Citation

Talukder S, Carbunar B. ACM Trans. Soc. Comput. 2020.

Copyright

(Copyright © 2020, Association for Computing Machinery)

DOI

10.1145/3408040

PMID

unavailable

Abstract

Social networks like Facebook provide functionality that can expose users to abuse perpetrated by their contacts. For instance, Facebook users can often access sensitive profile information and timeline posts of their friends and also post abuse on the timeline and news feed of their friends. In this article, we introduce AbuSniff, a system to identify Facebook friends perceived to be abusive or strangers and protect the user by restricting the access to information for such friends. We develop a questionnaire to detect perceived strangers and friend abuse. We train supervised learning algorithms to predict questionnaire responses using features extracted from the mutual activities with Facebook friends. In our experiments, participants recruited from a crowdsourcing site agreed with 78% of the defense actions suggested by AbuSniff, without having to answer any questions about their friends. When compared to a control app, AbuSniff significantly increased the willingness of participants to take a defensive action against friends. AbuSniff also increased the participant self-reported willingness to reject friend invitations from strangers and abusers, their awareness of friend abuse implications, and their perceived protection from friend abuse.

Language: en