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This study describes how search engines (SE) can be employed for automated, efficient data gathering for Webometric studies using well defined query specfic URLs in SE (predictable URLs). It then compares the usage of staff-related Web Impact Factors (WIFs) to web impact factors for a ranking of Australian universities, showing that rankings based on staff-related WIFs correlate much better with an established ranking from the Melbourne Institute than commonly used WIFs. In fact WIFs do notdoi:10.1080/09737766.2008.10700854 fatcat:hcprpx3bcnezlfcedp2jxbvepi