{'fraction_non_alphanumeric': 0.02250351617440225, 'fraction_numerical': 0.009142053445850914, 'mean_word_length': 5.352678571428571, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Trustworthy Website Detection Based on Social Hyperlink Network Analysis

Trustworthy website detection plays an important role in providing users with meaningful web pages, from a search engine. Current solutions to this problem, however, mainly focus on detecting spam websites, instead of promoting more trustworthy ones. In this paper, we propose the enhanced OpinionWalk (EOW) algorithm to compute the trustworthiness of all websites and identify trustworthy websites with higher trust values. The proposed EOW algorithm treats the hyperlink structure of websites as a social network and applies social trust analysis to calculate the trustworthiness of individual websites. To mingle social trust analysis and trustworthy website detection, we model the trustworthiness of a website based on the quantity and quality of websites it points to. We further design a mechanism in EOW to record which websites’ trustworthiness need to be updated while the algorithm “walks” through the network. As a result, the execution of EOW is reduced by 27.1 percent, compared to the OpinionWalk algorithm. Using the public dataset, WEBSPAM-UK2006, we validate the EOW algorithm and analyze the impacts of seed selection, size of seed set, maximum searching depth and starting nodes, on the algorithm. Experimental results indicate that EOW algorithm identifies 5.35 to 16.5 percent more trustworthy websites, compared to TrustRank.