.png){kind=icon}
Return
Identity Deception Prevention Using Common Contribution Network Data
.png){kind=icon}
PRE
AI
DOI：10.1109/TIFS.2016.2607697.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Identity deception in social media applications has negatively impacted online communities and it is likely to increase as the social media user population grows. The ease of generating new accounts on social media has exacerbated the issue. Many previous studies have been posited that focused on both verbal, non-verbal, and network data produced by users in an attempt to detect identity deception. However, although these methods produced a high accuracy, they are mainly reactive to the issue of identity deception. This paper proposes a proactive approach that leverages social network data and it is focused on identity deception prevention for online sub-communities, communities that exist within larger communities (e.g., Facebook groups or Subreddits). The method can be applied to various types of social media applications and produces high accuracy in identifying deceptive accounts at the time of attempted entry to a subcommunity. Performance results as well as limitations for the method are presented. A discussion follows on the identification of possible implications of this paper for social media applications and future directions on deception prevention are proposed.
Keywords:
Identity
deception
prevention
network
data
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
8
Papers:
5.3K
Citations:
2.3W
Organization
Cited Papers
.png){kind=icon}
ShareSaveThe Relationship Between a Leaf-rolling Moth (Dactylioglypha tonica) and Fungi Covering the Cocoon
PREAI
ShareSave
ShareSaveShareSave
View more