting Private Information and Collective Privacy Behavior in a Large Social Network

Content Provider	ACM Digital Library
Author	Greenstadt, Rachel Walsh, Jonathan Caliskan Islam, Aylin
Abstract	Detecting the presence and amount of private information being shared in online media is the first step towards analyzing information revealing habits of users in social networks and a useful method for researchers to study aggregate privacy behavior. In this work, we aim to find out if text contains private content by using our novel learning based approach `privacy detective' that combines topic modeling, named entity recognition, privacy ontology, sentiment analysis, and text normalization to represent privacy features. Privacy detective investigates a broader range of privacy concerns compared to previous approaches that focus on keyword searching or profile related properties. We collected 500,000 tweets from 100,000 Twitter users along with other information such as tweet linkages and follower relationships. We reach 95.45% accuracy in a two-class task classifying Twitter users who do not reveal much private information and Twitter users who share sensitive information. We score timelines according to three privacy levels after having Amazon Mechanical Turk (AMT) workers annotate collected tweets according to privacy categories. Supervised machine learning classification results on these annotations reach 69.63% accuracy on a three-class task. Inter-annotator agreement on timeline privacy scores between various AMT workers and our classifiers fall under the same positive agreement level. Additionally, we show that a user's privacy level is correlated with her friends' privacy scores and also with the privacy scores of people mentioned in her text but not with the number of her followers. As such, privacy in social networks appear to be socially constructed, which can have great implications for privacy enhancing technologies and educational interventions.
Starting Page	35
Ending Page	46
Page Count	12
File Format	PDF
ISBN	9781450331487
DOI	10.1145/2665943.2665958
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-11-03
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Privacy Privacy behavior Detecting private information Sensitive information Social network Text classification
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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