Avec 3.5 milliards de revenus selon Forbes, l'économie du partage s'installe tranquillement dans nos habitudes. Effet positif de la Crise, ce nouveau commerce n'a pas fini de faire parler de lui.
« Nous assistons à un mouvement de fond qui n’est pas prêt de s’arrêter ! » estime Loïc Le Meur. Et il n’est pas le seul à le penser! Depuis 5 ans, cette nouvelle idéologie économique basée sur l’entraide sans pour autant oublier l’aspect financier est en train de faire évoluer nos échanges commerciaux.
Un changement qui peut mener dans les prochaines années à un véritable bouleversement de l’économie mondiale et des mœurs au travers de nouveaux modes de consommation. « On ne peut éviter la Sharing Economy ! Elle est dans tous les médias et il s’agit non pas d’une mode mais d’un mouvement. Un mouvement optimiste qui va générer des millions d’emplois dans le monde et même devenir un vrai contre-pouvoir », précise le fondateur de la conférence internationale LeWeb.
MAIS QUI SONT CES ACTEURS ?
Certains sont désormais mondialement connu comme Airbnb, un des fers de lance de la consommation collaborative qui met en relation des consommateurs et des propriétaires immobiliers désireux de louer leurs biens en direct sans passer par un acteur de la location. Un service qui en seulement 5 ans a déjà conquis 195 pays, environ 30 000 villes et pas loin des 6 millions de nuits réservées. Et tout ça depuis 2008, année de sa création par Brian Cheski aujourd’hui milliardaire et peut-être un philanthrope en puissance. « L’Europe est actuellement le marché à la plus forte croissance pour Airbnb et cela permet de situer l’intérêt pour cette nouvelle race d’entreprises sur notre continent », ajoute Loïc Le Meur.
Un autre exemple de Startup basée sur l’économie collaborative est Lending Club qui a pour mission le prêt à taux bas. Elle met en relation des créanciers comme vous et moi désireux d’aider leurs semblables à mener à bien un projet sans passer par une banque. Un investisseur fait en moyenne une marge de 8% sur la somme qu’il a prêtée. Une entreprise qui a vu le géant Google investir 163 millions de dollars pour aider son développement. «Tous les secteurs économique sont désormais ciblés par cette nouvelle économie. Que ce soit la market place Etsy véritable lien entre acheteurs et revendeurs de produits divers et variés ou encore Zipcar un autre géant en devenir spécialisé dans la location de véhicules pour particulier ou professionnels »…
Researchers and companies who need social media data frequently turn to Twitter's API to access a random sample of tweets. Those who can afford to pay (or have been...
Systematic comparison of the Streaming API and the Firehose
A recent paper from ASU and CMU compared data from the streaming API and the firehose, and found mixed results. Let me highlight two cases addressed in the paper: identifying popular hashtags and influential users.
Of interest to many users is the list of top hashtags. Can one identify the “top n” hastags using data made available throughthe streaming API? The graph below is a comparison of the streaming API to the firehose: n(as in “top n” hashtags) vs. correlation (Kendall’s Tau). The researchers found that the streaming API provides a good list of hashtags when n is large, but is misleading for small n.
Another area of interest is identifying influential users. The study found that one can identify a majority of the most important users just from data available through the streaming API. More precisely1, the researchers could identify anywhere from “50–60% of the top 100 key-players when creating the networks based on one day of Streaming API data”
How to Find, Follow and Connect with Social Media Influencers WordStream (blog) In short, you want to be able to connect with influencers—people in your industry who are well respected, widely published, speak at important conventions, have a...
One of the lesser known ways to connect with influencers is through Google+ Ripples. The great thing about Ripples is the idea that you can see not just who is sharing your SEO content, but who your specific influencers really are and how to connect with them. You can see who shared your content and gave that content good visibility. That person is absolutely a social media influencer for you, so he/she is someone you should want to meet.
Intense scientific debate is going around the definition of the foundational concepts and appropriate methodological approaches to deal with the understanding of social dynamics. These challenges are aiming to understand human behavior in its complexity driven by intentional (and not necessarily rational) decisions and influenced by a multitude of factors. The functioning of communication-based mechanisms requires individuals to interact in order to acquire information to cope with uncertainty and thus deeply rely on the accuracy and on the completeness of information (if any). In fact, people’s perceptions, knowledge, beliefs and opinions about the world and its evolution, get (in)formed and modulated through the information they can access. Moreover their response is not linear as individuals can react by accepting, refusing, or elaborating (and changing) the received information.
Technology-mediated social collectives are taking an important role in the design of social structures. Yet our understanding of the complex mechanisms governing networks and collective behaviour is still quite shallow. Fundamental concepts like authority, leader-follower dynamics, conflict or collaboration in online networks are still not well defined and investigated – but they are crucial to illuminate the advantages and pitfalls of this form of collective decision-making (which can cancel out individual mistakes, but also make them spiral out of control).
The aim of this satellite is to address the question of ICT mediated social phenomena emerging in multiple scales ranging from the interactions of individuals to the emergence of self-organized global movements. We would like to gather researchers from different disciplines to form a forum to discuss ideas, research questions, recent results, and future challenges in this emerging area of research and public interest.
Particular attention will be devoted to the following topics:
Interdependent social contagion processPeer production and mass collaborationTemporally evolving networks and stream analyticsCognitive aspects of belief formation and revisionOnline communication and information diffusionViral propagation in online social networkCrowd-sourcing: herding behaviour vs. wisdom of crowdsE-democracy and online government-citizen interactionOnline socio-political mobilizationsPublic attention and popularity
All the participants of the satellite meeting (with or without abstract submission) must register for the European Conference on Complex Systems 2013.
The deadline for abstract submission is 30 June 2013.
How does network structure affect diffusion? Recent studies suggest that the answer depends on the type of contagion. Complex contagions, unlike infectious diseases (simple contagions), are affected by social reinforcement and homophily. Hence, the spread within highly clustered communities is enhanced, while diffusion across communities is hampered. A common hypothesis is that memes and behaviors are complex contagions. We show that, while most memes indeed behave like complex contagions, a few viral memes spread across many communities, like diseases. We demonstrate that the future popularity of a meme can be predicted by quantifying its early spreading pattern in terms of community concentration. The more communities a meme permeates, the more viral it is. We present a practical method to translate data about community structure into predictive knowledge about what information will spread widely. This connection may lead to significant advances in computational social science, social media analytics, and marketing applications.
For one thing, recent research has challenged the traditional understanding of how social contagion works. As network researcher Duncan Watts has put it, the contagious spread of influence “depends far more on the overall structure of the network than on the properties of the individuals who trigger it.” In other words, contagion happens because people are asking for it not because tellers are pushing for it. A breakout phenomenon satisfies what people are asking for, and once people are asking for it, anybody, not just highly influential tellers, can trigger a contagious outbreak of social influence.
Many companies and organizations use influencers to help them reach out to people. Curators are an interesting breed of influencers. However their full potential can only be understood and appreciated through the lens of their tribes. Tribes are groups of people gathering around strong passions or emotions like hiking, gardening or ABBA. Curators do not work in isolation, but in relation to the people that share their passions.
Curators as dumpster divers
I was in a discussion with Olga Kravets, a netnographer, and she proposed that curators serve their tribe like dumpster divers. They dive into containers to rummage through heaps of garbage to find useful stuff that can be re-purposed. When they are done they bring forth their scavenged gifts to their tribe.
Something really interesting happens in the curation process, because stories don’t have intrinsic value. An unshared story is basically like rubbish, lying around without any value. Stories gain their meaning and value by sharing, but it’s not as simple as that. The curator imparts her own value, status and trust, upon the story.
Curators represent a new type of tribal leadership that operates bottom-up and peer to peer. As a member of a tribe, curators will always be more native and relevant than any outsiders will ever be. Within a tribe they are not only appreciated for leveraging their insider skills, but for sustaining and developing their culture.
There’s a great assumption that the future of technology falls in the hands of emergent generations.
The youth of today will someday represent the majority of consumers, employees and citizens. That’s always the case, but what we don’t yet fully appreciate is just how different young adults think today. We don’t yet understand what it is they value and why. We’ve not yet assimilated how they make decisions and what factors influence their daily activities and journeys.
Homophily (i.e., "love of the same") is the tendency of individuals to associate and bond with similar others. The presence of homophily has been discovered in a vast array of network studies. More than 100 studies that have observed homophily in some form or another and they establish that similarity breeds connection. These include age, gender, class, and organizational role. This is often expressed in the adage "birds of a feather flock together". Individuals in homophilic relationships share common characteristics (beliefs, values, education, etc.) that make communication and relationship formation easier. Homophily often leads to homogamy—marriage between people with similar characteristics.
Homophily, the tendency to interact with others of similar type, is widely observed in nature. Sex- and age-related homophily, for example, shapes the formation of clusters of preferred companionships in zebras1, dolphins2, and predicts both the quantity and quality of many primate interactions3, 4. Meerkats tend to assortatively associate with other group members of similar attributes in dominance and foraging networks5. And across many dimensions of phenotypes, humans exhibit high levels of homophily in social tie formation6, 7, 8. In fact, recent evidence suggests that humans may even exhibit genotypic homophily, meaning that individuals with a certain genotype are more likely to be friends with others of the same genotype9. Heterophily, the tendency to interact with others of different type, also exists in nature at both the cellular10, 11, 12 and organismic levels. For example, research on collaboration networks suggests that people are likely to form heterophilic task-related ties with those who are complementary to their own skill sets8. Analogously, hunter-gatherer life is characterised by long-term imbalances in productivity and consumption, and by the division of labour13; hence, one might possibly expect that social interactions would, at least in part, be heterophilic, offering complementary advantages to interacting parties; but they are not7.
We study the diffusion of an idea, a product, a disease, a cultural fad, or a technology among agents in a social network that exhibits segregation or homophily (the tendency of agents to associate with others similar to themselves).
Much of the study of how social network structure impacts diﬀusion has focusedon the distribution of degrees.21 Here, we have addressed the eﬀect of homophilyon whether or not diﬀusion takes place, something which despite its importance has received little attention in the diﬀusion literature.22 As a ﬁrst step to understanding the eﬀect of homophily on diﬀusion, in this paper we focused on a speciﬁc question; namely the spreading of a new behavior when starting with a small initial seed. The main insight from our analysis is that homophily can facilitate infection or contagion, by allowing an infection to get a toehold in one population and subsequently spread. If infection would already spread in each population in isolation, or in none of the populations in isolation, then there can be no eﬀect.
The interesting case is then when some populations would harbor infection in isolation and others would not. In this situation, homophily plays an important role. If populations interact with each other suﬃciently, then the population that might harbor infection on its own might never reach an endemic state since they meet each other too rarely for infection to get started. However, when there is suﬃcient homophily, but still some interaction across groups, then infection can get started within one group and then transfer to others. Just as an example, schools can be strong incubators for a variety of diseases which then can spread beyond. Our results thus provide a variety of testable hypotheses relating whether diﬀusion occurs to proclivities of various groups for infection and relative interaction rates across groups.
There are other issues that are left for further work. For example, one could evaluate the extent of diﬀusion as a function of the homophily level. Homophily can have conﬂicting eﬀects: although it can facilitate an initial diﬀusion, the ultimate eﬀect on the diﬀusion can go either way
In his new book, Aisle50 cofounder Christopher Steiner counts the (many, many) ways digits have come to dominate. "If you look at who has the biggest opportunity in society right now," he says, "it’s developers."
When Christopher Steiner, the 35-year-old cofounder of Aisle50, a Y Combinator startup offering online grocery deals, set out to write the book Automate This: How Algorithms Came to Rule Our World, (out tomorrow) he’d planned to focus solely on Wall Street. “There were a ton of good stories and then the Flash Crash happened. There was a lot to tell,” says Steiner. “But at some point I thought ‘Do people really care about the 13 different electronic training networks that were going on in the 1990’s?’” Instead the former technology journalist expanded his research to explore how the power of algorithms has spread far beyond Wall Street and now touches all of us--starting with today’s young innovators.
I was intrigued by your discussion of Jon Kleinberg, a Cornell computer science professor who devised an algorithm to identify the influencers in a given organization.
He was the guy who came up with the original method that Google eventually used to create their PageRank algorithm. His newest algorithm ranks people and their place in society by how they affect others through language. For example, if, in any given group there’s one guy who influences the others more strongly than anyone else, he tends to be the leader. This can be measured quantitatively. The schematic of how this works looks just like the schematic of how web pages are ranked. Whoever is linked and has more power over all of these trusted sites is who ends up at the top of the Google rankings. Same for people.
It's about Influence and about Connections. What I was keen on discovering, was what service / insights I would be deriving from using the services. Read my blogpost to see why I preferred Kred (Measuring Influence: Kred vs.
Something strange happened in Iceland at RIMC. About half way through his talk on networks, Matt Roberts from Linkdex analysed the ‘network’ of speakers at the conference to identify ‘who he should take for lunch’ i.e. who was the most influential speaker who he wasn’t already connected to. To my surprise, I was that person.
By strange coincidence, Matt and I were sat opposite at the speaker’s dinner. After catching my first glimpse of the Northern Lights and being initiated to Icelandic ‘Black Death‘, I (probably not so soberly) asked Matt whether we could analyse the whole music business community as a network using Linkdex.
"Overall, we show striking temporal variation in network structure and traits that predict association patterns in a wild chimpanzee community. These empirically-derived networks can inform dynamic models of pathogen transmission and have practical applications for infectious disease management of endangered wildlife species."
SummaryHeterogeneity in host association patterns can alter pathogen transmission and strategies for control. Great apes are highly social and endangered animals that have experienced substantial population declines from directly transmitted pathogens; as such, network approaches to quantify contact heterogeneity could be crucially important for predicting infection probability and outbreak size following pathogen introduction, especially owing to challenges in collecting real-time infection data for endangered wildlife.We present here the first study using network analysis to quantify contact heterogeneity in wild apes, with applications for predicting community-wide infectious disease risk. Specifically, within a wild chimpanzee community, we ask how associations between individuals vary over time, and we identify traits of highly connected individuals that might contribute disproportionately to pathogen spread.We used field observations of behavioural encounters in a habituated wild chimpanzee community in Kibale National Park, Uganda to construct monthly party level (i.e. subgroup) and close-contact (i.e. ≤5 m) association networks over a 9-month period.Network analysis revealed that networks were highly dynamic over time. In particular, oestrous events significantly increased pairwise party associations, suggesting that community-wide disease outbreaks should be more likely to occur when many females are in oestrus.Bayesian models and permutation tests identified traits of chimpanzees that were highly connected within the network. Individuals with large families (i.e. mothers and their juveniles) that range in the core of the community territory and to a lesser extent high-ranking males were central to association networks, and thus represent the most important individuals to target for disease intervention strategies.Overall, we show striking temporal variation in network structure and traits that predict association patterns in a wild chimpanzee community. These empirically-derived networks can inform dynamic models of pathogen transmission and have practical applications for infectious disease management of endangered wildlife species.
L’affrontement économique s’impose aujourd’hui comme la forme décisive de confrontation des intérêts de puissance. Or, les victoires sur le théâtre des opérations commerciales et financières, ainsi que dans le domaine de la définition des normes de l’échange (qui avantagent certains acteurs ou handicapent d’autres), s’obtiennent aujourd’hui à travers des stratégies et opérations d’influence complexes et aux conséquences protéiformes redoutables pour ceux qui ont négligé d’y recourir.
Force est de constater que l’on est loin ici de la forme d’influence que l’époque réclame et dont certains analystes expliquent parfaitement la rationalité interne et la nécessité contemporaine, tel Philippe Ratte. Ce dernier expose avec raison que la puissance bien comprise, et durable, résulterait aujourd’hui d’un équilibre combinatoire, c’est-à-dire d’une interaction harmonieuse entre le « dedans » et le « dehors », entre l’identité d’un groupe (qui est elle-même un système en évolution) et le mouvement du monde. Si une communauté se révèle capable d’entrer intelligemment en synergie avec le « système monde » du moment, elle sera en mesure d’imprimer sa marque à ce dernier, de l’infléchir dans une certaine proportion, et d’engager ainsi une spirale positive, une dynamique de progrès (politique, socio-économique, technologique, etc.), pour elle-même bien entendu mais aussi pour les autres nations. Dès lors, le phénomène de mondialisation deviendrait une sorte de relais pour une telle société. Bien sûr, cette logique de résonance, d’influence mutuelle bénéfique, ne se conçoit que dans un cadre politique libéral et se situe aux antipodes des formes de pouvoir autoritaires et conquérantes des siècles écoulés. Ce qui revient à affirmer que la condition contemporaine de la puissance aurait basculé de la capacité à employer la force physique (ou à faire peser la menace de son exercice) à l’aptitude à améliorer le « fonctionnement » général de la collectivité, ce qui exige de rester à l’écoute du reste du monde et de s’appuyer avec ingéniosité sur ses métamorphoses. Par conséquent, il ne s’agirait plus de menacer ou de contraindre autrui systématiquement mais de « prendre de l’ascendant par son propre mouvement », d’investir dans son propre développement (passant par l’amélioration du système éducatif, la garantie de la liberté des personnes et de la sécurité des biens, ainsi que par le règne du droit) plutôt que d’essayer de compromettre celui de l’autre. Sur la scène internationale, une telle dynamique favoriserait à l’évidence les logiques coopératives et refoule les jeux conflictuels.
This is a guest post by David Gerster (@gerster), a data scientist and investor in BigML. I work at a consumer web company, and recently used BigML to understand what drives return visits to our si...
I work at a consumer web company, and recently used BigML to understand what drives return visits to our site. I followed Standard Operating Procedure for data mining, sampling a group of users, dividing them into two classes, and creating several features that I hoped would be useful in predicting these classes. I then fed this training data to BigML, which quickly and obediently produced a decision tree...
Most models of social contagion take peer exposure to be a corollary of adoption, yet in many settings, the visibility of one's adoption behavior happens through a separate decision process. In online systems, product designers can de ne how peer exposure mechanisms work: adoption behaviors can be shared in a passive, automatic fashion, or occur through explicit, active sharing. The consequences of these mechanisms are of substantial practical and theoretical interest: passive sharing may increase total peer exposure but active sharing may expose higher quality products to peers who are more likely to adopt.
We examine selection e fects in online sharing through a large-scale eld experiment on Facebook that randomizes whether or not adopters share O ers (coupons) in a passive manner. We derive and estimate a joint discrete choice model of adopters' sharing decisions and their peers' adoption decisions. Our results show that active sharing enables a selection e ect that exposes peers who are more likely to adopt than the population exposed under passive sharing. We decompose the selection e fect into two distinct mechanisms: active sharers expose peers to higher quality products, and the peers they share with are more likely to adopt independently of product quality. Simulation results show that the user-level mechanism comprises the bulk of the selection e ect. The study's fi ndings are among the first to address downstream peer ef ects induced by online sharing mechanisms, and can inform design in settings where a surplus of sharing could be viewed as costly.
See this post for an explanation of what this means, and the last section of my paper with Andrew Thomas (link below) for the technical/methodological questions which most interest me in this area. Query: to what extent do the same problems show up when looking at other sorts of networks, say of neurons, or of gene regulatory elements? Recommended:...
Whenever I give a lecture, I conduct a simple experiment. First I ask the members of the audience to raise a hand if they follow the actor Ashton Kutcher on Twitter. Usually most people’s hands go up—no big surprise. For several years Kutcher has been aggressively amassing followers, even renting billboards urging people to follow “aplusk,” his Twitter handle. In 2009 he became the first user to acquire 10 million followers; by early 2013 the total was 13.7 million. Kutcher would seem the very definition of a social media “influencer.” But then I ask the audience another question: How many have ever done something because Kutcher suggested it? Most often nobody raises a hand. So I have to wonder: If Kutcher is the quintessential influencer but no one does what he suggests, in what way is he influential?
One of the fundamental principles driving diversity or homogeneity in domains such as cultural differentiation, political affiliation, and product adoption is the tension between two forces: influence (the tendency of people to become similar to others they interact with) and selection (the tendency to be affected most by the behavior of others who are already similar). Influence tends to promote homogeneity within a society, while selection frequently causes fragmentation. When both forces are in effect simultaneously, it becomes an interesting question to analyze which societal outcomes should be expected.
In order to study the joint effects of these forces more formally, we analyze a natural model built upon active lines of work in political opinion formation, cultural diversity, and language evolution. Our model posits an arbitrary graph structure describing which "types" of people can influence one another: this captures effects based on the fact that people are only influenced by sufficiently similar interaction partners. In a generalization of the model, we introduce another graph structure describing which types of people even so much as come in contact with each other. These restrictions on interaction patterns can significantly alter the dynamics of the process at the population level.
For the basic version of the model, in which all individuals come in contact with all others, we achieve an essentially complete characterization of (stable) equilibrium outcomes and prove convergence from all starting states. For the other extreme case, in which individuals only come in contact with others who have the potential to influence them, the underlying process is significantly more complicated; nevertheless we present an analysis for certain graph structures.