Influence et contagion
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Influence et contagion
L'influence et la contagion dans la cyberculture
Curated by luiy
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Selection Effects in Online #Sharing: Consequences for Peer Adoption | #contagion

luiy's insight:

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 define 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 effects in online sharing through a large-scale field experiment on Facebook that randomizes whether or not adopters share Offers (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 effect that exposes peers who are more likely to adopt than the population exposed under passive sharing. 
We decompose the selection effect 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 effect. The study's findings are among the first to address downstream peer effects induced by online sharing mechanisms, and can inform design in settings where a surplus of sharing could be viewed as costly.

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¿#Guerra civil en Michoacán? | #mexico,, la plaza en lucha por el narco-gobierno, #derechoshumanos #offline

¿#Guerra civil en Michoacán? | #mexico,, la plaza en lucha por el narco-gobierno, #derechoshumanos #offline | Influence et contagion | Scoop.it
Ningún funcionario se atreve a decirlo. No hay gobierno municipal, estatal o federal que se decida a llamar a las cosas por su nombre. Pero lo que sucede en Mi
luiy's insight:

Ningún funcionario se atreve a decirlo. No hay gobierno municipal, estatal o federal que se decida a llamar a las cosas por su nombre. Pero lo que sucede en Michoacán se parece mucho a una guerra civil. Le pueden llamar equis. Pueden pedir suspensión de garantías individuales o disolver los poderes como solicitó el Partido Acción Nacional. Pueden mandar miles de soldados y marinos; de policías federales, estatales y municipales. Pueden militarizar al 100 por ciento el estado, pero lo que sucede en Michoacán se parece a una guerra civil. Y si el gobierno de Enrique Peña Nieto no quiere verlo, si quiere invisibilizarlo, si pretende que miremos a otro lado, comete un grave error. El hecho de ignorar o tapar un problema no lo elimina.

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The Dark Corners of the Internet | #SNA #dataviz

The Dark Corners of the Internet | #SNA #dataviz | Influence et contagion | Scoop.it
The way information spreads through society has been the focus of intense study in recent years. This work has thrown up…
luiy's insight:

The way information spreads through society has been the focus of intense study in recent years. This work has thrown up some dramatic results; it explains why some ideas become viral while others do not, why certain individuals are more influential than others and how best to exploit the properties of a network to spread information most effectively.

 

But today, Chuang Liu at Hangzhou Normal University in China and a few pals have a surprise. They say that when information spreads, there are always blind spots in a network that never receive it. And these unreachable dark corners of the network can be numerous and sizeable.

 

Until now theorists have predicted that information can always spread until it saturates a network to the point where everybody has received it. These predictions are come from models based on our understanding of diseases and the way they percolate through a population. The basic assumption is that information spreads in the same way.

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Marco Valli's curator insight, January 11, 2014 6:36 AM

A different view on information spread and diffusion on a network. A simple model, accounting for the key difference between "viruses" and "information", both from the sender and the receiver point of view.

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Distinguishing #influence - based #contagion from #homophily - driven #diffusion in dynamic networks | #dataviz

luiy's insight:

Node characteristics and behaviors are often correlated with the structure of social networks over time. While evidence of this type of assortative mixing and temporal clustering of behaviors among linked nodes is used to support claims of peer influence and social contagion in networks, homophily may also explain such evidence. Here we develop a dynamic matched sample estimation framework to distinguish influence and homophily effects in dynamic networks, and we apply this framework to a global instant messaging network of 27.4 million users, using data on the day-by-day adoption of a mobile service application and users' longitudinal behavioral, demographic, and geographic data. We find that previous methods overestimate peer influence in product adoption decisions in this network by 300–700%, and that homophily explains >50% of the perceived behavioral contagion. These findings and methods are essential to both our understanding of the mechanisms that drive contagions in networks and our knowledge of how to propagate or combat them in domains as diverse as epidemiology, marketing, development economics, and public health.

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#Contagion of Cooperation in Static and Fluid Social Networks

Cooperation is essential for successful human societies. Thus, understanding how cooperative and selfish behaviors spread from person to person is a topic of theoretical and practical importance. Previous laboratory experiments provide clear evidence of social contagion in the domain of cooperation, both in fixed networks and in randomly shuffled networks, but leave open the possibility of asymmetries in the spread of cooperative and selfish behaviors. Additionally, many real human interaction structures are dynamic: we often have control over whom we interact with. Dynamic networks may differ importantly in the goals and strategic considerations they promote, and thus the question of how cooperative and selfish behaviors spread in dynamic networks remains open. Here, we address these questions with data from a social dilemma laboratory experiment. We measure the contagion of both cooperative and selfish behavior over time across three different network structures that vary in the extent to which they afford individuals control over their network ties. We find that in relatively fixed networks, both cooperative and selfish behaviors are contagious. In contrast, in more dynamic networks, selfish behavior is contagious, but cooperative behavior is not: subjects are fairly likely to switch to cooperation regardless of the behavior of their neighbors. We hypothesize that this insensitivity to the behavior of neighbors in dynamic networks is the result of subjects’ desire to attract new cooperative partners: even if many of one’s current neighbors are defectors, it may still make sense to switch to cooperation. We further hypothesize that selfishness remains contagious in dynamic networks because of the well-documented willingness of cooperators to retaliate against selfishness, even when doing so is costly. These results shed light on the contagion of cooperative behavior in fixed and fluid networks, and have implications for influence-based interventions aiming at increasing cooperative behavior.

 

Jordan JJ, Rand DG, Arbesman S, Fowler JH, Christakis NA (2013) Contagion of Cooperation in Static and Fluid Social Networks. PLoS ONE 8(6): e66199. http://dx.doi.org/10.1371/journal.pone.0066199


Via Complexity Digest, A. J. Alvarez-Socorro
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wintrotech's curator insight, September 21, 2013 7:40 AM

the new domain hasing and domain selection is always help in good domain rankinh.

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Les bases neurologiques des “mèmes” : comment les idées deviennent-elles contagieuses ? | #contagio #viralidad

Les bases neurologiques des “mèmes” : comment les idées deviennent-elles contagieuses ? | #contagio #viralidad | Influence et contagion | Scoop.it
InternetActu.net est un site d'actualité consacré aux enjeux de l'internet, aux usages innovants qu'il permet et aux recherches qui en découlent.
luiy's insight:

Comment les idées deviennent-elles contagieuses ? La thèse comparant certaines idées à des “virus du cerveau” ne date pas d’hier. Dans son livre Le Gène égoïste, paru en 1976, Richard Dawkins avait créé la notion de mèmes, analogues “mentaux” des gènes, qui étaient capables de s’autorépliquer d’un cerveau à l’autre, et qui, à l’instar des créatures vivantes, cherchaient avant tout à maximiser leur capacité de reproduction. Par la suite, certains avaient essayé de donner corps à une nouvelle science, la mémétique, se basant sur cette notion. L’idée n’a jamais vraiment pris, et peu de chercheurs (à l’exception peut être du philosophe Daniel Dennett et de l’anthropologue des religions Pascal Boyer) ont vraiment continué à travailler sur ces bases. En 2005, le Journal of Memetics fermait définitivement ses portes après huit années d’existence.

 

En revanche, les mèmes sont devenus un élément constitutif de la pop culture internet. Restait cependant à savoir si cette contagion des idées possède de véritables bases neurales ou si elle n’est rien d’autre… qu’un mème.

 

Des recherches effectuées à l’UCLA, sous direction du psychologue Matthew Lieberman, donnent aujourd’hui à penser qu’il y aurait une réalité biologique à l’oeuvre dans ce processus de “contamination” intellectuelle. Des chercheurs ont en effet étudié les mécanismes cérébraux impliqués dans le “buzz”.

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Il est temps de changer de regard sur les influenceurs ! | #influence

Il est temps de changer de regard sur les influenceurs ! | #influence | Influence et contagion | Scoop.it

Via Dominique Cardon
luiy's insight:

On a plus que jamais besoin des influenceurs

La publicité en ligne est le moteur économique du web. Pourtant, la faiblesse de l'attention qui lui est accordée conjuguée à la perte de confiance dans les messages promotionnels obligent à repenser la communication en ligne en faveur d'une logique d'influence. Plus de 'earned media' et moins de "paid media". Et pour cause, si beaucoup ne regardent ou ne considèrent pas la publicité, de plus en plus d'internautes s'équipent pour ne pas y être exposés du tout. Selon une étude récente publiée par FairPage, plus de 20 % des internautes utiliseraient AdBlock ou une solution équivalente. 

 

Repenser la place des "top influenceurs"

Cependant, si la publicité n'a cessé d'évoluer, les pratiques en matière de relations influenceurs (ou IRM pour Influencer Relationship Management) sont restées les mêmes depuis plusieurs années. C'est particulièrement vrai pour le ciblage : la nature RP de la discipline encourage à privilégier une sélection des personnes les plus influentes possible selon des critères plus ou moins pertinents. Toucher les leaders d'opinion pour maximiser la résonance en ligne.

La vérité, c'est que cela marche de moins en moins bien. Les stars de la blogosphère sont devenues plus exigeantes que les journalistes des titres les plus lus et prestigieux ; la visibilité attendue reste moindre qu'un passage télé ou un bel article papier et la qualité de la couverture est inégale. Si cette approche conserve une certaine pertinence, elle ne constitue qu'une partie de la réponse.

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Mapping #influencers in healthcare | #SNA #dataviz

Mapping #influencers in healthcare | #SNA #dataviz | Influence et contagion | Scoop.it
Harnessing influence is vital for effective healthcare marketing, but robust, reliable methods of understanding its nature and identifying those who have it are needed

Via Emmanuel Capitaine
luiy's insight:

Mapping healthcare influencers online
The network-mapping approach finds its natural home, of course, online, and it is also possible to map the communities that gather around health topics on platforms such as Twitter. 

 

Physician networks tend to reflect real life – publication communities will tend to mirror brick-and-mortar communities such as hospitals – and influencers identified by the literature will probably also hold high-status positions at these institutions. But life online is much more frictionless. In Twitter, we see that communities tend to form around topics and opinions, with factors like geography, offline status and stage presence holding less weight.

 

For this reason, influence on Twitter is mostly fuelled by the content an individual shares – whether that's defined as the attitudes he or she has, the language he or she uses, or the links he or she shares. Because of this, identifying influencers on Twitter can give us really important insights into what content we should create, and how. These insights ensure our content is likely to be useful to – and shared by – the community we are interested in. They can also, of course, help us understand how to become influential ourselves.

 

It's easy to see why many of the tools that claim to identify influencers are so well used. They deliver quick, apparently useful, results, and it's only when you dig a little deeper or try to act on the insights that you see how little foundation their recommendations have. But influence is something it's worth taking the time to understand. We believe that robust, quantitative network analytics will revolutionise healthcare marketing in the same way it has revolutionised search engines, social media, and online shopping.

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Is Klout the Online Standard of Vanity? | #influence

Is Klout the Online Standard of Vanity? | #influence | Influence et contagion | Scoop.it
Image I’ve been doing a lot of thinking about the weight of Klout scores. With the gospel of influencer marketing pollinating the media, it’s hard not to.

Via Ron Sela
luiy's insight:

I’ve been doing a lot of thinking about the weight of Klout scores. With the gospel of influencer marketing pollinating the media, it’s hard not to. Like stocks and credit scores, it’s easy to get caught up in the rise and fall of where you stand in relation to others. But is there more to it that makes our Klout monitoring so addicting?

 

Many a marketing folk will tell you there’s no point in engaging with someone with a Klout score lower than 50. I’m writing today to tell you that’s b-u-l-l. For two reasons:

 

1) Many a great influencer was born offline.

2) Many a Klout master are just vain social media junkies with nothing of real value to add to the conversation.

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Ron Sela's curator insight, August 7, 2013 5:17 AM

Is Klout the Online Standard of Vanity?

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The Golden Age of Consumer #Influence | Lean Back 2.0

The Golden Age of Consumer #Influence | Lean Back 2.0 | Influence et contagion | Scoop.it
Social, Four Gears Model, Monetization, Dr. Michael Wu, Lithium Technologies, Social Media
luiy's insight:

The basic idea is this: in order to drive a viral growth—a lasting influx of increasingly loyal customers—your strategy needs four gears moving together with positive feedback as follows:

 

 

Acquisition—a mechanism that enables you to attract and capture new consumers

 

Engagement—a process that nurtures prospects and customers, and cultivates long term loyalty

 

Enlistment—an environment that enables your customers to participate in the business in a whole new way—helping you to acquire new customers, support other customers and generate new product ideas

 

Monetization—a seamless integration to your monetization engines (e.g. your e-commerce and CRM systems)  that helps you convert, deliver, satisfy and up-sell

 

A sustainable social strategy needs all four gears moving in concert to get that all-important viral loop spinning persistently over the long-term.

Done right, you get social customers who participate, help you to create trusted content, buy more, are willing to pay a premium for your products, stay loyal longer and recruit other customers.

Leading social brands demonstrate every day that solid attention to all four gears leads to increased sales, higher margins and lasting competitive advantage.

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How #Algorithms Rule The World | #pagerank #influencers

How #Algorithms Rule The World | #pagerank #influencers | Influence et contagion | Scoop.it

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.

 

Continue Reading... 

luiy's insight:

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.

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Influence Measuring: Klout vs. PeerIndex vs. Kred | #influence #dataviz

Influence Measuring: Klout vs. PeerIndex vs. Kred | #influence #dataviz | Influence et contagion | Scoop.it
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.

Via Ron Sela, Pascale Mousset
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Ron Sela's curator insight, June 27, 2013 3:01 AM

Influence Measuring: Klout vs. PeerIndex vs. Kred

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#ANAMIA EGOCENTER : Three #tools to visualize #personal networks | #dataviz #ethnography

#ANAMIA EGOCENTER : Three #tools to visualize #personal networks | #dataviz #ethnography | Influence et contagion | Scoop.it
Data visualization techniques are enjoying ever greater popularity, notably thank to the recent boom of Big Data and our increased capacity to handle large datasets. Network data visualization tech...
luiy's insight:

Three tools to visualize personal networks

Data visualization techniques are enjoying ever greater popularity, notably thank to the recent boom of Big Data and our increased capacity to handle large datasets. Network data visualization techniques are no exception. in fact, appealing diagrams of social connections (sociograms) have been at the heart of the field of social network analysis since the 1930s, and have contributed a lot to its success. Today, all this is evolving at unprecedented pace.

In line with these tendencies, the research team of the project ANAMIA (a study of the networks and online sociability of persons with eating disorders, funded by the French ANR) of which I was one of the investigators, have developed new software tools for the visualization of personal network data, with different solutions for the three stages of data collection, analysis, and dissemination of results.

 

Specifically:

- ANAMIA EGOCENTER is a graphical version of a name generator, to be embedded in a computer-based survey to collect personal network data. It has turned out to be a user-friendly, highly effective interface for interacting and engaging with survey respondents;

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Mapping the Spread of Viruses / #Contagions via Contact Tracing | #SNA

Mapping the Spread of Viruses / #Contagions via Contact Tracing | #SNA | Influence et contagion | Scoop.it
The Spread of a Contagion through a Human Network
luiy's insight:

Mapping the Spread of #Contagions via Contact Tracing

A contagion passed by human contact, such as SARS or TB, spreads through human networks based on how infectious and susceptible each party is. Multiple contacts with infectious others play a role in the probability of infection. Contagions that flow through human-based networks can be bad(disease, gossip), good(ideas and information) or neutral(money and investments).

 

Public health officials perform contact tracing to map the spread of the infection and manage its diffusion. The network map above, created at the epidemiology unit of The Centers for Disease Control [CDC], shows the spread of an airborne infectious disease. The map was created using actual contact data from the community in which the outbreak was happening.

 

Black nodes are persons with clinical disease (and are potentially infectious), pink nodes represent exposed persons with incubating (or dormant) infection and are not infectious, green represent exposed persons with no infection and are notinfectious. The infection status is unknown for the grey nodes.

 

Unfortunately the 'social butterfly' in this community, the black node in the center of the graph, is also the most infectious -- a super spreader.

 

Current procedures focus on inoculating the vulnerable -- often the very young and the very old. Network analysis tells us that it may be smarter, and more efficient, to focus on the spreaders -- those with many contacts to many groups.

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The Simple Rules of Social #Contagion | #behaviors

It is commonly believed that information spreads between individuals like a pathogen, with each exposure by an informed friend potentially resulting in a naive individual becoming infected. However, empirical studies of social media suggest that individual response to repeated exposure to information is significantly more complex than the prediction of the pathogen model. As a proxy for intervention experiments, we compare user responses to multiple exposures on two different social media sites, Twitter and Digg. We show that the position of the exposing messages on the user-interface strongly affects social contagion. Accounting for this visibility significantly simplifies the dynamics of social contagion. The likelihood an individual will spread information increases monotonically with exposure, while explicit feedback about how many friends have previously spread it increases the likelihood of a response. We apply our model to real-time forecasting of user behavior.

 

The Simple Rules of Social Contagion
Nathan O. Hodas, Kristina Lerman

http://arxiv.org/abs/1308.5015


Via Complexity Digest, Shaolin Tan, ukituki
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António F Fonseca's curator insight, December 23, 2013 7:12 AM

Another paper about information propagation. A study on the user interface of two social sites, mainly the problem of limited attention and attention managment.

Claude Emond's curator insight, September 23, 2014 3:52 PM

A contagious feel-good-ness :)

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The Ripple Effect: Emotional #Contagion and its #Influence on Group Behavior

luiy's insight:

Group emotional contagion, the transfer of moods among people in a group, and its influence on work group dynamics was examined in a laboratory study of managerial decision making using multiple, convergent measures of mood, individual attitudes, behavior, and group-level dynamics. Using a 2 times 2 experimental design, with a trained confederate enacting mood conditions, the predicted effect of emotional contagion was found among group members, using both outside coders' ratings of participants' mood and participants' self-reported mood. No hypothesized differences in contagion effects due to the degree of pleasantness of the mood expressed and the energy level with which it was conveyed were found. There was a significant influence of emotional contagion on individual-level attitudes and group processes. As predicted, the positive emotional contagion group members experienced improved cooperation, decreased conflict, and increased perceived task performance. Theoretical implications and practical ramifications of emotional contagion in groups and organizations are discussed.

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What Facebook and Twitter Reveal About #Contagion | #health

What Facebook and Twitter Reveal About #Contagion | #health | Influence et contagion | Scoop.it
All that chatter on social media may be more valuable than we think, say researchers who are mining the postings for clues about how to best control infectious disease.
luiy's insight:

What people share on social media can sometimes predict the spread of ideas about diseases like the flu, for example, or beliefs about vaccinations. The researchers looked at the social media reactions to issues like childhood immunizations, acceptance of quarantine during the SARS outbreak and public health messages related to infections like influenza.

 

“If highly connected nodes in the social network (such as celebrities) suggest that the vaccine carries risks, the resulting perception of vaccine risks can propagate quickly through the social network, fueling a vaccine scare and a drop in vaccine coverage,” they write. Cough cough, Jenny McCarthy. But such social connectivity can also help to prevent biological contagion through imitated or culturally promoted behaviors, like covering your mouth when you cough. And control of the SARS virus was largely possible because the general public was accepting of the quarantines.



Read more: http://healthland.time.com/2013/10/03/what-facebook-and-twitter-reveal-about-contagion/#ixzz2gnWcPCFi

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Creating buzz: The neural correlates of effective message propagation | #influence #neuroimaging

luiy's insight:

Social interaction promotes the spread of values, attitudes, and behaviors. Here we report on neural responsesto ideas that are destined to spread. Message communicators were scanned using fMRI during their initial exposure to the to-be-communicated ideas. These message communicators then had the opportunity to spread the messages and their corresponding subjective evaluations to message recipients, outside the scanner. Successful ideas were associated with neural responses in the mentalizing system and the reward system when first heard, prior to spreading them. Similarly, individuals more able to spread their own views to others produced greater mentalizing system activity during initial encoding. Unlike prior social influence studiesthat focus on those being influenced, this investigation focused on the brains of influencers. Successful social influence is reliably associated with an influencer-tobe’sstate of mind when first encoding ideas.

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Data Mining, Machine Learning and Business Intelligence | #datamining

Data mining


Via Yves Mulkers
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Yves Mulkers's curator insight, September 8, 2013 6:17 AM

Quick overview of what is about and where to find which tools (commercial and opensource)

Fàtima Galan's curator insight, September 12, 2013 6:43 AM

Clarifying concepts!! great summary

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#COOLHUNTING FOR THE WORLD’S THOUGHT LEADERS | #SNA

luiy's insight:

Which thinkers are we guided by? A novel “Thought Leader Map” shows the select group of people with real influence who are setting the trends in the market for ideas. The influencers in philosophy, sociology, economics, and the “hard sciences” have been identified by a Delphi process, asking 50 thought leaders to name their peers. The importance of the influencers is calculated by constructing a cooccurrence network in the Blogosphere. Our main insight is that the era of the great authorities seems to be over. Major thought leaders are rare – the picture is composed of many specialists.

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I Flirt and Tweet. Follow Me at #Socialbot

I Flirt and Tweet. Follow Me at #Socialbot | Influence et contagion | Scoop.it
Socialbots are being designed to sway elections, to influence the stock market, even to flirt with people and one another.

Via Ron Sela
luiy's insight:

FROM the earliest days of the Internet, robotic programs, or bots, have been trying to pass themselves off as human. Chatbots greet users when they enter an online chat room, for example, or kick them out when they get obnoxious. More insidiously, spambots indiscriminately churn out e-mails advertising miracle stocks and unattended bank accounts in Nigeria. Bimbots deploy photos of gorgeous women to hawk work-from-home job ploys and illegal pharmaceuticals.

 

Now come socialbots. These automated charlatans are programmed to tweet and retweet. They have quirks, life histories and the gift of gab.

 

Many of them have built-in databases of current events, so they can piece together phrases that seem relevant to their target audience. They have sleep-wake cycles so their fakery is more convincing, making them less prone to repetitive patterns that flag them as mere programs. Some have even been souped up by so-called persona management software, which makes them seem more real by adding matching Facebook, Reddit or Foursquare accounts, giving them an online footprint over time as they amass friends and like-minded followers.

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Ron Sela's curator insight, August 13, 2013 7:01 AM

I Flirt and Tweet. Follow Me at Socialbot.

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Influencia - Tendances - Sharing Economy : l'économie du monde moderne ?

Influencia - Tendances - Sharing Economy : l'économie du monde moderne ? | Influence et contagion | Scoop.it
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.
luiy's insight:

« 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 »…

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Pattern-detection and Twitter’s Streaming API - Strata | #dataviz #influence

Pattern-detection and Twitter’s Streaming API - Strata | #dataviz #influence | Influence et contagion | Scoop.it
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...
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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”

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How to Find, Follow and Connect with Social Media Influencers | #influence #networkawareness

How to Find, Follow and Connect with Social Media Influencers | #influence #networkawareness | Influence et contagion | Scoop.it
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...

Via Ron Sela
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How To Track Influencers With Google+ Ripples

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.

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Kenneth Mikkelsen's comment, June 27, 2013 10:11 AM
Great post. Thank you for sharing, Luis.