Posts Tagged ‘ Digital platforms ’

Digital inequalities in time of pandemic

Just published a new, collective paper on new kinds of risk that are emerging with the COVID-19 virus, arguing that these risks are unequally distributed. Digital inequalities and social inequalities are rendering certain subgroups significantly more vulnerable to exposure to COVID-19. Populations bearing disproportionate risks include the social isolated, older adults, penal system subjects, digitally disadvantaged students, gig workers, and last-mile workers. We map out the intersection between COVID-19 risk factors and digital inequalities on each of these populations in order to examine how the digitally resourced have additional tools to mitigate some of the risks associated with the pandemic. We shed light on how the ongoing pandemic is deepening key axes of social differentiation, which were previously occluded from view.

These newly manifested forms of social differentiation can be conceived along several related dimensions. At their most general and abstract, these risks have to do with the capacity individuals have to control the risk of pathogen exposure. In order to fully manage exposure risk, individuals must control their physical environment to the greatest extent possible in order to prevent contact with potentially compromised physical spaces. In addition, they must control their social interactional environment to the greatest extent possible in order to minimize their contacts with potentially infected individuals. All else equal, those individuals who exercise more control over their exposure risk — on the basis of their control over their physical and social interactional environments — stand a better chance of staying healthy than those individuals who cannot manage exposure risk. Individuals therefore vary in terms of what we call their COVID-19 exposure risk profile (CERPs).

CERPs hinge on pre-existing forms of social differentiation such as socioeconomic status, as individuals with more economic resources at their disposal can better insulate themselves from exposure risk. Alongside socioeconomic status, one of the key forms of social differentiation connected with CERPs is digital (dis)advantage. Ceteris paribus, individuals who can more effectively digitize key parts of their lives enjoy better CERPs than individuals who cannot digitize these life realms. Therefore we believe that digital inequalities are directly and increasingly related to both life-or-death exposure to COVID-19, as well as excess deaths attributable to the larger conditions generated by the pandemic.

The article has been published in First Monday and is available here.

In the same special issue of First Monday, I co-published two reference articles:

Digital inequalities 2.0: Legacy inequalities in the information age

Digital inequalities 3.0: Emergent inequalities in the information age

Internship offer, TRIA project

I am currently seeking to hire a student intern for new research project TRIA (Les TRavailleurs de l’Intelligence Artificielle / Los TRabajadores de la Inteligencia Artificial). Start as soon as possible, conditional on evolving regulations at the end of the current lockdown. Max 6 months.

A full description of the project is enclosed (in French).

HDR Paola Tubaro

(English version below)

J’ai le plaisir d’annoncer la soutenance de mon habilitation à diriger des recherches en sociologie intitulée :

Décrypter la société des plateformes: Organisations, marchés et réseaux dans l’économie numérique.

Cette soutenance aura lieu le mercredi 11 décembre 2019 à Sciences Po Paris, 9 rue de la chaise, salle 931, à 10h00.

Si vous souhaitez venir, merci de confirmer votre présence grâce à ce lien car les personnes externes à Sciences Po ne pourront pas accéder à la salle si elles ne sont pas annoncées.


Le jury sera composé de :

  • M. Gilles Bastin, Professeur des universités, IEP de Grenoble (rapporteur)
  • M. Rodolphe Durand, Professeur, HEC Paris
  • M. Emmanuel Lazega, Professeur des universités, IEP de Paris (garant et rapporteur)
  • Mme Béatrice Milard, Professeure des universités, Université de Toulouse Jean Jaurès (rapporteure)
  • M. José Luís Molina González, Professeur, Universitat Autònoma de Barcelona
  • M. Tom A.B. Snijders, Professeur, Rijksuniversiteit Groningen

La soutenance sera suivie d’un pot. 

Résumé

Le manuscrit original conceptualise la récente montée en puissance des platesformes numériques selon trois dimensions principales : leur nature de dispositifs de coordination alimentés par les données, les transformations du travail qui en découlent, et les promesses d’innovation sociétale qui les accompagnent. L’ambition globale est de décortiquer le rôle de coordination de la plateforme et sa position à l’horizon de la dualité classique entreprise – marché. Il s’agit aussi de comprendre précisément comment elle utilise les données pour ce faire, où elle amène le travail, et comment elle gère des projets d’innovation sociale. Je prolonge cette analyse pour faire apparaître la continuité entre la société actuelle dominée par les plateformes et la « société organisationnelle », montrant que les plateformes sont des structures organisées qui distribuent les ressources, produisent des asymétries de richesse et de pouvoir, et repoussent l’innovation sociale vers la périphérie du système. Je discute des implications de ces tendances pour les politiques publiques, et propose des pistes pour la recherche future. 


I am pleased to announce the defense of my habilitation to direct research in sociology entitled:


Decoding the platform society: Organizations, markets and networks in the digital economy


This defense will take place on Wednesday, 11 December 2019 at Sciences Po Paris, 9 rue de la chaise, room 931, at 10am.


If you wish to attend, please confirm your presence through this link because people who are external to Sciences Po will be denied access to the room if they are not announced.

Members of the jury are:

  • Prof. Gilles Bastin, IEP de Grenoble (referee)
  • Prof. Rodolphe Durand, HEC Paris
  • Prof. Emmanuel Lazega, IEP de Paris (advisor and referee)
  • Prof. Béatrice Milard, Université de Toulouse Jean Jaurès (referee)
  • Prof. José Luís Molina González, Universitat Autònoma de Barcelona
  • Prof. Tom A.B. Snijders, Rijksuniversiteit Groningen

There will be drinks after the defense.


Abstract

The original manuscript conceptualizes the recent rise of digital platforms along three main dimensions: their nature of coordination devices fueled by data, the ensuing transformations of labor, and the accompanying promises of societal innovation. The overall ambition is to unpack the coordination role of the platform and where it stands in the horizon of the classical firm – market duality. It is also to precisely understand how it uses data to do so, where it drives labor, and how it accommodates socially innovative projects. I extend this analysis to show continuity between today’s society dominated by platforms and the “organizational society”, claiming that platforms are organized structures that distribute resources, produce asymmetries of wealth and power, and push social innovation to the periphery of the system. I discuss the policy implications of these tendencies and propose avenues for follow-up research.

New ANR Project HUSH: Human supply chain behind smart technologies

Together with sociologist Antonio A. Casilli and economist Ulrich Laitenberger, I have recently received ANR (French National Research Agency) funding for a new study of human inputs – mostly platform-mediated work in the production of artificial intelligence solutions. In our project called HUSH (Human supply chain behind smart technologies) we aim to shed light on the whole ecosystem linking platforms, workers and their clients demanding data-related and algorithmic services.

For this project, we are now looking for a

PhD researcher in digital economics

The position provides the opportunity to focus strongly on research, in a very active environment. The team has collaborations with different online platforms and has collected data sets from the web, which can be used by the applicant for their thesis. The focus of the current position is to work on the economic aspects of platform-mediated work, using quantitative analyses. Two other PhD students (in sociology) have already been recruited for this project and work on related topics.

The starting date is January 2020 (a later starting date is also possible). As per national regulations, the annual stipend will be about 1,600 euros per month, with possibility to obtain a complement for extra activities such as teaching. Social security and professional training are provided. Additional funding is available to present your research at international conferences and workshops. The position will be based at the new campus of Telecom Paris in Palaiseau, in the direct neighborhood of École Polytechnique and ENSAE.

Your profile

Applicants should have successfully completed a Master’s degree in economics, socio/economic data science or related disciplines, or expect completion at the beginning of the year 2020. They should have a strong interest in digital platforms, from the perspective of industrial organization or labor economics, and have an empirical focus (econometrics, data science). They should aim at developing programming skills and have an interest in the evaluation of internet data. Fluency in English is required; knowledge of French is advantageous, but not essential.

Telecom Paris and IP Paris

Telecom Paris is part of the newly founded Institute Polytechnique (IP) Paris, together with Ecole Polytechnique, ENSTA, ENSAE and Telecom Sud. The department of social sciences and economics (SES) at Telecom Paris studies the impact of the digitization on economic activity and society. For more information, please see https://www.telecom-paris.fr/fr/lecole/departements-enseignement-recherche/sciences-economiques-sociales/structure/economie-gestion

How to apply

Please submit a cover letter, a curriculum vitae, a transcript of records (listing all subjects taken and their grades), and contact details of one to two referees by November 15, 2019 to Ulrich Laitenberger ( laitenberger@enst.fr ).

Update: applications open until December 15, 2019.

Work, Employment & Society

BSA_WES2018Just came back from the Work, Employment and Society (WES) conference 2018, that British Sociological Association (BSA) organizes every other year. Perhaps more intimate and newbie-friendly than the main BSA event, this year’s WES in Belfast was also a positive surprise in terms of its academic content. There were several sessions on the so-called ‘gig economy’ (or as one speaker put it, ‘gig economies‘), the effects of digital business models that often go under the name of ‘uberization’, and atypical forms of work.

Some lessons I am taking home:

  • A growing number of researchers are studying platform work – not just the most visible forms of it such as Uber drivers and Deliveroo couriers, but also those who are hidden at home: freelancers and to a lesser extent, micro-workers;
  • The question of how platform workers self-organize, and what can be done to improve their organization capacity, is attracting a lot of attention;
  • Efforts at establishing standards, fairness criteria and forms of social protection for atypical platform workers are gaining momentum;
  • There is a lot we can learn from research in neighboring areas: for example the distinction between employee-friendly and employer-friendly flexible work, initially developed for people in employment, is also helpful here.

What is still missing from the picture is information on the ‘long tail’ of smaller, often national rather than international, platforms, and on the workers (especially micro-workers) who use them. Besides, clients and requesters are little known – on all platforms. Estimating the size of the platform worker population remains an unresolved issue – whether at local, national or international level. A common grievance by researchers is difficulty to access crucial data from commercial platforms that use them as their private property.

A cooperative approach to platforms

I was yesterday at a nice and interesting conference in Brussels on “How to coop the collaborative economy“, organized by major actors of the Belgian cooperative movement and building on the experience of a growing network of persons and organizations to enhance a cooperative view of the internet. Several themes in connection with my studies of the collaborative economy emerged, and I’d like to summarize here what were, in my view, the main lessons learned of the day.

Continue reading

Big data, big money: how companies thrive on informational resources

Information oils the economy – as we know since the path-breaking research of George Akerlof, Michael Spence and Joseph Stiglitz in the 1970s – and information can be extracted from data. Today, increased availability of “big” data creates the opportunity to access ever more information – for the good of the economy, then.

But in practice, how do companies extract value from this increasingly available information? In a nutshell, there are three ways in which they can do so: matching, targeted advertising, and market segmentation.

Matching is the key business idea of many recently-created companies and start-ups, and consists in helping potential parties to a transaction to find each other: driver and passenger (Uber), host and guest (Airbnb), buyer and seller (eBay), and so on. It is by processing users’ data with suitable algorithms that matching can be done, and the more detailed are the data, the more satisfactory the matching. Firms’ business model is usually based on taking a fee for each successful transaction (each realized match).

Targeted advertising is the practice of selecting, for each user, only the ads that correspond at best to their tastes or practices. Publicizing diapers to the general population will be largely ineffective as many people do not have young children; but targeting only those with young children is likely to produce better results. Here, the function of data is to help decide what to advertise to whom; useful data are people’s socio-demographic situation (age, marriage, children…), their current or past practices (if you bought diapers last week, you might do that again next week), and any declared tastes (for example as a post on Facebook or Twitter). How this produces a gain is obvious: if targeted adverts are more effective, sales will go up.

Continue reading

Twitter networks at the OuiShare Fest Barcelona 2016

Twitter conversations are one way through which participants in an event engage with the programme, comment and discuss about the talks they attend, prolong questions-and-answers sessions. Twitter feeds have become part of the official communication strategy of major events and serve documentation and information purposes, both for attendees and for outsiders. While tweeting is becoming more an more a prerogative of “official” accounts in charge of event communication, it is also a potential tool in the hands of each participant, allowing anyone to join the conversation at least in principe. Earlier, I have discussed how the Twitter discussion networks formed at the OuiShare Fest 2016, a major gathering of the collaborative economy community that took place last May in Paris, were one opportunity to see such mechanisms in place.

Here is a similar analysis, performed after the OuiShareFest Barcelona – the Spanish-language version of the event that I had the chance of attending last week. The size of this event is smaller than its Paris counterpart but nonetheless impressive: I mined 3497 tweets with the official hashtag of the event, #OSfestBCN, mostly written during the two days of the event (my count stopped the day after). Do Twitter #OSfestBCN conversations describe the community?

First, when did people tweet? As often happens, there are more tweets on the first than the second day of the event, and there are more tweets during the first hours of each day, though the difference between morning and afternoon is not dramatic; tweeting declines only at night, when the fest’s activities are suspended. Online activity is not independent of what happens on the ground – quite on the contrary, it follows the timings of physical activity.

osfestbcn_tweetsovertime_days12_plum

Who tweeted most? Obviously the official @OuiShare_es account, who published 630 tweets – nine times as many as the second in the ranking. Those who follow immediately are all individuals, who have between 50-70 tweets each.

Who tweeted with whom? What interests me most are conversations – who interacts with whom. The most explicit way of seeing this with Twitter data is to look at replies: who replied to whom. This corresponds to a small social network of 134 tweeters (the coloured points in the next Figure). Ties among them are represented as lines in the figure, and the size of points depends on the number of their incoming ties, that is, the number of replies received. Beyond the official @OuiShare_es account, several tweeters receive a lot of replies:  they are mostly speakers, track leaders, or otherwise important actors in the community.

replies

Now, who tweeted about whom? This is also an important aspect of Twitter conversations. We can capture it with the social network of mentions, associating each tweeter with those they mentioned, and counting the number of times they did so. This will be a larger network (with 2553 mentions) compared to the net of replies, as mentions can be of many types and also include retweets.

The below figure represents the network of mentions. As before, the colored points are tweeters (the larger they, the more often they have been mentioned by others), while lines between them are mentions (the thicker they are, the higher the number of times a user has mentioned another). Colors represent a measure called “modularity”, which identifies clusters of nodes whereby internal connections are stronger than the connections they have with nodes in other clusters; so for example, a purple node is more likely to have mentioned other purple nodes, than blue nodes.

Modularity is computed based only on counts of ties, without considering the nature of their conversations (what the mention is about) ou other qualities of nodes (gender, nationality, language of tweeters, etc.). And yet, it clearly identifies specific sub-communities. The very numerous, central purple nodes are the OuiShare community: connectors, activists, and others close to the organization especially within Spain. The green nodes at the bottom-left are the catalan community, including representatives of local authorities,notably the Barcelona municipality. The blue nodes at the bottom are different actors and groups from other parts of Spain. The few black nodes on the left are the international OuiShare community, and the sparse orange ones at the top are other international actors.

mentions22

This analysis is part of a larger research project, “Sharing Networks“, led by Antonio A. Casilli and myself, and dedicated to the study of the emergence of communities of values and interest at the OuiShare Fest 2016. Twitter networks will be combined with other data on networking – including informal networking which we are capturing through a (perhaps old-fashioned, but still useful!) survey.

The analyses and visualizations above were done with the package TwitteR in R as well as Gephi.

Are we all data laborers?

autonomyI gave today a talk at AUTONOMY, a major festival of urban mobility in Paris, where new technologies are at center stage, from driverless cars to electric scooters, bike-sharing solutions, and connected infrastructure for the smart city. I had been asked to talk about labor in digital platforms, such as those offering mobility services.

Digital platforms are often thought of in terms of automation, but it islogos clear that there is labor too: we all have in mind the example of the couriers and drivers of the “on-demand” economy. But there’s more: I’ll show how platforms involve the labor of everyone, including passengers and users of all types. By labor, I mean here human activity that produces data and information – the key source of value for platforms. It is often an implicit, invisible activity of which we may not even be aware – as we tend to focus more on consumption aspects as we talk routinely about “car pooling” or “car sharing”, rather than looking at the underlying productive effort. This is what scholars call “digital labor”.

Four eco-systems

Specialist Antonio Casilli distinguishes four forms of digital labor in platforms, and I am now going to briefly outline them.

Continue reading

Twitter networks at the OuiShare Fest 2016

Twitter conversations are one way through which participants in an event engage with the programme, comment and discuss about the talks they attend, prolong questions-and-answers sessions. Twitter feeds have become part of the official communication strategy of major events and serve documentation and information purposes, both for attendees and for outsiders. While tweeting is becoming more an more a prerogative of “official” accounts in charge of event communication, it is also a potential tool in the hands of each participant, allowing anyone to join the conversation at least in principe. Participants may become aware of each other, perhaps using the opportunity of the event to meet face-to-face, start relationships and even collaborations. A Nesta study insisted on the potential for using social media data to attain a quantitative understanding of events and their impacts on participants’ networks.

The OuiShare Fest 2016, a major gathering of the collaborative economy community that took place last week in Paris, was one opportunity to see such mechanisms in place. Tweeting was easy – with an official hashtag, #OSFEST16, although related hashtags were also widely used. I mined a total of 12440 tweets over the four days of the event. Do Twitter conversations related to the Fest bring to light the emergence of a community? While it’s too early for any deep analysis, some descriptive results can already be shown.

First, when did people tweet? Mostly at the beginning of each day’s programme (9am on the first two days, 2pm on the third day). Tweeting was more intense in the first day and declined over time (Figure 1). The comparatively low participation on the fourth day is due to the fact that the format was different – an open day in French (rather than an international conference in English), whereby local people were free to come and go. Online activity is not independent of what happens on the ground – quite on the contrary, it follows the timings of physical activity.

Tweets_Over_Time_Blog

Figure 1: Tweets over time.

Who tweeted most? Our dataset has a predictable outlier, the official @OuiShareFest Twitter account, who published 727 tweets – twice as many as the second in the ranking. But let’s look at the people who had no obligation to tweet, and still did so: who among them contributed most to documenting the Fest? Figure 2 shows the presence of some other institutional accounts among the top 10, but the most active include a few individual participants. Ironically, one of them was not even physically present at the Fest, and followed the live video streaming from home. In this sense, Twitter served as an interface between event participants and interested people who couldn’t make it to Paris.

OSFest16_Top10_NoOutliers

Figure 2: Ten most active tweeters (excluding @OuiShareFest).

What was the proportion of tweets, replies and retweets? Original tweets are interesting for their unique content (what are people talking about?), while replies and retweets are interesting because they reveal social interactions – dialogue, endorsement or criticism between users. Figure 3 shows that the number of replies is small compared to tweets and retweets.

Tweets_By_Type_blog

Figure 3: Tweets, replies and retweets

Let’s now look closer at the replies. By taking who replied to whom, we can build a social network of conversations between a group of tweeters. It’s a relatively small network of 311 tweeters (the coloured points in Figure 4), with 321 ties among them (the lines in Figure 4). The size of points depends on the number of their incoming ties, that is, the number of replies received: even if the points haven’t been labelled, I am sure you can tell immediately which one represents the official @OuiShareFest account… the usual suspect! But let’s look at the network structure more closely. Some ties are self-loops, that is, people replying to themselves. (Let’s be clear, it’s not a sign of social isolation, but simply a consequence of the 140-character limit imposed on Twitter: self-replies are meant to deliver longer messages). A lot of other participants are involved in just simple dyads or small chains (A replies to B who replies to C, but then C does not reply to A), unconnected to the rest. There is a larger cluster formed around the most replied-to users: here, some closure becomes apparent (A replies to B who replies to C who replies to A) and enables this sub-network to grow.

Network_replies_OSF16_blog

Figure 4: the network of replies.

Now, my own experience of tweeting at the Fest suggested that tweets were multilingual. Apart from the fourth day, there seemed to be a large number of French-speaking participants. A quick-and-dirty (for now) language detection exercise revealed that roughly 60% of tweets were in English, 25% in French, the rest being split between different languages especially German, Spanish, and Catalan. So, did people reply to each other based on the language of their tweets? It turns out that quite a few tweeters were involved in conversations in multiple languages. Figure 5 is a variant of Figure 4, colouring nodes and ties differently depending on language. A nice mix: interestingly, the central cluster is not monolingual and in fact, is kept together by a few, albeit small, multi-lingual tweeters.

Replies_by_language

Figure 5: the network of replies, by language.

Let’s turn now to mentions: who are the most mentioned tweeters? Again, I’ll take out of the analysis @OuiShareFest, hugely ahead of anyone else with 832 mentions received. Below, Figure 6 ranks the most mentioned: mostly companies (partners or sponsors of the event such as MAIF), speakers (such as Nathan Schneider, Nilofer Merchant), and key OuiShare personalities (such as Antonin Léonard). Mentions follow the programme of the event, and most mentioned are people and organizations that play a role in shaping it.

15MostMentioned_OSF16

Figure 6: Most mentioned tweeters.

Mentions are also a basis to build another social network – of who mentions whom in a tweet. This will be a larger network compared to the net of replies, as mentions can be of many types and also include retweets (which as we saw above, are very numerous here). There are 17248 mentions (some of which are repeated more than once) in the network. They involve 796 users who mention others and are mentioned in turn; 550 users who are mentioned, but do not mention themselves; and 1680 users who mention others, but are not themselves mentioned.

A large network such as this is more difficult to visualize meaningfully, and I had to introduce some simplifications to do so. I have included only pairs in which one had mentioned the other at least twice: this makes a network of 778 nodes with 2222 ties. The color of nodes depends on their modularity class (a group of nodes that are more connected with one another, than with any other nodes in the network) and their size depends on the number of mentions received. You will clearly recognize at the center of the network, the official @OuiShareFest account, which structures the bulk of the conversations. But even intuitively, other actors seem central as well, and their role deserves being examined more thoroghly (in some future, less preliminary analysis).

Mentions2

Figure 7: Network of Twitter mentions

This analysis is part of a larger research project, “Sharing Networks“, led by Antonio A. Casilli and myself, and dedicated to the study of the emergence of communities of values and interest at the OuiShare Fest 2016. Twitter networks will be combined with other data on networking – including informal networking which we are capturing through a (perhaps old-fashioned, but still useful!) survey.

The analyses and visualizations above were done with the packages TwitteR and igraph in R; Figure 7 was produced with Gephi.