A Data Science Central Community
Search engines results are notoriously biased, creating unfair competition for many small publishers. For instance, they routinely attribute an article not to the source, but instead to another website who just copied the original article. We don't know if this is done on purpose (if the search engine has a privileged relationship with the plagiarist) or because of faulty relevancy algorithms. We think it is a combination of both, the main cause being faulty algorithms and sloppy application of data science principles. Partly because these search engines are being gamed by undetected web spammers who artificially boost their results with sophisticated SEO.
We want to test if you can outsmart these search engine algorithms, and eliminate these biases. If successful, this will become a long term project requiring very little maintenance and regular, monthly revenue. Essentially, your task will consist in developing or exploiting a Botnet (real or simulated, open or not) that generates
Only organic search results are considered here: there will be no manufactured clicks on paid links.
In short, we are looking for someone (e.g. paid intern initially) who can develop a tool to game search engines algorithms, to eliminate the bad consequences of their bugs. We will provide state-of-the-art statistical, search engine, keyword and data science knowledge to help you avoid detection. But you must be an expert in Botnet architecture and their life cycle. The work can be performed remotely anywhere on Earth, payment will be made via Paypal. You can use any technology, not necessarily a Botnet, as long as it achieves the desired goal: to beat the search engine keyword relevancy algorithms, to indirectly eliminate copyright violations and plagiarism from search results, on behalf of our clients (the victims).
For more about what we want to accomplish, read the second item the article Google search: three bugs to fix with better data science.