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The purpose of this article was to confirm whether users’ interests are reflected by tweeted Web pages, and to evaluate the credibility of interest reflection of tweeted Web pages.

Interest reflection of Twitter is investigated based on the context of sharing behavior. A context-oriented approach is proposed to evaluate the interest reflection of tweeted Web pages based on machine learning. Some different distribution models of similarity are present, and infer whether tweeted Web pages reflect respective users’ interests by analyzing user access profiles.

The analysis of browsing behaviors finds that many users partially hide their own concerns, hobbies and interests, and emphasize the concerns about social phenomenon. The extensive experimental results showed the context-oriented approach is effective on real net view data.

As the first-of-its-kind study on evaluating the credibility of interest reflection on Twitter, extensive experiments have been conducted on the data sets containing real net view data. For higher accuracy and less subjectivity, various features are generated from user’s Web view and Twitter submission background with some different context factors.