Behavioral Prediction for Objectionable Content Filtering
Date Issued
2015
Date
2015
Author(s)
Lee, Lung-Hao
Abstract
This dissertation proposes user-behavior-based models to identify and filter objectionable content, such as pornography, gambling, violence, and drugs, for protecting children or anyone else from inappropriate materials during their web surfing. From users’ behavioral perspectives, this dissertation can be divided into two parts, which are introduced as follows. The first part is mining searching behaviors for collaborative cyperporn filtering. We present the search-intent-based methods to generate and update pornographic blacklists for filtering the major objectionable category. Searches-and-clicks keeping in the server-side query logs can be effectively exploited for tagging the categories of users’ clicked URLs without the help of analyzing any page content. Our proposed methods can be adopted to help the search engines to mask objectionable results for child suitability purpose. The second part is mining browsing behaviors for objectionable content filtering. In addition to retrieving the information via search engines, users have many alternatives to access other kinds of objectionable web content. We further explore users’ browsing intents to predict the category of a user’s next access and apply the results to filter objectionable content. Client-side click-through data is evaluated to demonstrate the feasibility of predicting categories without the necessity of crawling page content for machine learning. Traditional filtering techniques regard this research problem as categorization through intelligent content analysis. Different from this research line, we propose another direction via behavioral mining. In practices, our proposed prediction models are complementary to the existing solutions by mining users’ behaviors.
Subjects
users’ behavioral mining
context-aware prediction
web content filtering
child protection
categorization.
SDGs
Type
thesis
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