Public referencing engines have become an important element in research of publications. They collate a large proportion of online and print articles in their databases. One of the most popular free engines is Google Scholar.
One of the features of Google Scholar is that it allows users to add their academic library login and thus allow full access to articles that are made available through that library. This feature makes the search engine advantageous to an academic library one as it allows access to articles that the local library may not index. The fact that a user can include an academic library login may imply that the results are similar to what the academic library service would produce. However, the algorithm such public search engines use varies from local libraries and therefore produces different search results. Users of these databases need to be aware of different results otherwise they may miss articles that are more relevant for their research.
One study that compared Google Scholar results to PubMed found that “for quick clinical searches, Google Scholar returns twice as many relevant articles as PubMed and provides greater access to free full-texts” (Shariff et al., 2013). A separate paper found that “Google Scholar articles appear to have a higher number of citations and to come from higher impact journals when compared with PubMed articles.” (Nourbakhsh, Nugent, Wang, Cevik & Nugent, 2012) The reason for these differences is that Google Scholar algorithm has more emphasis on citations as well as how the article is optimised for its search engine.
Researchers need to be made aware that with the growing reliance on online search databases the concept of Academic Search Engine Optimisation is being utilised. “Academic search engine optimization (ASEO) is the creation, publication, and modification of scholarly literature in a way that makes it easier for academic search engines to both crawl it and index it.” (Beel, Gipp & Wilde, 2010) This method of optimising articles and publications may lead to some indexing higher than they otherwise would without optimising.
Following on with their original article Beel & Gipp (2010) found that “Google Scholar indexed invisible text in all kind of articles. A researcher could put invisible keywords in his article before, or even after, publication and increase the ranking and visibility of this article on Google Scholar.” This type of spamming needs to be monitored and therefore assessed when using public academic search engines. The results of public search engines must always be compared to academic databases to provide a better understanding of what articles are available on a particular topic.
To find the articles mentioned above I used the QUT Library search facility. Searching “google scholar comparison” under Google Scholar produced irrelevant articles as well out outdated ones. This confirms the need to always search multiple databases and never rely on one particular database no matter how popular or large it may be. Furthermore, cross checking may also help in weeding out articles that may have been manipulated to appear higher in results.
Beel, J., & Gipp, B. (2010). Academic Search Engine Spam and Google Scholar’s Resilience Against it. The Journal Of Electronic Publishing, 13(3). http://dx.doi.org/10.3998/3336451.0013.305
Beel, J., Gipp, B., & Wilde, E. (2010). Academic Search Engine Optimization (ASEO). Journal Of Scholarly Publishing, 41(2), 176-190. http://dx.doi.org/10.3138/jsp.41.2.176
Nourbakhsh, E., Nugent, R., Wang, H., Cevik, C., & Nugent, K. (2012). Medical literature searches: a comparison of PubMed and Google Scholar. Health Information & Libraries Journal, 29(3), 214-222. http://dx.doi.org/10.1111/j.1471-1842.2012.00992.x
Shariff, S., Bejaimal, S., Sontrop, J., Iansavichus, A., Haynes, R., Weir, M., & Garg, A. (2013). Retrieving Clinical Evidence: A Comparison of PubMed and Google Scholar for Quick Clinical Searches. Journal Of Medical Internet Research, 15(8), e164. http://dx.doi.org/10.2196/jmir.2624