Mining browsing behaviors for objectionable content filtering

Abstract

This article explores users' browsing intents to predict the category of a user's next access during web surfing and applies the results to filter objectionable content, such as pornography, gambling, violence, and drugs. Users' access trails in terms of category sequences in click‐through data are employed to mine users' web browsing behaviors. Contextual relationships of URL categories are learned by the hidden Markov model. The top‐level domains (TLDs) extracted from URLs themselves and the corresponding categories are caught by the TLD model. Given a URL to be predicted, its TLD and current context are empirically combined in an aggregation model. In addition to the uses of the current context, the predictions of the URL accessed previously in different contexts by various users are also considered by majority rule to improve the aggregation model. Large‐scale experiments show that the advanced aggregation approach achieves promising performance while maintaining an acceptably low false positive rate. Different strategies are introduced to integrate the model with the blacklist it generates for filtering objectionable web pages without analyzing their content. In practice, this is complementary to the existing content analysis from users' behavioral perspectives.

Handling of online information by users: evidence from TED talks

“…In that, Zhang et al (2014) find that content factors are more important than contextual factors, Fiksdal et al (2014) report information saturation and fatigue as main reasons for stopping information retrieval and Wook and Salim (2014) identify specification requirements for visual aspects of information provision as the use of space, organisation of information, and function and use of colour. Moving further, predictive models of web browsing behaviour based on its past records are viable as suggested by Lee et al (2015). Importance of the information provision style and consumers' perception is reported by Hsieh et al (2015) and Gao and Bai (2014), confirm like other studies above the cognitive processing of online information by visitors.…”

CLAD: A corpus-derived Chinese Lexical Association Database

“…Besides eliciting real-time responses, the use of text corpora may be the most convenient method of obtaining large quantities of linguistic and cognitive information. Corpus-based studies have for many years been used in language-related research, including data mining (Aggarwal & Zhai, 2012), pedagogical and specialized lexicography (Kilgarriff & Grefenstette, 2003), machine translation and learning (Liu, Hsaio, Lee, Chang, & Kuo, 2016; Rauf & Schwenk, 2011; Sung et al, 2015), artificial intelligence (Boden, 1998; McNamara, Crossley, & Roscoe, 2013), and numerous other examples (Chen, Liu, Chen, Wang, & Chen, 2016; Lee, Juan, Tseng, Chen, & Tseng, 2015). Because the linkages between words represent the relationships between the concepts embodied in human language (Li & Zhao, 2017), the numerous connections between words in a large corpus when distilled into an association database could be a great supplement to traditional association norms.…”

Exploratory Study on the Stability of Consumer Rationality in Judging Online Reviews

“…The specificity of such reactions points us to why many firms want to discover microtrends in order to target particular consumer groups (Penn & Zalesne, 2007). This is one more justification for why firms study the detailed browsing behaviors of consumers (L.-H. Lee, Juan, Tseng, Chen, & Tseng, 2014).…”