• Corpus ID: 8694035

FREDERIC KAPLAN Linguistic Capitalism and Algorithmic Mediation

@inproceedings{Kaplan2014FREDERICKL,
  title={FREDERIC KAPLAN Linguistic Capitalism and Algorithmic Mediation},
  author={F. Kaplan},
  year={2014}
}

• F. Kaplan
• Published 2014
• Computer Science

Google’s highly successful business model is based on selling words that appear in search queries. Organizing several million of auctions per minute, the company has created the first global linguistic market and demonstrated that linguistic capitalism is a lucrative business domain, one in which billions of dollars can be realized per year. Google’s services need to be interpreted form this perspective. This article argues that linguistic capitalism implies not an economy of attention but an economy of expression. As several million users worldwide daily express themselves through one of Google’s interfaces, the texts they produce are systematically mediated by algorithms. In this new context, natural languages could progressively evolve to seamlessly integrate the linguistic biases of algorithms and the economical constraints of the global linguistic economy. Representations 127. Summer 2014 © The Regents of the University of California. ISSN 0734-6018, electronic ISSN 1533-855X, pages 57–63. All rights reserved. Direct requests for permission to photocopy or reproduce article content to the University of California Press at http://www.ucpressjournals.com/reprintinfo.asp. DOI: 10.1525/rep.2014.127.4.57. 57 engines and, by the beginning of the year 2000, Google was the most popular portal for accessing information on the World Wide Web. This algorithm relies heavily on the blind mechanisms of so-called collective intelligence. It functions well if document creators ignore the existence of the ranking and if actors do not deliberately try to create content to enhance their scoring artificially. As expected, in the last ten years, many algorithms have been developed to deceive Google’s ranking criteria. Such algorithms optimize textual and intertextual content to push content further in the search results. Google has kept updating its methods for detecting those algorithmically produced fake contents, but text-producing algorithms have continued to improve their ability to outwit Google’s countermeasures. This is how the first ‘‘linguistic war’’ on the Internet, characterized by the first massive production of algorithmic texts on the Word Wide Web, started. I will discuss some of its multiple implications later in this article. In March 2000, the ‘‘Internet bubble’’ collapsed, and many ‘‘start-ups’’ offering good use value but no exchange value went bankrupt. Most of their business models were based on selling advertising space on high-traffic web pages, hoping that the popularity of their services would motivate high prices. Again, Google’s founders had a clever intuition. They realized that they were accumulating a form of linguistic capital as the number of Google’s users continued to grow, and to enter ever-larger numbers of search queries. Google managed to transform this linguistic capital into actual money by organizing an algorithmic auction model for selling keywords. The principle of this system is well known. Every time a user views search results on Google, several sponsored links with a short text are presented. Advertisers pay only if Google displays their ad and users click on the link. In order to choose what ad to display, an algorithm organizes a bidding process in three steps. First, advertisers select a keyword—for instance ‘‘vacation’’—and define the maximum price they would be ready to pay if a user arrives on their site by clicking on the link of the ad. To help advertisers, Google gives an estimate of the amount one needs to offer to have a reasonable chance of being among the selected ads. However, a high bid does not automatically guarantee selection. Second, Google associates a quality score with the ad. This figure, ranging from 1 to 10, evaluates the global ‘‘quality’’ of the ad, which is computed through a complex combination of various factors, including the relevance of the text ad regarding the keyword, the average number of clicks on the ad, and the performance and quality of the linked website. This score measures how well the ad is working (remember that Google is only making money if users actually click on the advertiser link). The exact computation method is kept secret and can be changed at any time by Google.