Читать книгу The Digital Economy - Tim Jordan - Страница 13

To examine a specific practice, it needs to be acknowledged that practices come from points of view. A practice is always with or from someone or something. Points of view come with intentions and meanings, with authors, actants and actors aiming to do certain things, even if sometimes they achieve only related or different things. Points of view are necessary entanglements with other points of view, not all of which are visible to each other. The individual who searches does not necessarily see the algorithm forming the answer, nor does the algorithm necessarily account for the computers it requires, which themselves alter environments through their hunger for electricity, rare metals and so on. The points of view can never all be collated; the ‘god’s eye view’ misleads with a false promise of totality (Haraway 1991: 189–98). Yet, points of view can anchor an analysis of a web of intersections though a focus on a certain perspective that brings into view economically significant practices.

Practices are materialised in repeated actions, ideas and relations, each practice making a particular context material. When a practice is performed, one materiality is realised leaving behind other possibilities. While the previous discussion established the importance of habits amid differing activities for understanding practices generally, moving to a specific set of practices (here those of searching) requires taking into account points of view and understanding the materiality and the moment of activities. When a practice is performed, for example when a search is made, a reality is cut – in Barad’s sense – out of the mess of entanglements that intersect in each specific moment and place. And in each cut where a materiality is made there will be multiple points of view, different ways of performing in the complexity of an overall practice (Barad 2007: 148–70). One matter realised once is still matter, but it only transforms into a socially, culturally, politically or economically significant matter when the cut is repeated across a range of points of view and the same matter is realised. To understand Google’s economic practices, then, the points of view from which such repeated practices create and rely on materialities need to be followed. Three such points of view will be examined: those of the searcher or user; of the advertiser; and of Google itself, particularly in relation to its algorithms and datasets and the demands on these created by its commitment to a for-profit corporate logic.

A caveat to this analysis is that Google search has changed and continues to change, becoming more complex over its development. This case study will simplify somewhat by focusing on Google relatively early in its advertising days, at the point broadly speaking when its two key ad programs – Adwords and Adsense – and personalisation through data were established.

In 2016 around 2 trillion searches were conducted using Google’s search engine. To explore user practices, I will follow one user finding an answer (Sullivan 2016). Imagine you are writing a book about the digital economy based on following economic practices in specific digital contexts, rather like this book. As part of this project, you wish to outline the various practices related to searching for information using Google, and, as you write that, you realise it might be helpful to locate those practices in a broader context, perhaps by establishing how many Google searches are made. Practices then follow.

First, your practices have a material context. This will include working on a desktop computer from home, rather than in the office your employer provides, or on a phone while moving around, or on a tablet while commuting to work on a train. There are a range of taken-for-granted practices here: using a mouse and the Microsoft operating system, using alt-tab to switch to an already open browser (Firefox) and knowing that typing a string of words – ‘Google search enquiries 2017 total number’ – into what looks like the address box of the browser will invoke the Google search engine. Underpinning even these unthought and semi-automatic practices are a range of things like electricity, broadband access, light and so on, which create a material context in which our user can sit and quickly bash out an enquiry, hitting return to initiate the search.

Taking most of this material context for granted, our user is now looking at a computer screen on which a window organised by the Firefox browser has opened up. The window is very familiar. At the top are buttons, an address window, and various customisations (Zotero for referencing, for example), which control the actions that can be taken in the browser. Just below this ribbon of buttons is the Google logo, the search box in which the words used in the search reappear, reminding the user what the initial action was. Below that is a list of responses to the query, with a blank space to the side of these answers, though the user knows that sometimes advertisements or summaries will appear there. The list of entries is each an address (a URL) linking to another site on the World Wide Web, with the first two to three lines of each site being shown. From this our user realises that their search words were not the best, as the second entry is for the UK’s Her Majesty’s Land Registry, which records land ownership in the UK and has nothing to do with the number of Google search queries. Scrolling over the entry box for a new search brings up a suggestion – ‘how many Google searches per day 2017’ – which our user clicks on to generate a second set of results. The result that was first on the first search is now second, but our user clicks on that result as it has now shown up twice, and in their experience (that is, in the practices they have developed to search for information using Google), this is a reasonable indication that the answer they want may be found there. Disappointingly, the article linked to is from 2016 but it is from a search engine analysis site and seems reasonably sourced, so our user lifts the 2 trillion a year figure and adds it to their text.

From here our user may move in several directions, perhaps diving more deeply by looking at the top result in the second search, which purports to record live how many Google searches are being made (www.internetlivestats.com/google-search-statistics). Or they may restrain the impulse to dive deeper into the topic and return to the writing at hand. The practice of searching is closely connected to other practices that make up this working life. Our user has one last reflection as they notice that accompanying the second site they looked at there are advertisements for paintings by indigenous Australian artists, and they remember that similar ads have been following them as they visited different websites at other times. These paintings indicate the practices of Google advertising, which we can turn to next.

Many readers will have guessed immediately what happened in relation to the searcher finding ads for indigenous Australians’ art on various websites that have nothing to do with such art, because ads that follow a searcher have become a common experience. The user must at some point have looked at or searched for such art, and tracking mechanisms on the internet have recorded this and used it to target ads. Similarly, some years ago, I booked a trip to Walt Disney World online, which led to Mickey Mouse and his friends stalking me across the Web amid the often noted, and ongoing, irony of being shown ads to go somewhere I could no longer afford to go because I had just paid to go there.

In following the practices of a user engaged in materialising an answer to a question, a second set of practices that can be followed has emerged with advertising, which implies an advertiser. For an advertiser, economic practices, at their crudest, involve trying to persuade people to buy the goods the advertiser has been paid to get them to buy (though it should be noted that advertising strategies may be complex, such as building brand loyalty or gaining attention). The question is, how does an advertiser end up in profit by being paid to boost a company’s sales? Let us assume this advertiser decides to work with online advertising and goes to Google. The magnet Google has to attract potential buyers is its search engine. In the period of Google’s development focused on here, two broad routes are offered. One is that when someone searches for a term related to a product being advertised, then the websites our advertiser has designated show up in the advertising sections of the Google page on which the search query is returned. The second is that Google facilitates ads appearing on other companies’ websites and, again, our advertiser can pay for their products and related sites to show up on sites other than Google’s.

To appear on the search page is to directly draw on the magnet of Google search. Ads appearing here are marked out in slightly different colours and with words indicating that they are ads – ‘sponsored’ often appears – ensuring that they remain distinct from the search results. If our advertiser works for a business that sells package holidays, they may want their site to be advertised to anyone searching for terms like ‘Disney’, ‘beach’ and so on. To do this they have to decide what kinds of words a user might type into search that would indicate they might be interested in holiday products. Google runs an auction on keywords and advertisers bid according to how much they are willing to pay each time someone clicks on the advertisement. The way Google’s auction is set up guarantees that the winner only pays just above whatever the second highest bid was. Once the auction is won, the ads our advertiser wishes to be seen will appear on Google’s search page when the words are used in a search. If a user then clicks on the ad, the advertiser pays Google. There are complications to this simple scenario – such as Google rating good or bad ads, the standards ads must meet, and the information Google offers advertisers to improve their ads and so on – but the fundamentals are in this practice of buying words at auctions which result in ads being served to users of Google search (Turrow 2011: 67; Levy 2011: 87–93).

The second process – in which ads appear on websites other than Google’s search page – runs in a similar way to the first. For example, a fan of model railroads might run a website and seek to gain some funding by signing up with Google to host ads. Our advertiser might then think that people who are interested in model railroads might also be interested in holidays featuring rail travel (and when I say ‘think’ that probably means research into the demographics and holiday habits of model railroaders). Having come to this conclusion, the advertiser bids for the relevant words and then pays in various ways. One key form of payment is what is called ‘cost per mille’, or what an advertiser will pay for 1,000 views of their ad on various sites. Google is paid by the advertiser and then pays the hosting website a percentage based on the ads served up and viewed. Again, there are many complications here and there have been developments over time, but the core practices of advertisers should by now be clear.

The third set of intersecting activities, or third point of view, making up the economic practices of Google search are those of Google itself. These activities split between the structures set up by the company that allow it to offer services and mediate between search users and advertisers, and the implementation of those structures in the software/hardware that allows practices to be automated. This connection transformed Google and established one kind of digital economic practice as a money gusher, as noted earlier in the company’s turn from loss to profit once Adwords was implemented. To sustain this, Google’s economic practices have a dual character, with a never-ending process of improving search alongside never-ending developments in advertising.

We have followed a single search from the point of view of the individual searcher, but from Google’s point of view things appear differently. Instead of the individual who searches, Google has to first see the collective and its social relations, which it can read to judge what search results to deliver. From this point of view, a search question is the last point of a search enquiry; it is what leads up to the delivery of certain results in a certain order that determines whether a search engine will be good or bad. This also highlights a recurrent frustration in trying to follow digital economic practices, as the algorithms and programs that fuel search engines are generally industry (or government) secrets. In the case of Google, however, the broad principles are known because its theoretical foundation, the PageRank algorithm, is publicly available (Page et al. 1999).

PageRank was the first method Google used to generate search results and was the basis of its early success, on which everything else depended. The fundamental insight was that the World Wide Web could be read through techniques modelled on academic citation practices. Citations are a means of judging how important an article is by measuring how many people cite that article in later papers; it is in this sense a ‘backlink’ because the links, here in the form of citations, appear after the article is published. To read the World Wide Web in this way, Google’s founders Larry Page and Sergey Brin developed a model that treats the links from one website to another as a backlink similar to an academic citation and then judges the importance of a site in relation to a particular subject by the number of backlinks. Further, they created a recursion through which, having worked out what sites were important on a particular topic (by reading the numbers of links to that site), they could weight those sites more heavily. This meant that their model generated complexity, as many links from unimportant sites might be balanced by a site having only a few links if those few links came from important sites (Page et al. 1999).

To fully grasp the significance of this use of the World Wide Web we need to remember that what Google were (and are) reading through PageRank is a collectively created store of information to which anyone with access to the internet can add on topics of their choosing, including linking as website creators feel is appropriate. The WWW is created by following a set of formal standards that define how you have to form information and load it on a networked computer for it to be visible to other sites (as will be discussed further in Chapter 5). Once a website is visible other sites can link to that site just as anyone can link to their sites. The standards were released to be freely available and are maintained by a not-for-profit consortium. Much of the content that was created was done so freely by ordinary users with internet access and computing resources, though over time corporate and government sites run by paid employees have played a greater role. The WWW is then a collective creation formed of a series of groups that link to each other because they choose to do so in order to ensure that relevant information is connected and available. Although it was heavily commercialised once it became popular, the WWW preceded the birth of Google, and remains a space in which groups of people with similar interests can generate and share information resources (Berners-Lee 2000; Gillies and Cailliau 2000).

PageRank was a means of reading these linked groups and their social relations. Once PageRank had read, for example, sites devoted to surfing it had evidence of the most important sites based on those who loved surfing and had created sites on the subject, including what those people thought were the most important sites and topics. This was the key work done in the initial Google search engine which can be drawn on when someone makes a surf-related search query. In this sense, any search query comes last in the practices of answering it, after the work has been done to read the relevant topics represented on the WWW.

The PageRank algorithm did not, however, last long in its original form. As Google gained a reputation as a good search engine and traffic to it began to increase, it became possible to raise a site up the search rankings by adding fake links to it. Large farms of sites which did nothing but try to game Google’s rankings by faking links appeared in the first rounds of the then emerging and now never-ending struggle between Google’s attempts to deliver the search results it deems best and the attempts of individual sites to ensure they are returned as high as possible in the results. As one information expert in search put it: ‘there’s definitely a kind of, ah, a kind of a war going on between the search engine and the marketers, marketers are pressuring the search engines to be more crafty, more authentic in how they rank’ (cited in Mager 2012: 777). Google then has to commit considerable labour to constantly monitoring and then upgrading its search mechanisms, which then feeds through to changes in advertising. This leads to the second set of practices necessary to understand Google search, which involves the elaboration of the original algorithm with more algorithms (Hillis et al. 2012).

One of the best-known early additions to PageRank was the Random Surfer Model, which injected, as its name implies, randomness by assuming that at certain points anyone following web links would randomly jump to some other link. Further improvements were made, some in response to attempts to game the system and others to improve search results. For example, the Hilltop algorithm aims to divide the Web up into thematic sections and then judge if a site has links to it from experts who are not connected to that site. If many independent experts link to the site, then it is deemed an authority in its thematic area and can be used to judge the importance of other sites. Hilltop thus builds on citation practices while developing them in a specific direction. This algorithm was initially developed independently of Google and was bought by them to be integrated into its own set of tools. There are no doubt many other adjustments and wholly new algorithms integrated into PageRank and because of trade secrecy there will be more than we know about. But these examples are enough to establish the basic principle that, however it is implemented, Google’s successful search – successful both in terms of delivering useful results and in terms of popularity – derives from reading the creations of the pre-existing community of the World Wide Web (Turrow 2011: 64–8; Vaidhyanathan 2012: 60–4; Hillis et al. 2012).

The second key area of search development was opened up by Google only after the first algorithms for reading the WWW proved successful. This second area was that of personalisation, which only became possible once Google became big enough to start collecting significant datasets on those using its search engine. Exploring these datasets enabled the targeting of search results, with different users receiving different search results. This is particularly the case if the searcher uses other Google services, such as Gmail, and has a Google account. Personalisation appears to many to be the process whereby Google judges whether a searcher who uses a term like ‘surf’ is interested in surfing on water, musical channels, or the Web and so on. It also seems to identify users individually, each having a certain age, location, gender, race and so on, bringing users the results that are judged appropriate to their demographics. However, reading personalisation in this way is to read it from the point of view of the user’s practices rather than Google’s. For the latter, the key is not so much each individual but the correlations between many individuals; it is the inter-relations that are key to producing a useful result for an individual, not the other way around. This is because the inference has to be constantly made that if many individuals of a certain type favour a particular search result then this can be delivered to individuals who fit that type. It is these kinds of mass correlations that allow for the targeting of particular groups of people – assuming, for example, that men of a particular age group might prefer the Burt Reynolds version of the film The Longest Yard, whereas those from a younger age group might be looking for the Adam Sandler remake with the same name, and those of a different nationality may be interested in the Vinnie Jones-led soccer version called The Mean Machine (Feuz et al. 2011; Hillis et al. 2012).

Personalisation achieved by building correlations between categories, or profiling as it is sometimes known, is a second way to mine social relations to create Google search (Elmer 2004). The results delivered to individuals are partially based on correlations which are meant to mathematically capture what social and cultural life means. This is not a totalising analysis which posits one set of internally consistent social dynamics, but a tracing or mapping of whatever social relations can be found from analysing the data Google collects. In this way, Google’s practices of delivering search results and generating data on which ads can be based include various ways algorithms can read the relations between people.

Starting with the social relations that can be read from the structure of the WWW, Google search then progresses through various means of manipulating and extending that reading. Once enough data has been collected, it can progress to reading the kinds of correlations that measure social relations, which may then be used to personalise search results. Google search practices intertwine different kinds of people, algorithms, datasets and constant updating and storing processes to deliver an answer to a question. These algorithmic logics, that are interweaving different kinds of actors in people, software, data, hardware and so on, must continue to deliver a successful search engine, but they must also conform to the corporate logics Google has embraced as a for-profit company.

For example, one of Google’s initial problems in profiting financially from its search engine was how to generate trust in its very different way of selling advertisements (Auletta 2011: 3–6). As already mentioned, Google runs automated auctions to allocate search words. From the advertiser’s point of view, this is about connecting their ad with the best search query or term on the best site, while for Google it is about balancing income with advertiser trust and ease of use. These interests are resolved in the specific auction practice in which the winner does not pay what they bid but only a small amount more than the second-placed bid. While this is usually less than Google might have earned, the process has the advantage of building long-term trust with advertisers. At the same time, the process removes the advertiser’s interest in bidding lower than they might otherwise due to worries about over-bidding (Levy 2011: 89–91). Here is a specific kind of practice, again automated through algorithms and networks, that mediates the search result into an advertising program that generates revenue for Google and, possibly, for the advertiser. But search had to come first: the value of Google had to be established by the practices of creating a functioning, free and attractive search engine, so that practices could then be generated connecting the value of search to the value of money.

The corporate logics of revenue and profit have to be implemented after the practices of search as value, but these logics also find that the practices of creating search can themselves be translated into and reused as practices of revenue and profit. In particular, the processes of personalisation can develop closely with those of profits derived from advertising, because in both the issue is one of using datasets to generate ways of grouping users together. The same clues that allow personalisation can be moulded to deliver targeted advertising. These Google practices consist of translating both advertiser trust and dollars through the prism of profiling users, similar to personalisation. This is not to say that personalisation and targeted advertising are exactly the same processes, only that they draw on the same idea that correlations and profiling can be generated from Google’s vast datasets of its users’ behaviour (Hillis et al. 2012). In this way, Google’s algorithmic and corporate logics intertwine.

Essential to Google’s practices is a set of algorithms that distributes ads, according to words won at auction, onto its own or other websites. It should be particularly noted that Google has access to recursions within its data, recursion being the process of creating infinite information by returning the results of an information process to itself: when the output serves as input to create a different output then information may increase exponentially and infinitely (Jordan 2015: 29–44). Here we catch sight of a corporate reason for the mass information storage and processing, because a core recursion involves using the information collected to refine searching and so to refine the delivery of advertisements. In this sense, Google’s advertising practices are essentially a recursion of some of their search practices, particularly personalisation, but with the information being delivered in the form of advertisements rather than as answers to search queries. While we know this, the nature and specificity of these practices are obfuscated as a trade secret, leading to the ‘Search Engine Optimisation’ industry, which seeks to find ways to improve clients’ ranking in search engine results by analysing and manipulating their secret algorithms (Havalais 2009).

The obfuscation of algorithms and networks will be a repeated issue when analysing digital economic practices, but it should not be over-emphasised. As demonstrated above, while we cannot follow the details, we can follow the nature of the practices that create search, partly because we know what users, in this case searchers or advertisers, are doing, and because we know the nature of what the company is doing. Closer work would have value, but in defining digital economic practices generally, or Google’s practices specifically, the level of detail that is available is more than adequate.

With this analysis of Google’s economic practices, we have thoroughly examined a leading example of digital economic practice in which the searcher, the advertiser, the advertising site and Google itself each have different practices that intersect to create the overall practice. At its core, we see that while the money comes from advertising, this revenue is dependent on prior search processes. Advertising is then second both temporally – the search engine has to first be established to attract users, whom the company can then use to attract advertisers – and existentially, in the sense that without a successful search engine advertising is irrelevant since no company will survive for very long. Once Google had learned how to read the community of the WWW and ally this to the data flow derived from its searchers, it could then make the profitable leap to advertising, while still offering its core ‘value’ of search as a free service. At Google, the addition of monetisation is materialised in new practices of analysing users, as well as in auctions, money transfers and bookkeeping. Fundamentally, Google’s digital economic practice is based on its ability to read a community and then pass that reading through recursions that both identify better search results and deliver targeted advertising.