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Presence of information from multiple sources on the internet requires evaluating the credibility of the information, before its utilization. Researchers have suggested that internet users experience difficulty in accessing necessary information and do not pay enough attention to its credibility. We present here the design and implementation of an automated Web Credibility Assessment Support Tool (WebCAST) that considers multiple factors (type of website, popularity, sentiment, date of last update, reputation and review based on users' ratings reflecting personal experience) for assessing the credibility of information and returns a summary indication of the credibility of a website. We use Potentially All Pairwise RanKings of all possible Alternatives (PAPRIKA) method of Multi-Criteria Decision Analysis (MCDA) to give weights to the scale values on each factor, representing the relative importance of the attributes. An empirical evaluation of the tool was conducted by computing the correlation between the tool-generated credibility scores and that of human judges. The correlation was found to be 0.89, thus verifying the validity of the tool. In the future the proposed tool can be made useful to students in their learning process of credibility assessment.

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Navigation within a website is an important factor for the success of a website. Faster and easy web-navigation leads to better usability and reduces cognitive load on the user. Several cognitive models exist that simulate the web-navigation process. In this paper we propose a new cognitive model -CoLiDeS++Pic (based on Comprehension-based Linked model of Deliberate Search or CoLiDeS) that incorporates path adequacy and backtracking strategies. This model also takes into consideration the semantics of pictures. Firstly, we present here the results of an experiment in which we test the efficacy of support based on the new model CoLiDeS++Pic and multi-tasking under cognitively demanding situations. The results prove that the model-generated support is effective. Secondly, we also propose that in this way navigation behavior can be better modeled when compared to previous models. We verify this hypothesis by simulating the model on a mock-up website and comparing the results with a previous model CoLiDeS+. Extending our previous work we demonstrate that the performance of the new model CoLiDeS++Pic is improved compared to the preceding model CoLiDeS+. We further discuss the challenges and advantages of automating navigation support using the proposed model.

Easiness of navigation within a website is an important factor for information seeking performance. Several cognitive models exist that simulate the web-navigation process and these models in turn can be useful in supporting information seeking behavior. In this chapter we first discuss previous work we did on further developing a cognitive model of web-navigation CoLiDeS (Comprehension-based Linked model of Deliberate Search) that takes information from pictures into consideration, next to information from hyperlinks. This model is called CoLiDeS + Pic. Just like its parent model CoLiDeS, it uses Latent Semantic Analysis to compute semantic similarity in order to measure the information scent of hyperlinks available on a page. Next, we propose a new model CoLiDeS ++ Pic that adds path adequacy (with information from both hyperlinks and pictures) and applies backtracking. We hypothesize that in this way the information seeking process can be better modeled when compared to the previous model CoLiDeS + Pic. This was verified by simulating the model on a mockup website and comparing the results with the previous CoLiDeS + Pic model. The results support our hypothesis. We also present briefly the results of an experiment with tool-support based on the new model CoLiDeS ++ Pic. The results prove that model-generated support is fostering information seeking performance and helps in search tasks. We further discuss the challenges and advantages of automating navigation support using the proposed model.