Search query: report spam

Product: Arctic Silver A Little Spam Will Do YA: A case study on astroturfing,spam,fake on line reviews in the gaming PC after market
Relevance: Substitute
This is a compilation of articles as published on the Edstech reports blog and CNNireport

In the Spirit of Academia we picked a product called Arctic Silver 5 whom is one of the most egregious Astroturfers as they have used just about every single Black Hat technique known to the world of SEO to buck the system.

The book here is a compilation of articles published in CNN online and the eds tech report blogs and is a case study of internet web spam and astroturfing

It is in this spirit of investigation and academia that we purposely focused on a single product and manufacturer in order to fully reveal and expose the full depth and breadth of this ubiquitous yet clandestine modern phenomena called Astroturfing and Spam and are delineated in the contents of this book

This work has been created to be a manual to educate the novice SEO or the consumer in regard to the endemic practice of Astroturfing, Backlinking , the various types of Spam and how they all connect to each other

This work came to being as a direct result stemming from an 18 month long research investigation into the dark side of online marketing and propaganda known as Astroturf, Spam and Black Hat SEO and published online in the Ed’s Tech Report and Ed’s Tech Rebort2 blogs

Biased tested Internet searches were conducted between the dates of approximately May 1, 2015 and June 1, 2016 and the results were observed and documented with screenshots of results in this time frame.

Product: Opinion Analysis For Online Reviews (East China Normal University Scientific Reports Book 4)
Relevance: Irrelevant
This book provides a comprehensive introduction on opinion analysis for online reviews. It offers the newest research on opinion mining, including theories, algorithms and datasets. A new feature presentation method is highlighted for sentiment classification. Then, a three-phase framework for sentiment classification is proposed, where a set of sentiment classifiers are selected automatically to make predictions. Such predictions are integrated via ensemble learning. Finally, to solve the problem of combination explosion encountered, a greedy algorithm is devised to select the base classifiers.