A Collaborative Method for Protecting Teens Against Online Predators Over Social Networks: A Behavioral Analysis

Abstract

Online social networks have gained ubiquitous popularity in the last decade but despite the advantages, they have also raised privacy concerns for users. Many people have lost their jobs, relationships, and are stalked because of online privacy breaches. This situation is even more disturbing for young teens as they are in the process of learning emotional, social, and physical behaviors. Online predators take advantage of their immaturity and raise various threats to teens including aggression, rape, abduction, physical and emotional sadistic torture, and even human trafficking. Teens, on the other hand, do not have any mechanism to protect them against such threats. Therefore, this research addresses this overlooked need and proposes a trust model for the teen community, which can evaluate the trustworthiness of a stranger based on teens" psychological and social needs. This paper first identifies various factors of teen psychology from literature, and then proposes a trust model for teens based on those factors. The model ensures the trustworthiness of a stranger through a two-dimension matrix consists of his reputation in other teens and reliability. The proposed model was simulated through colored Petri nets and implemented as a real-time trust evaluation application over Facebook. A user acceptance testing was also performed by teens, which suggests that 81.77% of teens were overall satisfied with the proposed approach.

Improving misspelled word solving for human trafficking detection in online advertising data

“…Machine learning [3] is one branch of artificial intelligence that focuses on teaching machines through data to build models to achieve goals, such as text classification, face detection, and face recognition. Datasets can be collected in various ways, including application programming interfaces (API) [4], organizations, and crowdsourcing [5]. However, the quality of the data strongly affects model performance, 6559 such as misspelled words [6].…”