Bad news: analysis of the quality of information on influenza prevention returned by Google in English and Italian

Information available to the public influences the approach of the population toward vaccination against influenza compared with other preventative approaches. In this study, we have analyzed the first 200 websites returned by searching Google on two topics (prevention of influenza and influenza vaccine), in English and Italian. For all the four searches above, websites were classified according to their typology (government, commercial, professional, portals, etc.) and for their trustworthiness as defined by the Journal of the American Medical Association (JAMA) score, which assesses whether they provide some basic elements of information quality (IQ): authorship, currency, disclosure, and references. The type of information described was also assessed to add another dimension of IQ. Websites on influenza prevention were classified according to the type of preventative approach mentioned (vaccine, lifestyle, hygiene, complementary medicine, etc.), whether the approaches were in agreement with evidence-based medicine (EBM) or not. Websites on influenza vaccination were classified as pro- or anti-vaccine, or neutral. The great majority of websites described EBM approaches to influenza prevention and had a pro-vaccine orientation. Government websites mainly pointed at EBM preventative approaches and had a pro-vaccine orientation, while there was a higher proportion of commercial websites among those which promote non-EBM approaches. Although the JAMA score was lower in commercial websites, it did not correlate with the preventative approaches suggested or the orientation toward vaccines. For each of the four search engine result pages (SERP), only one website displayed the health-of-the-net (HON) seal. In the SERP on vaccines, journalistic websites were the most abundant category and ranked higher than average in both languages. Analysis using natural language processing showed that journalistic websites were mostly reporting news about two specific topics (different in the two languages). While the ranking by Google favors EBM approaches and, in English, does not promote commercial websites, in both languages it gives a great advantage to news. Thus, the type of news published during the influenza season probably has a key importance in orienting the public opinion due to its high visibility. This raises important questions on the relationships between health IQ, trustworthiness, and newsworthiness

Enhanced Trustworthy and High-Quality Information Retrieval System for Web Search Engines

The WWW is the most important source of information. But, there is no guarantee for information correctness and lots of conflicting information is retrieved by the search engines and the quality of provided information also varies from low quality to high quality. We provide enhanced trustworthiness in both specific (entity) and broad (content) queries in web searching. The filtering of trustworthiness is based on 5 factors – Provenance, Authority, Age, Popularity, and Related Links. The trustworthiness is calculated based on these 5 factors and it is stored thereby increasing the performance in retrieving trustworthy websites. The calculated trustworthiness is stored only for static websites. Quality is provided based on policies selected by the user. Quality based ranking of retrieved trusted information is provided using WIQA (Web Information Quality Assessment) Framework

The relationship of (perceived) epistemic cognition to interaction with resources on the internet

Information seeking and processing are key literacy practices. However, they are activities that students, across a range of ages, struggle with. These information seeking processes can be viewed through the lens of epistemic cognition: beliefs regarding the source, justification, complexity, and certainty of knowledge. In the research reported in this article we build on established research in this area, which has typically used self-report psychometric and behavior data, and information seeking tasks involving closed-document sets. We take a novel approach in applying established self-report measures to a large-scale, naturalistic, study environment, pointing to the potential of analysis of dialogue, web-navigation – including sites visited – and other trace data, to support more traditional self-report mechanisms. Our analysis suggests that prior work demonstrating relationships between self-report indicators is not paralleled in investigation of the hypothesized relationships between self-report and trace-indicators. However, there are clear epistemic features of this trace data. The article thus demonstrates the potential of behavioral learning analytic data in understanding how epistemic cognition is brought to bear in rich information seeking and processing tasks

Users' trust in information resources in the Web environment: a status report

This study has three aims; to provide an overview of the ways in which trust is either assessed or asserted in relation to the use and provision of resources in the Web environment for research and learning; to assess what solutions might be worth further investigation and whether establishing ways to assert trust in academic information resources could assist the development of information literacy; to help increase understanding of how perceptions of trust influence the behaviour of information users

Adding dimensions to the analysis of the quality of health information of websites returned by Google. Cluster analysis identifies patterns of websites according to their classification and the type of intervention described.

Background and aims: Most of the instruments used to assess the quality of health information on the Web (e.g. the JAMA criteria) only analyze one dimension of information quality, trustworthiness. We try to compare these characteristics with the type of treatments the website describe, whether evidence-based medicine or note, and correlate this with the established criteria. Methods: We searched Google for “migraine cure” and analyzed the first 200 websites for: 1) JAMA criteria (authorship, attribution, disclosure, currency); 2) class of websites (commercial, health portals, professional, patient groups, no-profit); and 3) type of intervention described (approved drugs, alternative medicine, food, procedures, lifestyle, drugs still at the research stage). We used hierarchical cluster analysis to assess associations between classes of websites and types of intervention described. Subgroup analysis on the first 10 websites returned was performed. Results: Google returned health portals (44%), followed by commercial websites (31%) and journalism websites (11%). The type of intervention mentioned most often was alternative medicine (55%), followed by procedures (49%), lifestyle (42%), food (41%) and approved drugs (35%). Cluster analysis indicated that health portals are more likely to describe more than one type of treatment while commercial websites most often describe only one. The average JAMA score of commercial websites was significantly lower than for health portals or journalism websites, and this was mainly due to lack of information on the authors of the text and indication of the date the information was written. Looking at the first 10 websites from Google, commercial websites are under-represented and approved drugs over-represented. Conclusions: This approach allows the appraisal of the quality of health-related information on the Internet focusing on the type of therapies/prevention methods that are shown to the patient

Effects of reputation and aesthetics on the credibility of search engine results

Search engines are the primary gatekeepers of online information, but are judged differently than traditional gatekeepers due to the interactive and impersonal nature of the online search process. The researcher distributed an online survey with 141 respondents and conducted 22 observational interviews. Information credibility was tested through measures of expertise, goodwill, and trustworthiness, which were each correlated with perceived reputation and perceived aesthetics. Search engine reputation was found to have moderate correlations with expertise and trustworthiness, and a lesser, but still moderate correlation with goodwill. Aesthetics was related to the credibility measures in similar but lesser proportions. Interviews indicated search habits such as wariness towards commercial interests and the high impact of search intent on the rigor of credibility judgments

HealthTrust: Assessing the Trustworthiness of Healthcare Information on the Internet

As well recognized, healthcare information is growing exponentially and is made more available to public. Frequent users such as medical professionals and patients are highly dependent on the web sources to get the appropriate information promptly. However, the trustworthiness of the information on the web is always questionable due to the fast and augmentative properties of the Internet. Most search engines provide relevant pages to given keywords, but the results might contain some unreliable or biased information. Consequently, a significant challenge associated with the information explosion is to ensure effective use of information. One way to improve the search results is by accurately identifying more trustworthy data. Surprisingly, although trustworthiness of sources is essential for a great number of daily users, not much work has been done for healthcare information sources by far. In this dissertation, I am proposing a new system named HealthTrust, which automatically assesses the trustworthiness of healthcare information over the Internet. In the first phase, an unsupervised clustering using graph topology, on our collection of data is employed. The goal is to identify a relatively larger and reliable set of trusted websites as a seed set without much human efforts. After that, a new ranking algorithm for structure-based assessment is adopted. The basic hypothesis is that trustworthy pages are more likely to link to trustworthy pages. In this way, the original set of positive and negative seeds will propagate over the Web graph. With the credibility-based discriminators, the global scoring is biased towards trusted websites and away from untrusted websites. Next, in the second phase, the content consistency between general healthcare-related webpages and trusted sites is evaluated using information retrieval techniques to evaluate the content-semantics of the webpage with respect to the medical topics. In addition, graph modeling is employed to generate contents-based ranking for each page based on the sentences in the seed pages. Finally, in order to integrate the two components, an iterative approach that integrates the credibility assessments from structure-based and content-based methods to give a final verdict - a HealthTrust score for each webpage is exploited. I demonstrated the first attempt to integrate structure-based and content-based approaches to automatically evaluate the credibility of online healthcare information through HealthTrust and make fundamental contributions to both information retrieval and healthcare informatics communities

EXTRACTING ACCURATE DATA FROM MULTIPLE CONFLICTING INFORMATION ON WEB SOURCES

For The World-Wide Web has become the most important information source for most of us. As different websites often provide conflicting information there is no guarantee for the correctness of the data. Among multiple conflict results, can we automatically identify which one is likely the true fact?, In this paper our experiments show that Fact finder, a supporter for user to resolve the problem, successfully finds true facts among conflicting information, and identifies Trust worthy websites better than the popular search engines. In our paper we give ratings based on two things- popularity or the hits & number of occurrences of same data. As we can’t give preference only to popularity, we have considered another rating i.e. about number of occurrences of same data in several other websites, which are less popular. This paper helps user to get resolved by conflicting facts from multiple websites on two basis. Further by considering few more relations we can develop a search engine that truly helps the user to resolve the Veracity problem