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antropologiaNet, dataviz, collective intelligence, algorithms, social learning, social change, digital humanities
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Crowdfunding Platform for Science Research Grants

Crowdfunding Platform for Science Research Grants | e-Xploration | Scoop.it
Grow the next generation of ideas

Via Wildcat2030
luiy's insight:

Microryza is a crowdfunding platform for research. Individuals pool their money until the funding goal is reached.

 

You'll get to see experiments unfold with updates and progress from the lab and have a chance to interact directly with the researchers.

 

At the end of the project, you will receive access to the results in a beautiful, online format.

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'Networked minds' require fundamentally new kind of economics

'Networked minds' require fundamentally new kind of economics | e-Xploration | Scoop.it
In their computer simulations of human evolution, scientists have discovered the emergence of the “homo socialis” with “other-regarding” preferences.

Via Viktor Markowski
luiy's insight:

Evolution of “friendliness”


Prof. Dirk Helbing of ETH Zurich, who coordinated the study, adds: “Compared to conventional models for the evolution of social cooperation, we have distinguished between the actual behavior – cooperation or not – and an inherited character trait, describing the degree of other-regarding preferences, which we call the friendliness.” The actual behavior considers not only the own advantage (“payoff”), but also gives a weight to the payoff of the interaction partners depending on the individual friendliness. For the “homo economicus”, the weight is zero. The friendliness spreads from one generation to the next according to natural selection. This is merely based on the own payoff, but mutations happen.

For most parameter combinations, the model predicts the evolution of a payoff-maximizing “homo economicus” with selfish preferences, as assumed by a great share of the economic literature. Very surprisingly, however, biological selection may create a “homo socialis” with other-regarding preferences, namely if offsprings tend to stay close to their parents. In such a case, clusters of friendly people, who are “conditionally cooperative”, may evolve over time.

If an unconditionally cooperative individual is born by chance, it may be exploited by everyone and not leave any offspring. However, if born in a favorable, conditionally cooperative environment, it may trigger cascade-like transitions to cooperative behavior, such that other-regarding behavior pays off. Consequently, a “homo socialis” spreads.

 

 

Networked minds create a cooperative human species


“This has fundamental implications for the way, economic theories should look like,” underlines Professor Helbing. Most of today’s economic knowledge is for the “homo economicus”, but people wonder whether that theory really applies. A comparable body of work for the “homo socialis” still needs to be written.

While the “homo economicus” optimizes its utility independently, the “homo socialis” puts himself or herself into the shoes of others to consider their interests as well,” explains Grund, and Helbing adds: “This establishes something like “networked minds”. Everyone’s decisions depend on the preferences of others.” This becomes even more important in our networked world.

 

 

A participatory kind of economy


How will this change our economy? Today, many customers doubt that they get the best service by people who are driven by their own profits and bonuses. “Our theory predicts that the level of other-regarding preferences is distributed broadly, from selfish to altruistic. Academic education in economics has largely promoted the selfish type. Perhaps, our economic thinking needs to fundamentally change, and our economy should be run by different kinds of people,” suggests Grund. “The true capitalist has other-regarding preferences,” adds Helbing, “as the “homo socialis” earns much more payoff.” This is, because the “homo socialis” manages to overcome the downwards spiral that tends to drive the “homo economicus” towards tragedies of the commons. The breakdown of trust and cooperation in the financial markets back in 2008 might be seen as good example.

“Social media will promote a new kind of participatory economy, in which competition goes hand in hand with cooperation,” believes Helbing. Indeed, the digital economy’s paradigm of the “prosumer” states that the Internet, social platforms, 3D printers and other developments will enable the co-producing consumer. “It will be hard to tell who is consumer and who is producer”, says Christian Waloszek. “You might be both at the same time, and this creates a much more cooperative perspective.”

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Viktor Markowski's curator insight, March 25, 2013 3:49 PM

Economics has a beautiful body of theory. But does it describe real markets? Doubts have come up not only in the wake of the financial crisis, since financial crashes should not occur according to the then established theories. Since ages, economic theory is based on concepts such as efficient markets and the “homo economicus”, i.e. the assumption of competitively optimizing individuals and firms. It was believed that any behavior deviating from this would create disadvantages and, hence, be eliminated by natural selection. But experimental evidence from behavioral economics show that, on average, people behave more fairness-oriented and other-regarding than expected. A new theory by scientists from ETH Zurich now explains why. 

Onearth's curator insight, March 26, 2013 4:58 AM

After homo sapiens sapiens it's time for homo sapiens socialis

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Max Planck Research Networks

Max Planck Research Networks | e-Xploration | Scoop.it
luiy's insight:

This multi-touch installation reveals how Max Planck Institutescollaborate with each other, and with their international partners. 

For this visualization, we analysed data from SciVerse Scopus for over 94,000 publications over the last ten years. A dynamic network provides a high-level map of the Max Planck Institutes and their connections. The size of the institute icons represents the number of scientific publications, and the width of the connecting lines the number of jointly published papers between two institutes.

 

The map of Max Planck institutes on the right shows their respective locations, whereas the world map on the bottom shows the locations of external collaboration partners.

 

Touching an institute icon on the multitouch screen centers the view around it and highlights its most important collaboration partners, both in the network as well as on the maps. Visitors can move and zoom all views by touching and ‘pinching’ (moving two fingers together or apart). The international flow of ideas is represented metaphorically by streams of energy particles, being continuously exchanged between the institutions.

 

The application is on display at the Max Planck Science Gallery, a highly interactive exhibition space presenting new forms of science communication in Berlin.

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Visualize any Text as a Network - Textexture

Visualize any Text as a Network - Textexture | e-Xploration | Scoop.it
luiy's insight:

Welcome to Textexture. Using this tool you can visualize any text as a network. The resulting graph can be used to get a quick visual summary of the text, read the most relevant excerpts (by clicking on the nodes), and find similar texts.

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Visualizing Databases | Digital Humanities Specialist

Visualizing Databases | Digital Humanities Specialist | e-Xploration | Scoop.it

Summaries and statistics drawn from within the structure of the database are not enough. If there is to be any real grappling with the database as an culturally-embedded construct, then it has to be done in a manner that reveals the data, the model and the population simultaneously.


Via Lauren Moss
luiy's insight:

I’ve become quite the fan of Gephi, lately, and received a good-natured challenge by one of my colleagues, which went something like, “Why is a everything a network with you, now?”  Obviously, in the case of social network-like phenomena, such as mapping collaboration in the Digital Humanities with the DH@Stanford graph–network theory and network language (whether visual or theoretical) make sense.  Network analytical tools like Gephi are also only a short step away from spatial analytical tools, like ArcGIS, many of which are used to ask questions about geographic networks and not about the kind of continuous data found in topography.

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Parsing the humanities | University Affairs

Parsing the humanities | University Affairs | e-Xploration | Scoop.it
Everything you wanted to know about digital humanities.

Via Pierre Levy
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Pierre Levy's curator insight, March 15, 2013 1:45 PM

A canadian point of view...

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Big Data and a Renewed Debate Over Privacy

Big Data and a Renewed Debate Over Privacy | e-Xploration | Scoop.it
The dawn of mainframe computers offered huge technological benefits, but also challenged notions of privacy. Now Big Data is bringing similar expectations and concerns.

Via Pierre Levy, Rui Guimarães Lima
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Pierre Levy's curator insight, March 24, 2013 5:43 PM

The privacy backlash...

Intriguing Networks's curator insight, March 25, 2013 7:22 AM

Mainframe and Big Data Privacy the debate continues but is it any dfferent?

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Student Attention, Engagement and Participation in a Twitter-friendly Classroom

Guided by a participatory action research methodology, this paper outlines an approach to integrating the social media Twitter platform within a tertiary education course, based on a social, constructivist pedagogy. It explores the perceptions of students on the benefits of using this technology for enhancing attentiveness, engagement and participation in the classroom. Previous studies have shown that greater participation and communication can stimulate student learning and lead to better academic performance, increased motivation, and an appreciation of different points of views. The untested hypothesis is that social media tools like Twitter can foster this type of communication. Students posted their responses during classroom activities via Twitter

and then were surveyed on their perceived benefits associated with using the social media platform. The preliminary findings of the qualitative study suggest that, while not without its challenges, social media tools like Twitter have the potential to be used effectively for education-based activities in the classroom to improve communication and engagement both amongst the students and with the instructor.

 

PDF Copy: http://dro.deakin.edu.au/eserv/DU:30049108/ally-studentattention-2012.pdf

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Share Your Google Analytics Data As An Infographic

Share Your Google Analytics Data As An Infographic | e-Xploration | Scoop.it

Wouldn’t it be great to get weekly website performance updates as a simple, easy-to-read graphic?

Now you can go beyond the Google Analytics dashboard with a new creative  – and free – tool by Visual.ly. The New Google Analytics Report automatically delivers an infographic depicting your favorite metrics right to your desktop. See the infographic at the article link for a sample of a full infographic that is generated...


Via Lauren Moss
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Didde Glad's curator insight, March 24, 2013 5:52 PM

Præsentér ledelsesinformation i GRATIS designet dashboard med gnaske få klik 

 

 

 

 

ParadigmGallery's comment, March 25, 2013 11:48 AM
did it, interesting, not so sure the artsy, soft approach to the analytics report is as visually satisfying as the bright, primary colors of google.....
AlGonzalezinfo's curator insight, April 9, 2013 10:03 PM

Awesome scoop, thanks Robin!

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The Complexity Leadership Theory (CLT)

The Complexity Leadership Theory (CLT) | e-Xploration | Scoop.it
The Complexity Leadership Theory (CLT) starts with the notion of Complex Adaptive Systems (CAS), which are a basic unit of analysis in complexity science.

Via Dr. Susan Bainbridge
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Piratical Practices - a theory/practice project - remix + appropriation + [de]collage + intellectual property...

Piratical Practices - a theory/practice project - remix + appropriation + [de]collage + intellectual property... | e-Xploration | Scoop.it

Piratical Practices is a theory/practice project exploring the aestheticonceptechniques && intersections of remix + appropriation + [de]collage + intellectual property + sampling + plunderphonics + detournement + plagiarism + versioning + sharing + [etc] w/ a focus on our technological times ✄ ☠ ✍


Via Jacques Urbanska
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European Cybercrime Center (EC3)

European Cybercrime Center (EC3) | e-Xploration | Scoop.it

With increasing cybercrime targeting citizens, businesses and governments EC3 came operational on 11 January 2013 be the focal point in the EU’s fight against cybercrime.

The European Union is a key target because of Internet-based economies and payment systems and its advanced Internet infrastructure.

EC3 will support Member States and the European Union’s institutions in building operational and analytical capacity for investigations and cooperation with international partners.


Via Paulo Félix
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Visualize the whole Internet with an app

Peer 1 Hosting has a new app that allows you to see the interconnecting servers that make up the Internet around the world.
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Open access and the humanities: reimagining our future

Open access and the humanities: reimagining our future | e-Xploration | Scoop.it
Instead of worrying about the 'potential destruction' open access might have on the humanities, says Martin Eve, why not work towards a solution?

Via Pierre Levy
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Pierre Levy's curator insight, March 25, 2013 8:14 PM

While, then, many are digging their heels in, kicking and screaming, or even just more quietly worrying about the potential destruction of tried-and-tested scholarly communications systems, other groups of activists in the humanities – and also forward-thinking commercial academic publishers – are seizing the bull by the horns, seeing either an ethical imperative to openly disseminate work that would otherwise remain accessible to a relatively privileged few, or the need to change in order to salvage their business models. For these groups, the time is for praxis and their solutions are workable responses to the objections raised

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Big Data Reduction 1: Descriptive Analytics - Lithosphere Community

Big Data Reduction 1: Descriptive Analytics - Lithosphere Community | e-Xploration | Scoop.it
Now that SxSW interactive is over, it’s time to get back and do some serious business. For me, that means I’ll return to the world of big data . Bu
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Four Pillars of Successful Communities of Practice

Four Pillars of Successful Communities of Practice | e-Xploration | Scoop.it
Every so often, it’s good to revisit some of the fundamentals of knowledge management and reflect on their continuing importance to the field.   I've been working with several different groups on C...
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Marine Clause's curator insight, April 4, 2013 5:25 AM

[EN] Retour sur la définition donnée par Etienne Wenger sur les communautés de pratiques en 4 points : la passion qui anime les membres, la pratique qu'ils partagent et sur laquelle ils s'accordent, la volonté d'apprendre et enfin d'intéragir régulièrement !

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ThisIsLike.Com - The Associative Knowledge Network.

ThisIsLike.Com - The Associative Knowledge Network. | e-Xploration | Scoop.it
ThisIsLike.Com - the world's first associative guide to knowledge, entertainment, and travel. Find things that are similar to something you like.
luiy's insight:

An example of that approach is This Is Like – an online mnemonic networkthat helps you retain and share your knowledge through notating its relations. It was initiated in 2007 by Dmitry Paranyushkin with the support of Nodus Labs and went live in 2009. This Is Like is an open system, which allows anyone to add content entities and make connections between them. For example, one can start with adding their favorite venue in Berlin and connecting it to similar venues in New York. This way someone who’s traveling from NY to Berlin can quickly discover their scene quickly and easily.


A more sophisticated way to use the system is to notate one’s research. One can enter a certain concept and create relationships between this and other concepts, describing how they are related. This way it’s possible to keep track of the material and to represent it in a more interconnected way. This Is Like allows one to have instant access to the whole history of research and share it with others.

 

Another product that we developed is a network archival system realized as a WordPress blog plugin and widget that allows one to navigate through content visually based on its interconnectedness. The actual relations are curated by the editors who make links between the content units that are related to each other. The degree to which the content is embedded into the context of the website is clearly indicated to help users gauge its relative importance within a wider ecosystem. Some examples of the websites working on that technology are the Creative Network for Neukoelln Area of Berlin – www.knnk.org (supported by EU and Bundesministeriums für Verkehr, Bau und Stadtentwicklung) and an online magazine PLAYBerlin – www.playberlin.com

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Definitions That Matter (Of "Digital Humanities")

Definitions That Matter (Of "Digital Humanities") | e-Xploration | Scoop.it
In a recent post, “‘Digital Humanities’: Two Definitions,” I tried to point out an ongoing conflict in the deployment of the term “Digital Humanities.” While my goal was in part to show the practical range in definitions of DH, that was not really...

Via Rob Kitchin
luiy's insight:

I was curious about how this pattern has played out in the actual grants, so I read through several lists of the grants ODH has awarded since it was formed in 2007. I’ll admit that I was surprised by how exactly this funding conforms, almost entirely, to the narrow definition.

 

I couldn’t find an easy way to download all of the data, so here I’ve compiled a table of the ODH grants in 2010 (I’ve uploaded the complete data in anExcel spreadsheet of 2010 ODH grants). I’ve broken them down into categories that I’ve tried to make as fair as possible. There are just under $5 million in grants; of that about 1/3 goes to archives, 1/3 to tool-building, and 1/3 to workshops; in terms of the number of grants awarded the percentages are slightly different, but still go almost entirely to these three activities. There is exactly 1 grant that can reasonably be said to foreground interpretation or analysis. There are none that “study the impact of digital technology.” Based on my reading of the recent NEH records, this is a representative sample of ODH funding, and it is important to reiterate that while it by no means encompasses all of the grants NEH awarded that touched on digital topics, it does include all of the ODH grants, and therefore all of the grants formally labeled “Digital Humanities.” What is especially notable is exactly what the change in ODH mission wording would lead one to expect: there is virtually no funding for interpretation, analysis, or tool use as a primary activity. (The only topic that arguably might be framed misleadingly by my rough categorization is pedagogy, but only very subtly so: between a third and a half of the 12 workshops can be said to have pedagogy as a focus of the workshop being held–that is, they are workshops for teachers and other educators– but as Katherine Harris so rightly keeps emphasizing, this is not direct funding for pedagogical projects.)

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Intriguing Networks's curator insight, April 7, 2013 4:33 AM

everyone seems to spend a lot of time defining but does it matter or should it be fluid by the nature of the beast...

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Internet Census 2012

Internet Census 2012 | e-Xploration | Scoop.it
luiy's insight:

Abstract While playing around with the Nmap Scripting Engine (NSE) we discovered an amazing number of open embedded devices on the Internet. Many of them are based on Linux and allow login to standard BusyBox with empty or default credentials. We used these devices to build a distributed port scanner to scan all IPv4 addresses. These scans include service probes for the most common ports, ICMP ping, reverse DNS and SYN scans. We analyzed some of the data to get an estimation of the IP address usage. 

All data gathered during our research is released into the public domain for further study. 




1 Introduction 

Two years ago while spending some time with the Nmap Scripting Engine (NSE) someone mentioned that we should try the classic telnet login root:root on random IP addresses. This was meant as a joke, but was given a try. We started scanning and quickly realized that there should be several thousand unprotected devices on the Internet. 

After completing the scan of roughly one hundred thousand IP addresses, we realized the number of insecure devices must be at least one hundred thousand. Starting with one device and assuming a scan speed of ten IP addresses per second, it should find the next open device within one hour. The scan rate would be doubled if we deployed a scanner to the newly found device. After doubling the scan rate in this way about 16.5 times, all unprotected devices would be found; this would take only 16.5 hours. Additionally, with one hundred thousand devices scanning at ten probes per second we would have a distributed port scanner to port scan the entire IPv4 Internet within one hour.

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Monitor Twitter Search via Email – SoftLayer Blog

Monitor Twitter Search via Email – SoftLayer Blog | e-Xploration | Scoop.it
luiy's insight:

About three weeks ago, Hazzy stopped by my desk and asked if I’d help build a tool that uses the Twitter Search API to collect brand keywords mentions and send an email alert with those mentions in digest form every 30 minutes. The social media team had been using Twilert for these types of alerts since February 2012, but over the last few months, messages have been delayed due to issues connecting to Twitter search … It seems that the service is so popular that it hits Twitter’s limits on API calls. An email digest scheduled to be sent every thirty minutes ends up going out ten hours late, and ten hours is an eternity in social media time. We needed something a little more timely and reliable, so I got to work on a simple “Twitter Monitor” script to find all mentions of our keyword(s) on Twitter, email those results in a simple digest format, and repeat the process every 30 minutes when new mentions are found.

 

With Bear’s Python-Twitter library on GitHub, connecting to the Twitter API is a breeze. Why did we use Bear’s library in particular? Just look at his profile picture. Yeah … ’nuff said. So with that Python wrapper to the Twitter API in place, I just had to figure out how to use the tools Twitter provided to get the job done. For the most part, the process was very clear, and Twitter actually made querying the search service much easier than we expected. The Search API finds all mentions of whatever string of characters you designate, so instead of creating an elaborate Boolean search for “SoftLayer OR #SoftLayer OR @SoftLayer …” or any number of combinations of arbitrary strings, we could simply search for “SoftLayer” and have all of those results included. If you want to see only @ replies or hashtags, you can limit your search to those alone, but because “SoftLayer” isn’t a word that gets thrown around much without referencing us, we wanted to see every instance. This is the code we ended up working with for the search functionality:

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pyvideo.org - Analyzing Social Networks with Python

Social Network data is not just Twitter and Facebook - networks permeate our world - yet we often don't know what to do with them.
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A large-scale community structure analysis in Facebook

Understanding social dynamics that govern human phenomena, such as communications and social relationships is a major problem in current computational social sciences. In particular, given the unprecedented success of online social networks (OSNs), in this paper we are concerned with the analysis of aggregation patterns and social dynamics occurring among users of the largest OSN as the date: Facebook. In detail, we discuss the mesoscopic features of the community structure of this network, considering the perspective of the communities, which has not yet been studied on such a large scale. To this purpose, we acquired a sample of this network containing millions of users and their social relationships; then, we unveiled the communities representing the aggregation units among which users gather and interact; finally, we analyzed the statistical features of such a network of communities, discovering and characterizing some specific organization patterns followed by individuals interacting in online social networks, that emerge considering different sampling techniques and clustering methodologies. This study provides some clues of the tendency of individuals to establish social interactions in online social networks that eventually contribute to building a well-connected social structure, and opens space for further social studies.

 

A large-scale community structure analysis in Facebook
Emilio Ferrara

EPJ Data Science 2012, 1:9 http://dx.doi.org/10.1140/epjds9


Via Complexity Digest
luiy's insight:
Conclusions

The aim of this work was to investigate the emergence of social dynamics, organization patterns and mesoscopic features in the community structure of a large online social network such as Facebook. This task was quite thrilling and not trivial, since a number of theoretical and computational challenges raised.

First of all, we collected real-world data directly from the online network. In fact, as recently put into evidence in literature [40], the differences between synthetic and real-world data have profound implications on results.

After we reconstructed a sample of the structure of the social graph of Facebook, we unveiled its community structure. The main findings that emerged from the mesoscopic analysis of the community structure of this network can be summarized as follows:

(i) We assessed the tendency of online social network users to constitute communities of small size, proving the presence of a decreasing number of communities of larger size. This behavior explains the tendency of users to self-organization even in absence of a coordinated effort.

(ii) We investigated the occurrence of connections among communities, finding that some kind of links, commonly referred as to weak ties, are more relevant than others because they connect communities each other, according to the Granovetter’s strength of weak ties theory[24] and in agreement with recent studies on other online social networks such as Twitter [21].

(iii) The community structure is highly clusterized and the diameter of the community structure meta-network is small (approximately around 4 and 5). These aspects indicate the presence of thesmall world phenomenon, which characterizes real-world social networks, according to sociological studies envisioned by Milgram [23] and in agreement with some heuristic evaluations recently provided by Facebook [18,19].

The achieved results open space for further studies in different directions. As far as it concerns our long-term future research directions, we plan to investigate, amongst others, the following issues:

(i) Devising a model to identify the most representative users inside each given community. This would leave space for further interesting applications, such as the maximization of advertising on online social networks, the analysis of communication dynamics, spread of influence and information and so on.

(ii) Exploiting geographical data regarding the physical location of users of Facebook, to study the effect of strong and weak ties in the society [24]. In fact, is it known that a relevant additional source of information is represented by the geographical distribution of individuals [68-70]. For example, we suppose that strong ties could reflect relations characterized by physical closeness, while weak ties could be more appropriate to represent connections among physically distant individuals.

(iii) Concluding, we devised a strategy to estimate the strength of ties between social network users [71] and we want to study its application to online social networks on a large scale. In the case of social ties, this is equivalent to estimate the friendship degree between a pair of users by considering their interactions and their attitude to exchange information.

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Web analytics In Real Time

Web analytics In Real Time | e-Xploration | Scoop.it

Mankind loves making maps, and the world wide web, densely interconnected and phenomenally complex, always makes for a nice visual.

Typically these take the form of neon blobs floating against black backgrounds, like frames captured from old Winamp plug-ins, and while they’re always nice to look at, they don’t always do much in the way of helping us understand the massive global network we traverse every day. This latest effort, however, is a little different. Called simply Map of the Internet, it’s as informative as it is beautiful.


The map, which takes the form of a free app for Android and iOS, features 22,961 of the Internet’s biggest nodes--not individual websites, but the ISPs, universities, and other places that host them--joined by some 50,000 discrete connections. The app gives you two ways of surveying it all: geographically, on a globe, or by size, which rearranges the nodes into a loose column of points. Both views are interactive; instead of showing the Internet as a static neon blob, the app lets you explore the neon blob in the round, with all the familiar multitouch gestures. It may not look like the Google Maps app, but it instantly feels like it, which makes exploring the underbelly of the web all the easier...


Via Lauren Moss
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Chaos and Butterfly Effect - Sixty Symbols

The butterfly effect is associated with the unpredictable world of chaos... Two of our physicists have a chat about it. They are Laurence Eaves and Mark From...

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Competition among memes in a world with limited attention : Scientific Reports : Nature Publishing Group

Competition among memes in a world with limited attention : Scientific Reports : Nature Publishing Group | e-Xploration | Scoop.it

The wide adoption of social media has increased the competition among ideas for our finite attention. We employ a parsimonious agent-based model to study whether such a competition may affect the popularity of different memes, the diversity of information we are exposed to, and the fading of our collective interests for specific topics. Agents share messages on a social network but can only pay attention to a portion of the information they receive. In the emerging dynamics of information diffusion, a few memes go viral while most do not. The predictions of our model are consistent with empirical data from Twitter, a popular microblogging platform. Surprisingly, we can explain the massive heterogeneity in the popularity and persistence of memes as deriving from a combination of the competition for our limited attention and the structure of the social network, without the need to assume different intrinsic values among ideas.

luiy's insight:

MODEL DESCRIPTION.

 

Our basic model assumes a frozen network of agents. An agent maintains a time-ordered list ofposts, each about a specific meme. Multiple posts may be about the same meme. Users pay attention to these memes only. Asynchronously and with uniform probability, each agent can generate a post about a new meme or forward some of the posts from the list, transmitting the corresponding memes to neighboring agents. Neighbors in turn pay attention to a newly received meme by placing it at the top of their lists. To account for the empirical observation that past behavior affects what memes the user will spread in the future, we include a memory mechanism that allows agents to develop endogenous interests and focus. Finally, we model limited attentionby allowing posts to survive in an agent's list or memory only for a finite amount of time. When a post is forgotten, its associated meme become less represented. A meme is forgotten when the last post carrying that meme disappears from the user's list or memory. Note that list and memory work like first-in-first-out rather than priority queues, as proposed in models of bursty human activity34. In the context of single-agent behavior, our memory mechanism is reminiscent of the classic Yule-Simon model∼\cite{yule-simon43, Cattuto3001200744}.

 

The retweet model we propose is illustrated in Fig. 5. Agents interact on a directed social network of friends/followers. Each user node is equipped with a screen where received memes are recorded, and a memory with records of posted memes. An edge from a friend to a follower indicates that the friend's memes can be read on the follower's screen (#x and #y in Fig. 5(a)appear on the screen in Fig. 5(b)). At each step, an agent is selected randomly to post memes to neighbors. The agent may post about a new meme with probability pn (#z in Fig. 5(b)). The posted meme immediately appears at the top of the memory. Otherwise, the agent reads posts about existing memes from the screen. Each post may attract the user's attention with probability pr (the user pays attention to #x, #y in Fig. 5(c)). Then the agent either retweets the post (#x in Fig. 5(c)) with probability 1 − pm, or tweets about a meme chosen from memory (#v triggered by #y in Fig. 5(c)) with probability pm. Any post in memory has equal opportunities to be selected, therefore memes that appear more frequently in memory are more likely to be propagated (the memory has two posts about #v in Fig. 5(d)). To model limited user attention, both screen and memory have a finite capacity, which is the time in which a post remains in an agent's screen or memory. For all agents, posts are removed after one time unit, which simulates a unit of real time, corresponding toNu steps where Nu is the number of agents. If people use the system once weekly on average, the time unit corresponds to a week.

 

 

DISCUSSION.

 

The present findings demonstrate that the combination of social network structure and competition for finite user attention is a sufficient condition for the emergence of broad diversity in meme popularity, lifetime, and user activity. This is a remarkable result: one can account for the often-reported long-tailed distributions of topic popularity and lifetime7, 12, 14, 29 without having to assume exogenous factors such as intrinsic meme appeal, user influence, or external events. The only source of heterogeneity in our model is the social network; users differ in their audience size but not in the quality of their messages.

 

Our model is inspired by the long tradition that represents information spreading as an epidemic process, where infection is passed along the edges of the underlying social network35, 36, 37, 7, 28,12.

In the context of social media, several authors explored the temporal evolution of popularity. Wu and Huberman8 studied the decay in news popularity. They showed that temporal patterns of collective attention are well described by a multiplicative process with a single novelty factor. While the decay in popularity is attributed to competition for attention, the underlying mechanism is not modeled explicitly. Crane and Sornette10 introduced a model to describe the exogenous and endogenous bursts of attention toward a video, by combining an epidemic spreading process with a forgetting mechanism. Hogg and Lerman38 proposed a stochastic model to predict the popularity of a news story via the intrinsic interest of the story and the rates at which users find it directly and through friends. These models describe the popularity of a single piece of information, and are therefore unsuitable to capture the competition for our collective attention among multiple simultaneous information epidemics. Although recent epidemiological models have started considering the simultaneous spread of competing strains39, 40, our framework is the first attempt to deal with a virtually unbounded number of new “epidemics” that are continuously injected into the system. A closer analogy to our approach is perhaps provided by neutral models of ecosystems, where individuals (posts) belonging to different species (memes) produce offspring in an environment (our collective attention) that can sustain only a limited number of individuals. At every generation, individuals belonging to new species enter the ecosystem while as many individuals die as needed to maintain the sustainability threshold41.

 

Since Simon’s seminal paper4, the economy of attention has been an enormously popular notion, yet it has always been assumed implicitly and never put to the test. Our model provides a first attempt to focus explicitly on mechanisms of competition, and to evaluate the quantitative effects of making attention more scarce or abundant.

 

Our results do not constitute a proof that exogenous features, like intrinsic values of memes, play no role in determining their popularity. However we have shown that at the statistical level it is not necessary to invoke external explanations for the observed global dynamics of memes. This appears as an arresting conclusion that makes information epidemics quite different from the basic modeling and conceptual framework of biological epidemics. While the intrinsic features of viruses and their adaptation to hosts are extremely relevant in determining the winning strains, in the information world the limited time and attention of human behavior are sufficient to generate a complex information landscape and define a wide range of different meme spreading patterns. This calls for a major revision of many concepts commonly used in the modeling and characterization of meme diffusion and opens the path to different frameworks for the analysis of competition among ideas and strategies for the optimization/suppression of their spread.

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