Influence et contagion
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Identifying the brains behind a terrorist attack or an infectious-disease primary source | KurzweilAI

Identifying the brains behind a terrorist attack or an infectious-disease primary source | KurzweilAI | Influence et contagion | Scoop.it

Could a computer algorithm identify the source of a terrorism attack, like the Gauss virus, or an epidemic?

Pedro Pinto, a researcher at the Swiss Federal Institute of Technology in Lausanne (EPFL), says his team has developed such an algorithm.


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Influence et contagion
L'influence et la contagion dans la cyberculture
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Visualizing the spread of true and false news on social media | #SNA #contagion #FakeNews

Visualizing the spread of true and false news on social media | #SNA #contagion #FakeNews | Influence et contagion | Scoop.it

Cover stories offer a look at the process behind the art on the cover: who made it, how it got made, and why.

This cover (Fig. 1) depicts the breadth and depth of the spread of two different news stories through Twitter (see the Report by Vosoughi et al.). The larger orange object (or cascade) represents a false news story, whereas the teal one represents a true news story. I aimed to illustrate the stark differences in how broadly and deeply false news spreads compared with true news, which are reflected in the relative size and complexity of the cascades.

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Millions of People Post Comments on Federal Regulations. Many Are Fake | #fakenews #dataviz #contagion

Millions of People Post Comments on Federal Regulations. Many Are Fake | #fakenews #dataviz #contagion | Influence et contagion | Scoop.it
Dow Jones Digital Reprints & Licensing
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Parcourez la galaxie des fausses informations qui circulent sur Facebook | #FakeNews #Contagion #dataviz

Parcourez la galaxie des fausses informations qui circulent sur Facebook | #FakeNews #Contagion #dataviz | Influence et contagion | Scoop.it

Toutes ces fausses informations ont été diffusées par des pages Facebook publiques. Dans notre infographie, nous avons représenté ces pages par le symbole . Une fausse information peut être relayée par une ou plusieurs pages Facebook. Une page peut quant à elle en avoir diffusé plus d'une dizaine (au minimum trois dans notre corpus). Il devient alors possible de relier, sous la forme d'un réseau, les fausses informations à leur(s) diffuseur(s) et inversement. 


En savoir plus sur: 

http://www.lemonde.fr/les-decodeurs/visuel/2018/02/01/comment-les-fausses-informations-circulent-sur-facebook_5250516_4355770.html#CIfwGkbmTjUZRLAq.99

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Inside an AI 'brain' - What does machine learning look like?  | #SNA #MachineLearning #AI

Inside an AI 'brain' - What does machine learning look like?  | #SNA #MachineLearning #AI | Influence et contagion | Scoop.it

Graphcore Poplar software framework images of machine learning executed as a graph on the IPU Intelligent Processing Unit. Graph computing explained visually.


A graph is simply the best way to describe the models you create in a machine learning system. These computational graphs are made up of vertices (think neurons) for the compute elements, connected by edges (think synapses), which describe the communication paths between vertices.

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A Brief Survey of Journalistic Twitter #Bot Projects I #DDJ #activism #NSA

A Brief Survey of Journalistic Twitter #Bot Projects I #DDJ #activism #NSA | Influence et contagion | Scoop.it
In our collaborative op-ed for Sam Woolley’s provocateur-in-residence workshop at Data & Society, How to Think About Bots, we briefly surveyed the role bots can play in journalism. As we said in our…
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More Tweets, More Votes: Social Media as a #Quantitative Indicator of #Political Behavior - #Analytics #SNA

More Tweets, More Votes: Social Media as a #Quantitative Indicator of #Political Behavior - #Analytics #SNA | Influence et contagion | Scoop.it
Is social media a valid indicator of political behavior? There is considerable debate about the validity of data extracted from social media for studying offline behavior. To address this issue, we show that there is a statistically significant association between tweets that mention a candidate for the U.S. House of Representatives and his or her subsequent electoral performance. We demonstrate this result with an analysis of 542,969 tweets mentioning candidates selected from a random sample of 3,570,054,618, as well as Federal Election Commission data from 795 competitive races in the 2010 and 2012 U.S. congressional elections. This finding persists even when controlling for incumbency, district partisanship, media coverage of the race, time, and demographic variables such as the district's racial and gender composition. Our findings show that reliable data about political behavior can be extracted from social media.
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[1509.08295] Detecting global bridges in networks

[1509.08295] Detecting global bridges in networks | Influence et contagion | Scoop.it
The identification of nodes occupying important positions in a network structure is crucial for the understanding of the associated real-world system. Usually, betweenness centrality is used to evaluate a node capacity to connect different graph regions. However, we argue here that this measure is not adapted for that task, as it gives equal weight to "local" centers (i.e. nodes of high degree central to a single region) and to "global" bridges, which connect different communities. This distinction is important as the roles of such nodes are different in terms of the local and global organisation of the network structure. In this paper we propose a decomposition of betweenness centrality into two terms, one highlighting the local contributions and the other the global ones. We call the latter bridgeness centrality and show that it is capable to specifically spot out global bridges. In addition, we introduce an effective algorithmic implementation of this measure and demonstrate its capability to identify global bridges in air transportation and scientific collaboration networks.
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5 Questions for Duncan Watts | #SNA #influence

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Duncan J. Watts, principal researcher at Microsoft Research, is the 2014 winner of the Everett M. Rogers Award. The USC Annenberg Norman Lear Center got to sit down with him and ask him 5 questions about his talk "Social Influence in Markets & Networks: What's So Viral About Going "Viral"? 

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How Steve Jobs Connected It All: An Interactive Look at #Apple’s Technology #History | #influence #SNA

How Steve Jobs Connected It All: An Interactive Look at #Apple’s Technology #History | #influence #SNA | Influence et contagion | Scoop.it
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Data sources & methodology

 

The networks in this post are based on all Apple patents published between January 1978 and October 2014. To avoid duplicate entries based on applications for the same invention in multiple countries, only representatives of so-called patent families (groups of patents disclosing the same invention in multiple countries) are included. This leads to a total dataset of 9,663 patents listing 5,272 unique inventors over the 36 years analysed. Inventor names were deduplicated using a pattern-matching algorithm – any false negatives and positives were taken care of manually. This was done to avoid e.g. ‘Steve Jobs’ and ‘Stephen P Jobs’ appearing as two separate inventors. Inventors (circles) are connected in the networks whenever they appear as co-inventors on a patent; patents (squares) are connected to inventors whenever an inventor appears on that patent. Colours were assigned to patents and inventors based on the network cluster they reside in – clusters were identified using an algorithm which groups nodes when they are densely connected internally, but sparsely connected to other groups.

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Determinants of #Meme Popularity | #influence #twitter

Determinants of #Meme Popularity | #influence  #twitter | Influence et contagion | Scoop.it

Online social media have greatly affected the way in which we communicate with each other. However, little is known about what are the fundamental mechanisms driving dynamical information flow in online social systems. Here, we introduce a generative model for online sharing behavior and analytically show, using techniques from mathematical population genetics, that competition between memes for the limited resource of user attention leads to a type of self-organized criticality, with heavy-tailed distributions of meme popularity: a few memes "go viral" but the majority become only moderately popular. The time-dependent solutions of the model are shown to fit empirical micro-blogging data on hashtag usage, and to predict novel scaling features of the data. The presented framework, in contrast to purely empirical studies or simulation-based models, clearly distinguishes the roles of two distinct factors affecting meme popularity: the memory time of users and the connectivity structure of the social network.

 

Determinants of Meme Popularity
James P. Gleeson, Kevin P. O'Sullivan, Raquel A. Baños, Yamir Moreno

http://arxiv.org/abs/1501.05956


Via Complexity Digest
luiy's insight:

In summary, despite its simplicity, the model matches the empirical popularity distribution of hashtags on Twitter remarkably well; this is consistent with random-copying models of human decision-making [28] where the quality of the product—here, the “interestingness” of the meme—is less important than the social influence of peers’ decisions[29]. The generalization of the model (as shown in the SM) to incorporate (i) heterogeneous user activity rates and (ii) a joint distribution p jk of the number of users followed j and the number of followers k, remains analytically tractable and confirms the robustness of our main finding: that competition between memes for the limited resource of user attention induces criticality in the vanis hing-innovation limit, giving power-law popularity distributions and epochs of linear-in-time popularity growth. We believe that theanalytical results and potential for fast fitting to data will render this a useful null model for further investigations of the entangled effects of memory, network structure, and competition on information spread through social networks [30].

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Links that speak: The global #language network and its association with global fame | #SNA #DH

Links that speak: The global #language network and its association with global fame | #SNA #DH | Influence et contagion | Scoop.it
luiy's insight:
Languages vary enormously in global importance because ofhistorical, demographic, political, and technological forces. How-ever, beyond simple measures of population and economic power,there has been no rigorous quantitative way to define the globalinfluence of languages. Here we use the structure of the networksconnecting multilingual speakers and translated texts, as expressedin book translations, multiple language editions of Wikipedia, andTwitter, to provide a concept of language importance that goesbeyond simple economic or demographic measures. We find thatthe structure of these three global language networks (GLNs)is centered on English as a global hub and around a handfulof intermediate hub languages, which include Spanish, German,French, Russian, Portuguese, and Chinese. We validate the mea-sure of a language’s centrality in the three GLNs by showing that itexhibits a strong correlation with two independent measures ofthe number of famous people born in the countries associatedwith that language. These results suggest that the position ofa language in the GLN contributes to the visibility of its speakersand the global popularity of the cultural content they produce.
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GALLERY SocioPatterns project. SocioPatterns.org | #SNA #datascience

GALLERY SocioPatterns project. SocioPatterns.org | #SNA #datascience | Influence et contagion | Scoop.it
A gallery that offers a collection of visualizations, pictures, movies and other media created and/or recorded in the context of the SocioPatterns project.
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Dynamical Contact Patterns in a Primary School

 

This movie represents the dynamical contacts network measured during one day of activity in a primary school. Nodes represent individuals, and edges indicate face-to-face contacts. Every frame shows the contact network over a time window of 20 minutes. Nodes are arranged in groups that correspond to the school classes, with the teacher node at the center. Nodes are color-coded according to the grade  and teachers are shown in black. This movie is included in the supplementary information of our PLoS ONE paper. The network visualization was created by Alain Barrat and André Panisson using Gephi. The cumulative social network of interaction is available from the corresponding dataset page.

 

- See more at: http://www.sociopatterns.org/gallery/#sthash.ae3X56fs.dpuf

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The Evolution of Beliefs over Signed Social Networks | #SNA #influence

The Evolution of Beliefs over Signed Social Networks | #SNA #influence | Influence et contagion | Scoop.it
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We study the evolution of opinions (or beliefs) over a social network modeled as a signed graph. The sign attached to an edge in this graph characterizes whether the corresponding individuals or end nodes are friends (positive links) or enemies (negative links). Pairs of nodes are randomly selected to interact over time, and when two nodes interact, each of them updates its opinion based on the opinion of the other node and the sign of the corresponding link. This model generalizes DeGroot model to account for negative links: when two enemies interact, their opinions go in opposite directions. We provide conditions for convergence and divergence in expectation, in mean-square, and in almost sure sense, and exhibit phase transition phenomena for these notions of convergence depending on the parameters of the opinion update model and on the structure of the underlying graph. We establish a {\it no-survivor} theorem, stating that the difference in opinions of any two nodes diverges whenever opinions in the network diverge as a whole. We also prove a {\it live-or-die} lemma, indicating that almost surely, the opinions either converge to an agreement or diverge. Finally, we extend our analysis to cases where opinions have hard lower and upper limits. In these cases, we study when and how opinions may become asymptotically clustered to the belief boundaries, and highlight the crucial influence of (strong or weak) structural balance of the underlying network on this clustering phenomenon.

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Quantitative analysis of population-scale family trees with millions of relatives | #SNA #GenealogicalData

Quantitative analysis of population-scale family trees with millions of relatives | #SNA #GenealogicalData | Influence et contagion | Scoop.it
Family trees have vast applications in multiple fields from genetics to anthropology and economics. However, the collection of extended family trees is tedious and usually relies on resources with limited geographical scope and complex data usage restrictions. Here, we collected 86 million profiles from publicly-available online data shared by genealogy enthusiasts. After extensive cleaning and validation, we obtained population-scale family trees, including a single pedigree of 13 million individuals. We leveraged the data to partition the genetic architecture of longevity by inspecting millions of relative pairs and to provide insights into the geographical dispersion of families. We also report a simple digital procedure to overlay other datasets with our resource in order to empower studies with population-scale genealogical data.
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Computational social science: Making the links.

Computational social science: Making the links. | Influence et contagion | Scoop.it

From e-mails to social networks, the digital traces left by life in the modern world are transforming social science.


Infectious ideas In some instances, big data have showed that long-standing ideas are wrong. This year, Kleinberg and his colleagues used data from the roughly 900 million users of Facebook to study contagion in social networks — a process that describes the spread of ideas such as fads, political opinions, new technologies and financial decisions. Almost all theories had assumed that the process mirrors viral contagion: the chance of a person adopting a new idea increases with the number of believers to which he or she is exposed.

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Personal networks typologies: A structural approach | #SNA #Methodology

Personal networks typologies: A structural approach | #SNA #Methodology | Influence et contagion | Scoop.it
Building typologies allows to compare networks on multiple dimensions, and to approach a generalization grounded on empirical data. In this article, we present a typology of personal networks only based on indicators related to the structure of relations between alters. It is designed from very detailed data on young French people who were involved in a longitudinal study. Our typology mobilizes a small number of indicators to discriminate the types that compose it. In so doing, we intend to make it applicable to various surveys.
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Yochai Benkler: The Right-Wing Media Ecosystem - Shorenstein Center

Yochai Benkler: The Right-Wing Media Ecosystem - Shorenstein Center | Influence et contagion | Scoop.it
April 5, 2017—Yochai Benkler, professor at Harvard Law School and co-director of the Berkman Klein Center for Internet and Society at Harvard, discussed his recent study on conservative media and the 2016 election, which analyzed more than 1.25 millio
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Rise of the #Peñabots – Data & Society: Points l #bots #influence #EPN

Rise of the #Peñabots – Data & Society: Points l #bots #influence #EPN | Influence et contagion | Scoop.it
Shortly after the start of his campaign during the 2012 Mexican presidential election, then-candidate Enrique Peña Nieto (EPN) rose to Twitter dominance almost overnight. To some savvy Internet…
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OSoMe: Research Highlights - #fakenews #SNA #elections

OSoMe: Research Highlights - #fakenews #SNA #elections | Influence et contagion | Scoop.it
Why study fake news and digital misinformation


After the 2016 US elections, the topic of fake news and their spread on social media has become a hotly debated issue. As our group has been studying this phenomenon since 2010, our work has been covered and quoted in the media, analyzing the influence of social bots, the appearance of fake news in Facebook trends, vote suppression attempts, the magnitude of the problem, the potential of fake news in social media to sway elections, online advertising as incentives for fake news, the effectiveness of advertising bans, the steps taken by Facebook, the future of fake news, and the real consequences of conspiracy theories. Our editorial article in The Conversation has been republished widely, including by Time, Scientific American, and PBS. It is good that the problem of digital misinformation is getting the attention it deserves. Research investments are needed toward a deeper understanding of the phenomenon as well as toward socio-technical countermeasure to help mitigate the deceptive manipulation of opinions, without infringing on the free flow of information.
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Handbook of Graph Drawing and #Visualization | #freebook #SNA

Handbook of Graph Drawing and #Visualization | #freebook #SNA | Influence et contagion | Scoop.it
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FeaturesPresents the topological and geometric foundations of graph drawingDescribes many graph drawing algorithms and software systems, including the GDToolkit, OGDF, and PIGALECovers various applications of graph drawing in biological networks, computer security, data analytics, education, computer networks, and social networks

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Computer-based #personality judgments are more accurate than those made by humans | #social-cognitive

Computer-based #personality judgments are more accurate than those made by humans | #social-cognitive | Influence et contagion | Scoop.it

This study compares the accuracy of personality judgment a ubiquitous and important social-cognitive activity between computer models and humans. Using several criteria, we show that computers judgments of people's personalities based on their digital footprints are more accurate and valid than judgments made by their close others or acquaintances (friends, family, spouse, colleagues, etc.). Our findings highlight that people’s personalities can be predicted automatically andwithout involving human social-cognitive skills.

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Structural #Patterns of the Occupy Movement on Facebook | #socialchange #SNA

Structural #Patterns of the Occupy Movement on Facebook | #socialchange #SNA | Influence et contagion | Scoop.it

In this work we study a peculiar example of social organization on Facebook: the Occupy Movement -- i.e., an international protest movement against social and economic inequality organized online at a city level. We consider 179 US Facebook public pages during the time period between September 2011 and February 2013. The dataset includes 618K active users and 753K posts that received about 5.2M likes and 1.1M comments. By labeling user according to their interaction patterns on pages -- e.g., a user is considered to be polarized if she has at least the 95% of her likes on a specific page -- we find that activities are not locally coordinated by geographically close pages, but are driven by pages linked to major US cities that act as hubs within the various groups. Such a pattern is verified even by extracting the backbone structure -- i.e., filtering statistically relevant weight heterogeneities -- for both the pages-reshares and the pages-common users networks.

 

Structural Patterns of the Occupy Movement on Facebook
Michela Del Vicario, Qian Zhang, Alessandro Bessi, Fabiana Zollo, Antonio Scala, Guido Caldarelli, Walter Quattrociocchi

http://arxiv.org/abs/1501.07203


Via Complexity Digest
luiy's insight:
Data Description The dataset represents a complete screenshot of the Occupy Movement in the period immediately following the outbreak of the protest on September 17th, 2011 in the Zuccotti Park of New York. The dataset covers all the posts until the end of February 2013, at the time when all the major protests were no more active. After the Zuccotti occupation, in fact, an October full of similar occupational events followed, leading to an international protest movement that extended itself until the end of 2012, when the movement was principally an online collective protest.
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How #Network Science Is Changing Our Understanding of #Law | #SNA #patterns

How #Network Science Is Changing Our Understanding of #Law | #SNA #patterns | Influence et contagion | Scoop.it
The first network analysis of the entire body of European Community legislation reveals the pattern of links between laws and their resilience to change.

Via Complexity Digest
luiy's insight:

One of the more fascinating areas of science that has emerged in recent years is the study of networks and their application to everyday life. It turns out that many important properties of our world are governed by networks with very specific properties.

 

These networks are not random by any means. Instead, they are often connected in the now famous small world pattern in which any part of the network can be reached in a relatively small number of steps. These kinds of networks lie behind many natural phenomena such as earthquakes, epidemics and forest fires and are equally ubiquitous in social phenomena such as the spread of fashions, languages, and even wars.

 

So it should come as no surprise that the same kind of network should exist in the legal world. Today, Marios Koniaris and pals at the National Technical University of Athens in Greece show that the network of links between laws follows exactly the same pattern. They say their network approach provides a unique insight into the nature of the law, the way it has emerged and how changes may influence it in the future.

 

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Dynamical Contact #Patterns in a Primary School | #SNA #learning

luiy's insight:

This movie represents the dynamical evolution of the contacts during the first day of a deployment of the SocioPatterns sensing platform, see sociopatterns.org. Each dot represents an individual, and an edge is drawn when a contact between two individuals occurs. Only contacts lasting at least 40 s are retained. Each frame corresponds to an aggregation of the contact network over a time window of 20 mn, and successive frames correspond to aggregation time windows shifted by 10 s; the movie is then built using 20 frames per second. Nodes are disposed in circles corresponding to the various classes, with the teacher at the center, and color-coded according to the grade (teachers are shown in black).

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The Divination Network of Tarot | #SNA

The Divination Network of Tarot | #SNA | Influence et contagion | Scoop.it
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Tarot is a deck of cards used since the 15th century to play various games as well as for divination purposes. We at Nodus Labs studied the structure of various Tarot decks, treating the cards as the nodes and relations between them as edges, building a graph of relations between the cards that are invariant across various Tarot decks. We discovered that the structure of the resulting graph has a very specific community structure, which makes Tarot a very efficient tool for telling narratives. We are currently working on practical implementations of this study.

 

This research was performed by Dmitry Paranyushkin and Colin Johnco.

 

http://noduslabs.com/cases/divination-network-tarot/

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