big data challenge for social sciences · 2017. 11. 28. · big data dealing with the social...

Big Data dealing with the social produce predictivecorrelations for the benefit of brands and web plat-forms. Beyond ‘society’ and ‘opinion’ for which thetext lays out a genealogy, appear the ‘traces’ that mustbe theorised as ‘replications’ by the social sciences inorder to reap the benefits of the uncertain status of en-tities’ widespread traceability. High frequency repli-cations as a collective phenomenon did exist beforethe emergence of digital networks but now they leavetraces that can be computed. The third generation ofSocial Sciences currently emerging must assume thespecific nature of the world of data created by digitalnetworks, without reducing them to the categories ofthe sciences of ‘society’ or ‘opinion’.

Keywords: Big Data, social sciences, opinion,propagation, quantification

When in 2007 Savage and Burrows pointed out ‘thecoming crisis of empirical methods’, they were not ex-pecting to be so right. Their paper however became alandmark, signifying the social sciences’ reaction tothe tremendous shock triggered by digital methods.As they frankly acknowledge in a more recent paper,they did not even imagine the extent to which theirprediction might become true, in an age of Big Data,where sources and models have to be revised in thelight of extended computing power and radically in-novative mathematical approaches. They signalled notjust a debate about academic methods but also a mo-

mentum for ‘commercial sociology’ in which plat-forms acquire the capacity to add ‘another major nailin the coffin of academic sociology claims to jurisdic-tion over knowledge of the social’, because ‘researchmethods (are) an intrinsic feature of contemporarycapitalist organisations’ (Burrows and Savage, 2014,p. 2). This need for a serious account of researchmethods is well tuned with the claims of Social Stud-ies of Science that should be applied to the social sci-ences as well.

I would like to build on these insights and princi-ples of Burrows and Savage to propose an historicaland systematic account of quantification during thelast century, following in the footsteps of AlainDesrosières, and in which we see Big Data and Ma-chine Learning as a major shift in the way social sci-ence can be performed. And since, according toBurrows and Savage (2014, p. 5), ‘the use of new datasources involves a contestation over the social itself ’,I will take the risk here of identifying and definingthe entities that are supposed to encapsulate the socialfor each kind of method: beyond the reign of ‘society’and ‘opinion’, I will point at the emergence of the‘replications’ that are fabricated by digital platformsbut are radically different from previous entities. Thisis a challenge to invent not only new methods but alsoa new process of reflexivity for societies, made avail-able by new stakeholders (namely, the digital plat-forms) which transform reflexivity into reactivity (asoperational quantifiers always tend to). This great

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Big Data Challenge for Social Sciences: From Society and Opinion to Replications

Dominique Boullier

transformation is built by and for the sake of brandswhich are framing everyone’s perceptions, from trulycommercial brands to scholars for their H-index,cities for their attractiveness, youngsters on YouTubeor politicians on Twitter. This is why ‘the end of the-ory’ (Anderson, 2008) concerns everybody. It alsomeans the end of some kind of reflexivity, the end ofthe opportunity to connect long-term trends andopinion movements to personal experience by focus-ing only on replications, the high frequency dimen-sion of the social, although the social is made of allthree of these ‘wavelengths’ (Society, Opinion, Repli-cations). My proposal is not to disqualify data andprocesses related to high-frequency replications, butto maintain a pluralistic range of analysis while agree-ing to take up the challenge of Big Data and MachineLearning. Finally, there is no reason why the social sci-ences’ ‘authority’ should escape the challenge to allkinds of authority, intermediation and power gener-ated by the digital revolution. This seems more com-patible with a pragmatist approach, which requires usto understand the social from within while not beingtrapped in the hype of the promotion of this new ‘ap-paratus’ that is yet to be established as a convention(Eymard-Duvernay et al., 2004).

Therefore, the main challenges for the social sci-ences are twofold: fast data, and inductive methodswhenever they are used for tracing social behaviours(and not in genomics or in astrophysics for instance).This framework can help compare ‘epochs’ of quan-tification, in order to understand the conditions re-quired for these new conventions to be built.

1. Trouble over entities in the DigitalAge

Neither people nor identities nor communitiesbut traces are the ‘raw’ materialFor many years, but in an extended way with socialnetworks, computer science has calculated and mod-elled the social as if the traces collected allowed accessto the ‘truth’ about individuals in a more effective waythan do polls, surveys and censuses. Consider two ex-amples, one academic and the other commercial:

• ‘The Web does not just connect machines, it connectspeople.’ (Knight Foundation, 14 September 2008).There you are, this is what Sir Tim Berners-Lee, co-founder of the Web in 1991, with René Caillau,stated to emphasise the transition to a dimension ofnetworks which is neither technological (III for Inter-national Information Infrastructure) nor documen-tary (WWW), but social (GGG for Global GiantGraph).

• Facebook has managed the tour de force of ‘normal-ising’ members’ declaration of their true identity, thatis to say, the features of identity provided by the civilregistry, the name and surname, as opposed to the tra-dition of anonymity on the Web. The platform thusclaims to have become the authority of reference oreven a civil-registry-alternative, competing withGoogle in this regard. This is becoming usual when-ever one uses a Facebook account for creating and cer-tifying access to other apps, for instance.

Yet there is no guarantee whatsoever of any con-nection between the identities on Facebook, or Bern-ers-Lee’s ‘people’, and persons identified by their civilregistry. What are connected are merely the retrievedaccounts and data, and these are only the traces of ac-tivity from an entity, which could possibly take on theform of a legal civil status. Based on the scores thatclassify sites on a search engine, the resulting topologyof sites and blogs never discuss their contents as such,but rather the inbound and outbound links that pro-duce a rank of authority or hub, as defined in the net-work topology (Kleinberg et al., 1998) and are not acivil status. It should be noted here from the outsetwhat I mean by traces, in order to distinguish themfrom data. Traces can range from signals (so-called‘raw’ ones, generated by objects) to unstructured ver-batim; they can be traces exploited in databases (links,clicks, likes, cookies)1 by operators or platforms, butalso captured independently of this through the APIand, as such, fall outside permanent relational data-bases (Bowker and Star, 1999). Traces are not neces-sarily pre-formatted for a specific calculation nor arethey dependent upon aggregation that can then be ap-plied. It is easy to argue that, despite everything, ‘be-hind’ these sites or ‘behind’ these clicks, there are most

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certainly people, but that does not alter the fact thatthe algorithms themselves do not take this onto con-sideration and that, furthermore, no guarantees canbe given in this regard. Traces understood in the re-stricted sense are produced by platforms and digital-technological-systems, but are not the ‘signs’ orevidence of anything other than themselves, as longas relationships with other attributes are not createdand validated. Of course, they are transactional databecause there is no trace without a distributed settingof relationships including humans and non-humans.This differs radically from the data that can be recov-ered en masse from client files or from administrativeacts. Certainly, the Big Data methods for calculatingcan be applied here in both cases, but the traces are apriori independent of other attributes, in particularsocio-demographic features, which are rarely used incorrelations between traces. Relationships with moreconventional parameters in data sciences are limitedto time (a timestamp) and location (geo-locationtags), which allow for the production of timelines andmaps that become simplified modes of representationfor traces. Can the social sciences accept this shift inthe ‘raw’ material they usually process?

Traces are produced by platformsIn order to get into a thicker description of this sociallife of data, consider how it works for Amazon orApple. The Web is no longer distributed but monop-olised by these four platforms – GAFA that centralisethe majority of traffic, with Twitter extending thistraces industry, while Microsoft is trying to get backin the battle by purchasing the social networkLinkedin. What I described above about traces andtheir detachment from the legal ID’s features wasmore central for platforms such as Google and Face-book. The pretention of these platforms to performtheir version of a ‘society’ should be noticed accordingto the three wavelengths of social sciences above men-tioned. On such consumer platforms as Amazon andApple, it is not people who are linked to one anotherbut above all tastes (books or music originally), re-flected in traces of purchases, and thus of choices,which can be treated en masse to produce patternsand profiles, independently of personal information.

It should certainly not be forgotten that all these plat-forms without exception are also very fond of civil sta-tus-type data, phone numbers and other highlyattractive resources to advertisers, to whom they aresold. Amazon and Apple are designing the perfectmarket environment in which preferences can betraced, they built their own sociological device fo-cused on ‘opinion’, the second wavelength I men-tioned above. The marketing methods thus developedare largely based on mass advertising or on emails ad-dressed to IP addresses, or emails that have clicked onan article (retargeting), and much more rarely on so-phisticated links with other attributes of the supposedpeople attached to these addresses or clicks (profiling).Traces of digital behaviour are thus a particularly prof-itable ‘raw material’, without the need to appeal tothe social sciences, although platforms, social listeningagencies and marketing experts readily make use ofacademically coined terms such as ‘communities’,‘networks’, ‘engagement’, ‘tastes’ and so on. A distinc-tion could therefore be made between social networkplatforms and consumer platforms, despite the trendtowards some combination in many of them. On theone hand, the social registry that Facebook andGoogle tend to build is akin to what states and socialsciences did when designing censuses in an attemptto assemble socio-demographic features of individualsand traces left while using apps and interacting with‘friends’. On the other hand, consumer platformssuch as Apple or Amazon are more dedicated to a sortof mapping of affiliations, types, styles, and prefer-ences, all of which are more ephemeral or more pre-cisely cyclic than the traditional social features. Thisis quite close to the traditional social study of moods,fashion, and opinions in political life or in any mar-keting area, which consist of individual expressions,aggregated and mapped to make the opinion live, orof trends in cultural tastes. However, it is true that thesocial sciences and marketing research methods diduse sampling and were indeed concerned about main-taining the connection with the socio-demographicfeatures of the individuals they selected in their sam-ples, which is not true for Amazon or Apple and anyother consumer platforms. These two sets of digitaldata (socio-demographic features on the one hand,


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with Google + and Facebook, and preferences andtastes on the other, with Amazon and Apple) are notso new, indeed. Due to the specific way of producingthem in a natively digital format and sometimes onthe fly, they cannot be considered as substitutes forother protocols such as censuses or surveys. This isnevertheless how they come to be used by many datascientists and by some social scientists seeking short-cuts to the social. Twitter plays its own part, whichpaves the way for social sciences of a third kind, ac-counting for the high frequency propagation processes.

2. The making of ‘society’

As mentioned above, computer scientists do not hesi-tate to make use of social science categories such ascommunities, networks, and so on, even though theyvery often lack adequate knowledge of these concepts.This is a part of what I call ‘algorithmic positivism’.Yet one cannot blame them for the use of a conceptlike ‘society’ since there is nothing more ‘taken-for-granted’ among members of society. This is why it maybe risky to adopt a constructivist viewpoint and try toconvince data scientists that ‘society’ was designed andpromoted in such a successful move that no one daresto question it. If we want to understand the historicaltimes that we are living in as regards quantificationmethods (and the social reflexivity that is attached toit), we need to look back to the times of the construc-tion of this entity, ‘society’. Let us pretend here thatDurkheim succeeded in making ‘Society’ exist. Theterm was not coined by Durkheim, obviously, al-though its history is not a long one. The archaeologyof the concept of society (Latour, 2005) could be fur-ther enriched by calling upon the work of Quetelet,who produced the ‘average man’ which long remainedthe key to all statistics. At the end of the nineteenthcentury, however, and largely thanks to Durkheim’s ge-nius, ‘society’ took a strong stance regarding ‘commu-nity’, which was still prevalent (see Tönnies, 1887).Durkheim’s early work on the ‘division of labour in so-ciety’ (1893) was not based on statistical methods, butinstead laid the foundation for a model of social types,aggregated in mechanical and organic solidarity. De-tailed examination of legal systems served as demon-

strations and therefore relied on the groups formed orbeing formed that are legal systems in their traditionalor more modern aspects. With ‘The Suicide’ (1897),the method was set up to extend the discussion of thetypes that would reveal anomie to be a problematic sit-uation. But reliance on data records produced bystates, from their various components (ministries, pre-fectures, governments) became key to the demonstra-tion. It was these aggregates that are explained orexplanatory, using a method of comparison betweencountries, regions, counties or districts, where possibleand necessary. The method depended entirely on theavailable data and could not afford to criticise or toquestion the procedures for the production of thisdata, despite the countless limitations identified uponpublication (Douglas, 1967). By organising all his sys-tems of proof around these national administrative sta-tistics, Durkheim found a quantitative analogue forhis conceptual choice that put ‘Society’ in a separatestatus from all manifestations and individual behav-iours. Durkheim’s whole became an entity of the sec-ond degree, ‘Society’, (Latour, 2005), while thecensuses and other state-data-registers simply performthe task of recovering individual, administrative events(marital status, judicial procedures, etc.), formatted inidentical categories and aggregated to reveal the behav-iour of populations. Durkheim’s strength of convictionwould have been to make these statistics exist as equiv-alent to his ‘society’, where the quantification is ableto account for a ‘whole’ through the quality of exhaus-tiveness, while the concept accounts for the agency ofthe social structure as such.

It is necessary to note that a form of ‘convention’was formed between data producers from the state ad-ministrations and the emerging social sciences. To-gether they produced the entity ‘society’ as the objectto be tracked by the state for the purpose of governingand to be explained for scientific reasons. The result isthe widely shared and obvious fact that ‘society’ exists,and the methods that allow it to do so have no groundsto be questioned because they demonstrate both theirscientific and their operational value: they are ‘tools ofproof ’ and ‘tools of government’ as Desrosières (2014)put it.


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The age of calculations and calculatorsOther historical proximities are noteworthy, that donot mean causality but that do allow for an under-standing of the power-gains this approach affords inmaking society exist. In 1890, Hollerith used his ma-chine (that he had invented a few years earlier and forwhich he filed a patent application in 1886) to con-duct the U.S. census. The Census Bureau had not yetfinished processing the previous census dating backto 1880 when it had had to start the next one. Achange in technique was both necessary and available.Hollerith’s tabulating calculation-machine did thework and was sold for doing censuses in several coun-tries. His company would later be transformed intoIBM by Watson, in 1926. We can see how the powergained in the counting and description of populationsreinforces the status of the State and offers it suppos-edly useful sources of information for its governance.The pretence of the calculation’s exhaustivenessseemed to fulfil the promise of the concept of society:a technical device capable of inputting all that existed,that as Hollerith’s census-procedure-equipping ma-chine. It should be noted to what degree the invest-ments of form (Thévenot, 1986) that are censuses, endup being events in a given population and become in-disputable, appearing almost ritually, to portray allthat is social onto the members of this society.

Alain Desrosières had amply demonstrated thisprocess by showing how Durkheim’s concept of soci-ety took the genesis of nation states into account. Na-tion states rely as much on these figures as they do oninfrastructures. From this point on, territory becamea key mediation, of which we find traces in the emer-gence of social welfare policies or in the developingnational commodity markets, owing to the railroads,and then in national electoral campaigns through themedia (the press also circulated via the railroad, afterwhich came radio, that would become a vector for theemergence of opinion).

In modern Western countries, the State gained itslegitimacy through electoral processes that rely on na-tions, those ‘imagined communities’ (Anderson,1991) that work as content while the State is the con-tainer (Boullier, 2011), or the ‘materiality’ and the‘statement’ that constitute the apparatus in Foucal-

dian terms (Foucault, 1982). This is why ‘society’ isalways enacted in various ‘nations’ although social the-ory tries to extract it from the limitations of nationalboundaries. The first generation of social sciences wasindeed doomed to methodological nationalism (Beck,Sassen) and still has problems inventing the methodsto account for a globalised world made of flows (fi-nance, media, commodities, migrants, and so on).However, we shall see that when examining this dig-ital world, it is quite difficult for us to escape ‘method-ological platformism’, due to the total dependency onthe traces data platforms deliver!

Durkheim’s achievement has been to form an as-semblage of very powerful mediations:- Censuses- Assembled and formatted by public administrations- Under guarantee of exhaustiveness- For States- For government purposes- To produce ‘society’- Using tabulating calculation machines

3. The construction of ‘opinion’2

The contemporary situation is undoubtedly not thatfar from another key moment in the history of the so-cial sciences that would help us to understand bothwhat is happening and the conditions of felicity ofnew conventions. This is why we will consider somefeatures of that period when opinion polls were in-vented, as an indication of the equivalents in ourtimes. After the first generation of quantification,used by Durkheim to build the concept of ‘society’,we could give the label ‘2G’ to the emergence of pub-lic opinion in the late 1930s. In 1936 George Gallupwas able to predict the election of Roosevelt over Lan-don, based on a survey of 50,000 people. Roper andCrossley had done likewise at the same time. Gallupnot only impressed the media and policy makers, heradically disqualified older methods (straw polls), in-cluding that of the Literary Digest, based on responsesfrom 2 million people, whilst even predicting theirown erroneous results (Osborne and Rose, 1999).This impressive demonstration lays the foundationsof the survey’s reliability and of investigative sampling

methods. The exhaustiveness of inquiries on entirepopulations was indeed sacrificed in the process, butthe new approach managed to produce accurate re-sults, provided that the terms of representativeness wererespected. It nevertheless failed to predict the victoryof Truman in 1948, whose voters changed their mindsin the last ten days. Methods thus applied to politicallife and to life-size tests as important as a presidentialelection had previously been tested on readershipstudies for which Gallup had operationalised stratifiedsampling. In fact, these methods had already been ap-plied by the Norwegian Kiaer in 1894. Similar statis-tical methods in the field of agriculture and later inunemployment in the early 1930s, in the USA, un-derwent profound changes, from the correspondents’method to random sampling based on probabilisticapproaches (Desrosières, 1993) – as Emmanuel Di-dier has also shown (Didier, 2009). Quota methodsbased on ‘sensible choices’, where the selected sampleis matched with certain properties of the populationidentified by the census, were however different fromthose methods of stratified random selection, andwere even despised somewhat by statisticians3 (cf.Stephan quoted by E. Didier). The data collected werealso very different, since statisticians from agriculturalor employment administrations wanted to obtain‘facts’, but were nevertheless obliged to rely on state-ments, not measuring machines, even if they at-tempted the latter with the ‘crop meters’. Yet thesampling-legitimisation-operation generally suc-ceeded, primarily thanks to Gallup’s performances(1939), which were dedicated entirely to other socialworlds; those of ‘public opinion’ and not ‘society’. Thelatter remained a reference of statisticians of the fed-eral state and its offices. It was unquestionably in thecontext of the mass media that the importance ofsampling was recognised. With Ogilvy, Gallup stud-ied film audiences, and then with Crossley, at Young’sand Rubicam’s, he studied radio audiences, using tele-phone interviews before even making a proposal toconduct the election polls. From this point of view,Gallup’s name must be associated with that of Lazars-feld, who in the same period, in 1936, launched a‘Radio Research Program’, based on audience-re-search-work begun in 1930.4 Together with Merton

they launched the focus groups method as early as1941, and their study of Decatur in 1945 providedthe data for the analysis of ‘Personal Influence’ pub-lished in 1955 (Katz and Lazarsfeld, 1955). The latterstudy established the framework for analysis of the‘two-step flow’ in which mass media play a role, butthrough the mediation of various kinds of influentialrelationships.

The links between the mass media and politics arethus elements of new statistical methods. As AlainDesrosières noted (1998), a national election’s pre-dictability actually depended upon the formation ofa common public media-space across the UnitedStates, and only the radio could do this in such a wayas to make the voter’s knowledge about electoral can-didates comparable. Considerable media transforma-tion and the mass media (radio at the time)established the conditions for the emergence and val-idation of a survey technique, which thus opens up awhole new era, most notably for political science andmarket research. Moreover, it is ‘public opinion’ itselfwhich takes on a measurable existence with these sam-pling methods whose performative power will by farexceed their experimental phase. This move does notdisqualify the previous assemblage made by censusesand mecanographic calculators that will soon betransformed in computers. But the role of radio (andtelephone for the report of results and their aggrega-tion at a faster speed) contributes to a new apparatus.

Markets and national publics: the scale ofthe mediaThe missing link in my description remains the vehi-cle of financial incentives for such investments,needed to understand a public. Communicationagencies such as polling organisations cannot livesolely from their campaign activities, even if they dobring them high visibility and renown. From the out-set, their target was the mass media, as noted above,for one essential reason: audience measurement hasbeen the key to the distribution of advertising space,since the dawn of radio and then later with television(in 1941 the first advertisements were aired on Amer-ican television for Bulova watches, during a baseballgame). But these measures also serve to monitor the

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impacts of these campaigns on the minds of con-sumers, giving an unprecedented boost to marketing,which in turn drives increasingly sophisticated com-munication strategies (Cochoy, 1999). Brands havethus been present, from the outset, in methods of in-quiry into opinion using sampling; that is, from themoment such investigations were aimed primarily atmass-media audiences. Market research on consumergoods developed at the same time, from the 1930s,and in the same movement of national standardisa-tion of products, as Desrosières pointed out. The pro-duction of a unified national territory, through themedia, that included transportation and mail, estab-lished a new condition of felicity for these surveymethods. This allows me to draw a direct parallel withthe recent creation of a global market, this time be-yond the national ones, through the domination ofdigital platforms. Google, Apple, Facebook and Ama-zon have produced the same effect on a global terri-torial scale as radio and the railway had on theterritory of national markets. This is in line with thework of McLuhan (1964), for whom the change ofscale in itself constitutes another world far beyond theproperty or goods exchanged.

Public opinion exists, I measured it!The work done by Gallup on the operational side andLazarsfeld on the scientific side is therefore not simplya marketing operation or a face lift for the social sci-ences: it provides whole societies with methods withwhich to analyse themselves and to represent them-selves – as opinions. Tarde (1901) has certainly high-lighted the importance of these views, yet it is onlywhen the metrics are established and produced in aconventional way that opinion finally exists. Only themedia’s control and their ability to produce a unifiedpublic in a national territory enabled this method-ological assemblage to hold. The ‘whole’ referred toby the polls is in fact originally the public formed bythe media, which allow the audience to emerge as pub-lic opinion and to make it permanently visible andmeasurable. This connection between audience meas-urement and monitoring methods for public opinion,a connection that is both technical and historical,must be regarded as the key to the device: the media

want, above all, to measure audiences, as did Gallupfor reading, but the techniques in place turned intopredictive voting tools, which justified this betting onpublic opinion. The whole ‘audience’ or even ‘public’has mutated into ‘public opinion’ and managed to de-tach itself from its own reference within the media(which measure themselves), for the purpose of beingexploitable for brands to measure the influence oftheir campaigns. The parts (Latour et al., 2012) thatare individual expressions are preformatted to berecordable and calculable, but the link between theparts and everything else is made only by the pollsters’black boxes. The rigorous, scientific precautions areupheld through ‘confidence intervals’ (defined byNeyman in 1934), which keep a reference on the ex-haustiveness of the studied population. Bowley(1906) proposed these principles in 1906, whenspeaking of the ‘probable error’, allowing for the clearlinking of the polls and the emergence of statisticians’probabilities. But soon these precautions will disap-pear from the findings, as seen in the contemporarymedia. At this point, everyone knows that ‘opinionexists’, whatever the report about the artefacts neededto make it exist, and despite what Bourdieu said aboutit (1984).5 It has been naturalised, ‘taken for granted’,and the sampling methods lie buried beneath thepowerful performative effect of these immediate, ag-gregated indices. The approximation remains accept-able, especially with the repetition of the samequestionnaire over time (by panel, independent rotat-ing sample) under identical conditions, ‘all thingsbeing equal’. It allows for the smoothing-out of biaseswhich then become acceptable by convention. Suchsuccessful convention work focuses on the same as-semblages of mediations already mentioned for society:- the ‘surveys’ and ‘polls’ (from individual expressionsframed by questions and thus made calculable)- assembled and formatted by pollsters- guaranteeing the representativeness of samples (sam-pling)- for the media- for the purpose of monitoring- to generate public opinion (and audiences).


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As Alain Desrosières (2008) put it, the essentialthing is not whether these data are a reflection or mir-ror of society or something else, but rather whetherthey ‘make something that stands by itself ’. Note thatthere is a new element at work in this chain: that ofthe methodological limitation, expressed in terms ofthe representativeness of the samples, because this el-ement is still missing in digital traces, which explainsmuch of the uncertainty and suspicion on all resultscompared with the polls, for which the ‘biases’ arewell known but have been controlled by conventionsince the 1940s. The ‘consolidation’ that EmmanuelDidier (2002, 2007) describes for statistics and sur-veys remains to be done. This rather long account ofthe successful fabrication of opinion was necessary notonly to understand the similarities between that‘epoch’ and our current one, but also to measure thedistance and the work required to produce conven-tions of equal quality (Eymard-Duvernay et al., 2004)that would make ‘traces’ exist as entities recognised bythe social sciences. We certainly need to consideropinion to be a social reality that lives its life and isno longer called into question, thanks to the qualityof technical and institutional arrangements that sta-bilised its mode of appearance, notwithstanding themany critiques that they still face. And they will facemany critics more often due to the complete remod-elling of the media conventions by the social networksactivity. To be sure, the worlds of social science andmarketing differ, yet for years they have used the samemethods and even the same samples while being ableto distinguish themselves from one another. Withinthe shared new world of traces emerging on the web,how can we invent the social sciences in a way thatsuits these traces whilst admitting the conditions oftheir production and utilisation?

4. Three generations, three points ofview on the social

Building on these two historical landmarks of ‘society’and ‘opinion’ and on the mediations which managedto make them exist as taken-for-granted entities, Ipropose a comparative table in which the next gener-ation of social sciences is designed along the same cri-

teria in order to devise some roadmap for the consol-idation of the conventions. Digital devices generatenew opportunities for quantification but not only interms of volume. My main statement is to emphasisethe emergence of new entities, apart from society andopinion; entities that have an agency of their own andthat I call ‘replications’, propagated along networksin the material aspect of traces. The following tablewill make the comparison more visible and will intro-duce my understanding of the stakes of third-genera-tion social sciences.

The excessive coherence of any table must notmake us forget that what is at stake is the constructionof a proposal for the third-generation of social sci-ences, which is in no way guaranteed. Actor NetworkTheory has indeed laid the foundations, building onthe methods of scientometrics (and previously of bib-liometrics), and Tarde (1890) did announce the prin-ciples as a general theory of imitation (includingopposition and invention, not to be forgotten). But,for now, the trend is more at the ‘end of theory’ andthe occupation of the field by the Web platforms(GAFA). They produce, calculate and publish them-selves on these traces, and all for commercial purposesprimarily because major brands are demanding theseapproaches. I shall describe the choices available tothe social sciences in such a context. As the table sug-gests, not only does Big Data challenge the status ofsocial sciences in terms of empirical capacity and ofmodelling without theory, but Big Data really needsBig Theory for the social sciences to keep their rolealive. What begins as a historical comparison, wherethe succession of generations can let us believe thatthe next one makes the previous one obsolete, is turn-ing into a more diplomatic statement, where each ap-proach is able to grasp one specific aspect of the socialthat others cannot account for. This derives from thesocial studies of science led by Bruno Latour (1987),who has deeply transformed our understanding of theprocess of scientific knowledge. The insistence on theagency of devices, of scientific devices, can be used forsocial science as well, because one could say ‘we’ve gotthe sciences of our devices’. The census and the pollshave built entities that highlight some specific dimen-sions of the social which came to be framed as ‘society’

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and ‘opinion’, but digital devices make somethingnew appear.

Generations, waves or viewpoints on thesocial? From this historical account and from this diplomaticmove, a general pattern emerges. The social sciencesadopt a rather limited number of perspectives relatedto these devices and to the entities that have been con-structed. In short, ‘society’ is generated by a ‘structure’approach; ‘opinion’ is produced by a ‘market’ ap-

proach; and ‘replications’ (those of the digital world)are discovered through an ‘emergence’ approach.When trying to account for the various controversiesin the social sciences, one will eventually revert to thisclassification. And, more importantly, these old dis-putes can be considered as ‘viewpoints’ on the world,at the epistemic level, equipped with different devicesand targeting different entities. We may talk of thesedifferences in terms of ‘wave lengths’ as we did previ-ously: ‘structure’ analysis focuses on long wavelengths,‘market’ on mid-term and cyclical ones, and


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Table 1: The three generations of social sciences

1st generation 2nd generation 3rd generation

Concept of the Society/(ies) Opinion(s) Replication(s)social

Collection devices Censuses Surveys/Polls Platforms

Validation principle Exhaustiveness Representativeness Traceability

Co-construction institutions/ Registers/ Audience/ Polls Traces/ Repurposedresearch inquiries digital methods

Major players of reference States/ Nations Mass media/ Audiences Platforms/Brands(and funding)

Operational actors National Institutes Polling organisations Web platforms (GAFA)

Founding authors Durkheim Gallup, Lazarsfeld Callon, Latour, Law

Key problems of scientific Division of labour and the Propaganda and media- Science and technologyapproaches welfare state influence (audience (scientometrics)

measurement)

Technical conditions Hollerith’s machine Radio and telephone Internet, Web and Big(tabulating calculation) Data

Semiotic formats Crosstabs and topographic Curves, bar charts / pie Graphs, timelines,maps charts and topology of dashboards

influences

Metrics Statistics Sampling TPS (tweets per second) (scores) and similaritymatrix

Technical criteria for data Relevance, accuracy, Confidence intervals Volume, Variety andquality timeliness, accessibility, Probabilities Velocity

comparability, coherence

The social science’s Explanations Descriptive and predictive Predictive correlations,dominant modalities correlations memetics

This table can lead to a kind of ‘quantic sociology’,because one cannot adopt all points of view at thesame time and cannot sum up the views of ‘the worldout there’ in a ‘whole’ by assembling the outputs ofeach method. When changing methods, tools and ob-servers’ points of view, the focus shifts from waves toparticles, from positions to trajectories. Even the net-work analysis is not a shift as such since there aremany ways of selecting the features of the networkwhich are agencies of their own: its structure (ho-mophily for instance), the nodes (influentials for in-

stance, Watts, 2007), the entities that circulate andmake the connection (goods, messages, memes for in-stance). This pluralist and constructivist view of socialsciences approach offers the opportunity to distributeagencies among all entities and features while accept-ing the un-completeness of any account of the social.

The wavelengths of JeSuis CharlieLet’s illustrate this with a discussion about ‘Je suisCharlie’, the global movement that followed theCharlie Hebdo and Hypercasher attacks in January

‘emergence’ on high-frequency ones. This should re-mind historians, with a slight translation, of the Fer-nand Braudel’s famous distinction: long-term history,cycles, events (Braudel, 1996). He was right not tocondemn any of these viewpoints for the sake of an-other, and only to advocate sufficient diversity(against the trend, at the time, of focusing on events).All these three viewpoints could easily fall under ageneral theory of attraction, as accounting for the so-cial in general: long-term attractions of social tradi-tions, habits, and repetitions (the ones sociologistslove to love); cyclic attraction of fashions, politicalopinions, tastes and preferences in general (the oneseconomics, marketing, psychology and political sci-ence are fond of ); and high-frequency attraction ofreplications (that make the buzz as well as the finan-cial speculation). Imitation is not a matter of long-term structure,thecause it emerges at the very momentit occurs, nor a matter of strategic decision, because

the time lapse is so short that reactions are quasi ‘un-known’ to the subject, for these subjects are only thetargets of replicators, entities that connect every mindin a millisecond. This existed before the digital conti-nent emerged, such as in ‘olas’ or Mexican waves,crowd moves, rumours, and so on. But no social sci-ence has ever been equipped for documenting thesecontagious processes (Sperber, 1996) in which speedis so critical. Today the platforms amplify theseprocesses and produce the leverage for measuring andtracking them.

This extension from a historical approach to dif-ferent conceptual frameworks of the social has manyimplications and help understand why it is so difficultto speak of solid ground for social sciences: there arethree points of view, none of which is wrong but onlybrings a different perspective to make different entitiesexist.

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Generations 1st Society 2nd Opinion 3rd Replications

Points of view Structure Market Emergence

Wavelengths Long term Mid-term, cycles High frequency

Features of the networks Structure Nodes Circulating entities

Main concern Positions Decision Propagation

Process Inheritance Arbitrage Neighbourhood

Status of human actors Inheritors, determined Strategist, decision-maker, Vehicle for memes’subjects rational agent propagation


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2015 in Paris. A controversy emerged in Francearound the book of historian and anthropologist (ex-pert in demographics) Emmanuel Todd, a rather fa-mous and maverick kind of scholar. He rushed topublish his book (Qui est Charlie?, in May 2015)based on the correlation between the number of pro-testers in the streets in France and the age-old historyof Catholicism in each city. The book used well-known maps of the conflictual period of the FrenchRevolution that were based on priests’ acceptance orrejection of the new regime. I shall not get into thediscussion of the main hypothesis, that demonstra-tions were stronger in the regions where Catholicismused to be strong and are a significant anti-Muslimsignal of this ‘zombie Catholicism’ in France, wheretraces remain although the active practice of the reli-gion has fallen sharply. However, the method is some-what unbalanced in favour of long-term waves: theonly indicator used for the analysis of the demonstra-tions is the number of participants estimated both bythe police forces and by the leftist newspaper Libéra-tion, in a sort of reciprocal compensation. The figuresare ‘botched’ as the author said, but will do in thisstate of emergency (in order to deflate the mood ofunanimous republican celebration). What is more in-teresting, is that he did not consider it to be of anyinterest to ask the demonstrators any questions since,as he said, ‘very often, they did not know how to ex-plain their participation’, as all of them were ‘carriedaway by the mimetic intoxication of a saturated mediaspace’ (p. 21). The three generations in the same sen-tence, it seems: surveys do not make sense with ‘cul-tural idiots’ such as demonstrators and their ‘opinion’does not exist since the media are reaching a level ofexcitement that is contagious (mimetic intoxication),and this does not need any investigation, except a crit-ical stance from the point of view of the true and onlysocial science, the one based on long-term indicatorsthat crush all the mediations of events and of opinionsin a single move. Of course, French experts in politicalscience (Mayer and Tiberj, 2016) clearly documentedthe capacity of sampling surveys to account for a partof their motivations and their background (surveyswere conducted on a large scale right after the demon-stration). And Twitter specialists published dynamic

maps of the contamination of the hashtag “#JeSuis-Charlie” which managed to propagate all over theworld in the following six hours. This global dimen-sion of the process was not relevant for a social scien-tist who relied on its ‘methodological nationalism’,due to the idiosyncrasy of a general cause such as theFrench Revolution. More significant is the fact thateven at the level of the demonstration, the specificityof the event was missed by the author: when twodemonstrations took place, as in the case of Marseille(one for the left, one for the right), the score was justan addition of the two, as if a common demonstrationdoes not make a difference in such circumstances. Thelimits of the model when applied to Paris or to Stras-bourg were acknowledged, but never could they ques-tion the model itself. The demonstrations did nothave a life of their own; they were unexpected emer-gences of a ‘collective’ that would be quite difficult tolabel (‘the people’?), and for sure doomed to disappearthe next day, but were still a striking experience forthe participants. According to Todd, these demonstra-tors were just a number that can be correlated to theonly deep, real and permanent causes of French po-litical behavior, two centuries later (with this delay ex-plaining the ‘zombie’ rhetorical trick, since themediations – religious behaviour – disappeared fromthe long-term radar!). For opinion experts as well, theuse of polls was the only way to account for this sud-den change in the mind of so many people, and fromtheir individual expressions they were able to build animage of what ‘public opinion’ was saying – a muchmore complex view than the simple causalities oflong-term social sciences. However, they did not makeany use of the tremendous amount of data generatedon internet, via Twitter, Facebook, Instagram andother social networks, and on blogs, media websites’comments and so on. The public’s emotional experi-ence was expressed not only during demonstrationsbut also earlier, immediately after the attacks, througha contagious extension of some hashtags and espe-cially through the propagation of the meme “#JeSuisCharlie”. Thanks to the traceability providedby social network platforms, the global phenomenonof contagion becomes visible in real time, as shownon the following map and timeline.

(source: http://www.reputatiolab.com/2015/01/analyse-de-je-suischarlie-sur-les-reseaux-sociaux/

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07 Jan 10h

07 Jan 12h

07 Jan 14h

07 Jan 16h

07 Jan 18h

07 Jan 20h

07 Jan 22h

08 Jan 00h

08 Jan 02h

08 Jan 04h

08 Jan 06h

08 Jan 08h

08 Jan 10h

08 Jan 12h

08 Jan 14h

12 000

#JeSuisCharlie5 ago months by srogers

9 000

6 000

3 000

0

Tweets


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These traces data seem to provide very little infor-mation except for the volumes that they reflect: noth-ing about the Twitter accounts, profiles or networks;just a very simple display along timelines, showinghow strong yet ephemeral these kinds of propagationsare, and another very basic spatial representation,powerful for demonstrating the extension of the con-tagion worldwide but not precise enough to allow cor-relations with any other spatially referenced dataset.This is enough however to trigger more investigationwithout discarding such a global phenomenon, andto challenge the social sciences to account for thesehigh-frequency and short-term waves without usingtheir traditional tools and concepts. All three pointsof view can bring some insights, none of which canbe claimed to be the ultimate cause – in Tarde’s criticalterms, the ‘cause-finaliers’ –, or to assemble all of themin an overarching vision of a ‘whole’ which is just aneffect of the devices we adopt to delimit the social lifecontinuum (Latour et al., 2012). Some may look forthe translations from high-frequency traces (expres-sions or hashtags) to the cycles of opinion or to thelong-term memory of an event that will become a partof the globalised collective experience. Why not? Butthere is so much to do first to understand the speci-ficity of these waves of traces and to be sure of whatcan be extracted from them, that a precautionaryprinciple should apply to the reconnection of all thesewavelengths. In order to do so, we need to build theconventions that will guarantee ‘sufficient scientificity’to the social sciences of the Third Generation.

5. Brands’ grip on traces

This may look like a gamble since all these traces areproduced and controlled by digital platforms and, ul-timately, by the brands that are funding these socialnetworks for their own purposes: is there a way to es-cape the ‘methodological platformism’ (or ‘platformbias’; Marres, 2017) that can be observed for instancethrough computer and social scientists’ focus on Twit-ter? Where does this fascination with traces – as op-posed to data from registries and surveys – comefrom, despite their limitations? Does it come from theimpression that any observer can grasp the whole of

the phenomenon on such open platform as Twitter orto the fact that Twitter’s code encapsulates a quasi‘meme machine’, i.e. the Retweet function? The busi-ness model seems to be a better rationale for this at-tractiveness. The traces are actually a key resource forbrands to monitor the impact of their actions on thepublic. Reputation and renown no longer translateuniquely into audience measurements; this would bea simplistic import of measures built for the massmedia. On networks, one must measure not only aform of audience (the reach) and the most basic ac-tivities of its uncertain public (likes, stars), but alsomore sophisticated activities such as comments, whichconstitute what is called ‘the engagement rate’. Brandsare fond of these traces and it is they who fuel theturnover of all these platforms, and thereby of the en-tire Web. The opinion mining and sentiment analysistools (Boullier and Lohard, 2012) are thus the answerto ‘the marketer’s anxiety after the product launch’.However, the extension of this brand domain reachesinto all activities, whether commercial, cultural, po-litical, institutional or even interpersonal when every-one must measure her excellence with rankings, asresearchers are requested to do (Bruno and Didier,2013). It is the brands’ methods that take precedenceeverywhere and impose their law and their pace, evenon public services. But what concerns these brandsprimarily is not structured and constructed data totest e.g. causality, but many traces that function as in-dicators and alerts, even approximate ones, not at theindividual level but at the level of trends. Similarly, itis not reflexivity that is sought but primarily reactivity,the ability to determine which lever to act upon in re-lation to the dimensions (features) of the brand thatare affected. The political world itself is now caughtup in the spiral of reactivity and its addiction to tweetsled us to consider that we have entered the era ofHigh-Frequency Politics in the image of the High-Fre-quency Trading of speculative finance (Boullier, 2016).

Platforms pick up traces of the actions and clicksof Internet-users or machines, in a standardised for-mat that aggregates them and produces a score. Thisscore is displayed and can be used by the platform it-self to show trends to guide the placements of adver-tisers who also seek to achieve certain effects and to

optimise their investment or communication choices.In a simplified format, this is the string of events thatwas produced. The performative mechanism worksalmost identically to the audience measurement(Boullier, 2004). Some then try to develop a critiqueshowing that the ‘likes’ aggregate very different sortsof behaviour, including even purchased likes. The lim-ited quality of the traces is observable on all platforms,but these limits may be intrinsic, when they do notmeet the criteria for traceability that we consider cru-cial in order to exploit them, or extrinsic when wecriticise their lack of reliable relation to the ‘real’world. It is the latter stance that we find in boyd andCrawford as regards Twitter: ‘Some users have multi-ple accounts. Some accounts are used by multiplepeople. Some people never establish an account, andsimply access Twitter via the web. Some accounts are“bots” that produce automated content without in-volving a person. Furthermore, the notion of an “ac-tive” account is problematic. While some users postcontent frequently through Twitter, others participateas “listeners”. Twitter Inc. has revealed that 40 percentof active users sign in just to listen.’ (boyd and Craw-ford, 2011, p. 6). Other studies (Driscoll and Walker,2014) tested the data produced from various accessmethods offered by Twitter, for example, and showedthat the Search API, the Streaming API and GnipPower Track (paid service) provide very different re-sults. The latter method for instance collected a muchlarger number of tweets, but not uniformly accordingto the requests! This means that the traces collectedare entirely dependent on the collection mechanisms,which is not surprising but which we do tend to forgetsince other, older methods have become conventional.

But these limitations hardly concern operators,platforms or advertisers. Their action/reaction worksin the performative mode, where the likes reveal/pro-duce a reality that will initiate strategies to influencethe likes, in a self-referential cycle to which one couldalso assign audience ratings. However, in the case ofaudience ratings, all advertisers and programmershave agreed on stable criteria and produced a sharedagreement, and evidence of this has come to forcefullyimpose itself every morning in the management ofprogrammes in the mass-media. Social network plat-

forms and advertisers have not yet reached a stablecompromise, which explains the proliferation of serv-ices that claim to be the standard, as I have shown inthe case of Klout (Boullier and Lohard, 2015), andthat want to become the Nielsen of these measures. Itis easy to see the difference between these principlesand the traditions of the social sciences, as G. Bowkerdoes (2014), and to show their extreme reductionism:‘If I am defined by my clicks and purchases and soforth, I get represented largely as a person with noqualities other than “consumer with tastes”. However,creating a system that locks me into my tastes reducesme significantly. Individuals are not stable categories– things and people are not identical to themselvesover time. (This is argued in formal logic in the dis-cipline of mereology and in psychiatry by, say,ethnopsychiatry.) The unexamined term the “individ-ual” is what structures the database and significantlyexcludes temporality’ (Bowker, 2014, p. 1797).

Bowker has cause for concern from the point ofview of ‘society’, but the third generation of social sci-ences is not so much interested in ‘society’ as in othersocial processes created by other devices, but which,nonetheless, cause us to act. Brands, reputations andrecommendations as they are exploited by Amazoncan certainly be forcefully re-injected into a matrix‘society’ to make them say what they are not made tosay. But they also say something of themselves, fromanother world, that of the power of recommendationsand contagions that the social sciences are reluctantto understand. It is as if the sociology of ‘society’ werereliving an analogous experience to the one that an-thropology provoked, that of the necessary shift withthe modern world and its categories. To be sure,Durkheim did first use it to analyse religions(Durkheim, 1912) and to employ traditional societiesand totemism for his demonstration of the power ofsociety on individuals. But Mauss (1950) made agreat side step in recognising the power of things andthe spirit that persists within them, the ‘mana’, forwhich Levi-Strauss criticised him.

It is useless to complain about the imperfection ofthe data and its approximation because we are nowdealing with traces through a process of pervasivetraceability. This traceability connects entities that did

Dominique Boull ier

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not exist beforehand, but are now endowed with anIP address (thanks to the availability of IPv6, or 3.4×1038 addresses) and so can interact just as humansdo via their machines. The vital statistics which arethe reference base for the 3rd generation of the socialsciences are no longer the censuses but an index of IPaddresses, totally agnostic about the entities that ‘arebehind’ because all act almost equivalently and causethe others to act. This shift may seem radical, but ithelps to hold together the approaches of previous gen-erations while watching the ever-present world withthe tools and relevant categories at hand.

I have drawn up a table that merits systemisation.Digital networking generates:• traces• assembled and formatted by platforms• for brands• with a view to reactivity• in order to produce rankings or patterns.

This situation is akin to the two other key mo-ments in the existence of the social sciences, especiallysociology and political science, discussed above. How-ever, these new methods and principles have still tobe arranged in such a way that they transform them-selves into ‘socio-technical conventions’.

Conclusion

Science and Technology Studies have always rely onhistorical and narrative approaches that help under-stand how the black boxes of any ‘fact’, ‘result’ or ‘de-vice’ were designed along time, through carefulassemblages of entities, properties and allies. And inthe wake of this historical look to the emergence ofnew phenomena, very often the old and familiar as-semblages appear in a new light and are reconsidered.This is what innovators and scientists do every timethey make a move: their vision bears a reinterpretationof the previous taken-for-granted black boxes. Thedigital revolution brings so many disruption that theunderstanding of many ‘old’ technologies, methodsand practices can be reassessed, as it is for media,music, printing, and so on: under the scrutiny of thishard pressure from digital innovation, social sciences

appear to rely on a set of procedures, of devices andof selective attention to some features of the social.The omnipresent technology in this digital moveobliges us to think again how was designed our pre-vious assemblages for quantification. Of course, schol-ars like Desrosières paved the road for that, longbefore Big Data meteor hits the planet social sciences.But the need to understand what is occurring andwhy these changes are so troubling can be profitablefor all social sciences, as Burrows and Savage had en-visioned many years ago. I hope this tentative frame-work can help face the challenges of the newconventions to be built in a more informed way andto give a chance for social sciences to reorganise them-selves in order to maintain their social role of reflex-ivity without sticking to disciplines that may beheading for their fossilisation and marginalisationwhen faced to the new entrants in the field of socialanalysis, i.e. digital platforms.

Notes1 Dominique Cardon has proposed a typology con-sisting of links, clicks, likes and traces (Cardon, 2013).2 The works of Osborne and Rose (1999) and LoïcBlondiaux (1998) develop this story extensively.4 The coupling of operational/academic consistedrather of Gallup-Cantrill on one side and Roper-Lazarsfeld on the other, but history has rememberedmainly Gallup and Lazarfeld. See Blondiaux (1998)on this topic.5 Pp. 222-235. Stating his thoughts at the end of thearticle, he writes: ‘Public opinion in this sense, im-plicitly admitted by those who carry out opinion pollsor those who use the results, I’m just saying that thisopinion does not exist’. Being the champion of socialstructure and its reproduction in long-term trends,Bourdieu could not allow room for other entities suchas public opinion, which is more cyclical, to exist in-dependently.

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Dominique Boullier was trained as a sociologist and a linguist in Paris, EHESS, in 1987, andsince 1981, his research has focused on information technologies. He created and managed acompany, two testbeds (user labs) and 4 academic labs, including the médialab at Sciences PoParis with Bruno Latour (2008-2015). In 2015, he joined the Digital Humanities Institute atthe Ecole Polytechnique Fédérale de Lausanne (Switzerland)[email protected]

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