Using past search engine algorithm updates to predict future trends

Farrell, Jason; Weideman, Melius

Published Online

Farrell, J. & Weideman, M. 2014. Using past search engine algorithm updates to predict future trends. Working Paper, Cape Peninsula University of Technology, Cape Town. Available online:

Search engine algorithm updates cause website owners to spend millions of dollars to adapt to changes necessitated by these changes. Being able to predict future trends provides a competitive edge. The key focus of this research project was to compare the most recent updates made to the Google search algorithm, and to analyse the potential impact future updates may have.
This research will provide an understanding of the latest updates and offer an explanatory overview of where future trends may lead. It was found that Google does not normally provide warnings about updates, which increases the importance of knowing which elements the updates focus on. So far, these have been mostly the; authorship, quality, structure and uniqueness of content.
  1. Beel, J., Gipp, B. & Wilde, E. 2010. Academic Search Engine Optimization (ASEO): Optimizing Scholarly Literature for Google Scholar and Co. Journal of Scholarly Publishing, 41(2): 176-190.
  2. Gabe, G. 2013. Penguin 2.0 Initial Findings – A Deeper Update, But Not Broader [Analysis]. Online: [18 October 2014].
  3. Gesenhues, A. 2013. Google's Hummingbird Takes Flight: SEOs Give Insight On Google's New Algorithm. Online: [16 March 2014].
  4. Katz, A. 2010. Aesthetics, usefulness and performance in user search – engine interaction. Journal of Applied Quantitative Methods, 5(3):424-445. Online: [9 September 2014].
  5. Sullivan, D. 2013. FAQ: All About The New Google "Hummingbird" Algorithm. Online: [16 March 2014].
  6. Weideman, M. 2013. Comparative analysis of homepage Website visibility and academic rankings for UK universities. Information Research,18(4) paper 599. Online: [1 July 2014].
  7. Zuze, H. & Weideman, M. 2013. Keyword stuffing and the big three search engines. Online Information Review, 37(2): 268-286.
  8. Tober, M. 2014. Panda Update 4.0: Winners and Losers – Google USA. Online: [15 July 2014].
  9. Weiche, A. 2014. Local Search. Online: [1 September 2014].

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