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#ChaosIsGood: Using Social Networking Analysis To Measure Influence

#ChaosIsGood: Using Social Networking Analysis To Measure Influence | Social Network Analysis #sna | Scoop.it
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HR Trend Institute's curator insight, February 9, 2013 3:18 AM

Chaos is good! 

Andries Du Plessis's comment, February 27, 2013 10:58 AM
Once we understand where the influencers are in a network we can start to undertand how to manage them, right? Im surely curious to understand the new cohort of students' networks
luiy's curator insight, March 14, 2013 12:31 PM

Centrality 

Often, regardless of the industry or organization performing social networking analysis, it is important to understand which models govern their specific target network. It is also critical to understand the smaller, local relationships between the actors (nodes). For example, for intelligence analysis purposes, it is critical to identify how information flows through the network and which nodes are the most active in collecting or sharing information. As such, a centrality of a network describes how important/influential a node is to a network.

 

 Highly central networks operate similar to highly centralized governments such as theocracies or monarchies while least centralized networks mimic democratic system of governments. The centralization of a network is approximately an average of the maximum centrality of a single node over the entire network and can be calculated by Freeman’s general formula.  For practical purposes, it is not always required to calculate this number to be able to realize the centrality of a network. For example, comparing today’s terrorist groups to traditional ones it can be observed without going through the calculations that today’s groups are much less centralized and hence harder to target. Low centralized networks, though sometimes not as effective in terms of governance and implementation of an overall strategy, are much more resilient (‘anti-fragile’) to shocks. For example, it’s much easier to contain a virus in a highly centralized network than it is in a low centralized network. The other concepts of centrality: ‘Closeness’ and ‘Betweenness’ attempt to measure the minimum number of nodes information or a meme would have to travel to get from one node to another. A very close network with many well-connected nodes (‘Betweenness’) would be much better and faster in communicating certain information, virus, knowledge, tradition, and meme across its entire network. A network with a very low ‘Closeness’ would hence be less effective and efficient in doing the same. 

Influence 

One of the most important outcomes of SNA is determining influencers across a network, as well as their level of influence. There are various ways to locate influencers such as number of followers, friends or connections as well as level of activity on social media. However more models are needed to better locate influencers. 

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G20 Twitter Communities

G20 Twitter Communities | Social Network Analysis #sna | Scoop.it

Introduction Last weekend the G20 Conference was hosted in Brisbane Australia. According to the G20 official website the objectives of G20 are: The Group of Twenty (G20) is the premier forum for its members’ international economic cooperation and decision-making

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Davos on Twitter: who do the attendees follow?

Davos on Twitter: who do the attendees follow? | Social Network Analysis #sna | Scoop.it
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Network Visualization by Finanacial Times

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luiy's curator insight, Today, 5:48 AM

Every year, the World Economic Forum brings together the most recognisable figures of business and politics. With all eyes on Davos, we decided to turn the optics upside down and see who the twitterati gathered in Switzerland follow on social media.


The inner ring of circles represent the 20 most-followed accounts by Davos attendees, while the outer circles are individual attendees.

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Restaurant Business: Everyone Knows Everyone.

Restaurant Business: Everyone Knows Everyone. | Social Network Analysis #sna | Scoop.it
It’s uncanny how many New York chefs and restaurateurs have worked with the same handful of people.
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Wikipedia Mining Algorithm Reveals The Most Influential People In 35 Centuries Of Human History

Wikipedia Mining Algorithm Reveals The Most Influential People In 35 Centuries Of Human History - The Physics arXiv Blog - Medium
The top ranked men and women will surprise you
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What interests reddit?

What interests reddit? | Social Network Analysis #sna | Scoop.it
“Mark Allen Thornton, Psychology Ph.D. Candidate in the Social Cognitive and Affective Neuroscience Lab at Harvard University”
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Wang: The Role of the Director Social Networks in Spreading Misconduct

Wang: The Role of the Director Social Networks in Spreading Misconduct | Social Network Analysis #sna | Scoop.it

ABSTRACT: After 2000, a growing number of foreign firms list in the United States through reverse merger, a non-IPO listing technique that requires less information disclosure. Are the US regulations rigorous enough to deter the listing attempts of weak foreign firms? 

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Using a social network analysis, I find that the firms are assisted by Western professionals to help them circumvent the US regulations, and they commit fraud and benefit from fast stock sales after listing. Further, I find that the social network of the linked directors facilitates the spread of their misconduct. During the wrongdoers’ listings, the investors in these firms lost at least $811 million. However, the penalties charged to the wrongdoers only accounted for 4.19% of this loss. I also find that the US-listed Chinese firms have a lower average Tobin’s q compared to the China-listed firms, in contrast to the prior research’s findings. These findings contradict the concurrent research that uses the reverse mergers’ financial data, which proves to be unreliable

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Social network analysis: Centrality measures

Centrality measures: What they are, what they tell us and when to use them. Degree centrality, betweenness centrality and closeness centrality summarized.
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Intellectual Cooperation: multi-level network analysis of an international organization

Intellectual Cooperation: multi-level network analysis of an international organization | Social Network Analysis #sna | Scoop.it
Digital humanities, Data visualization, Network analysis
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Fascinating maps of what people tweet about in Istanbul, Baltimore, Barcelona and more

Fascinating maps of what people tweet about in Istanbul, Baltimore, Barcelona and more | Social Network Analysis #sna | Scoop.it
Dave Troy crunches data to see places not as neighborhoods but as relationships between people.
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72 Hours of #Gamergate — The Message — Medium

72 Hours of #Gamergate — The Message — Medium | Social Network Analysis #sna | Scoop.it
Digging through 316,669 tweets from three days of Twitter’s two-month-old trainwreck
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Existence of outsiders as a characteristic of online communication networks

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Online social networking services (SNSs) involve communication activities between large number of individuals over the public Internet and their crawled records are often regarded as proxies of real (i.e., offline) interaction structure. However, structure observed in these records might differ from real counterparts because individuals may behave differently online and non-human accounts may even participate. To understand the difference between online and real social networks, we investigate an empirical communication network between users on Twitter, which is perhaps one of the largest SNSs.
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The tribes of Davos

The tribes of Davos | Social Network Analysis #sna | Scoop.it
By David Blood and Aleksandra Wisniewska
More than 2,500 people are attending the World Economic Forum in Davos this week, but how are they connected outside of the picturesque alpine town?
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10 Types of Odd Friendships You're Probably Part Of | Wait But Why

10 Types of Odd Friendships You're Probably Part Of | Wait But Why | Social Network Analysis #sna | Scoop.it
When you're young, you make friends kind of by accident. Then they stick.
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Pseudo Feature Extraction in Social Network Analysis and Text Mining

Pseudo Feature Extraction in Social Network Analysis and Text Mining | Social Network Analysis #sna | Scoop.it
This is a webinar I delivered as a part of a webinar series entitled "We are all Social Things" organized by the IS department at King Saud University - Fema...
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Applause is Contagious Like a Disease - D-brief

Applause is Contagious Like a Disease - D-brief | Social Network Analysis #sna | Scoop.it
“ Applause spreads linearly, like a disease. The amount of time an individual feels like clapping is a factor, but not nearly as much as peer pressure.”
Via Sakis Koukouvis, Complexity Institute, Alessandro Cerboni
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Cat Perrin's curator insight, July 12, 2013 6:11 AM

La foule.. et son effet de masse...

robyns tut's curator insight, October 14, 2013 1:04 PM

This is interesting, how peer pressure can factor into little things. Would be good to see what makes the brain do these things and what chemical reactions occure.

-Tanah

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Social networks in primates: smart and tolerant species have more efficient networks

Social networks in primates: smart and tolerant species have more efficient networks | Social Network Analysis #sna | Scoop.it
Network optimality has been described in genes, proteins and human communicative networks. In the latter, optimality leads to the efficient transmission of information with a minimum number of connections. Whilst studies show that differences in centrality exist in animal networks with central individuals having higher fitness, network efficiency has never been studied in animal groups. Here we studied 78 groups of primates (24 species). We found that group size and neocortex ratio were correlated with network efficiency. Centralisation (whether several individuals are central in the group) and modularity (how a group is clustered) had opposing effects on network efficiency, showing that tolerant species have more efficient networks. Such network properties affecting individual fitness could be shaped by natural selection. Our results are in accordance with the social brain and cultural intelligence hypotheses, which suggest that the importance of network efficiency and information flow through social learning relates to cognitive abilities.
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What can we learn from the history of social network analysis? | Knight Lab | Northwestern University

What can we learn from the history of social network analysis? | Knight Lab | Northwestern University | Social Network Analysis #sna | Scoop.it
Northwestern University Knight Lab is a team of technologists and journalists working at advancing news media innovation through exploration and experimentation.
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Network visualization – Gephi fun with R

Network visualization – Gephi fun with R | Social Network Analysis #sna | Scoop.it
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In the second part of my “how to quickly visualize networks directly from R” series, I’ll discuss how to use R and the “rgexf” package to create network plots in Gephi. Gephi is a great network visualization tool that allows real-time network visualization and exploration, including network data spatializing, filtering, calculation of network properties, and clustering. 

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Thanksgiving ingredient network leftovers

Thanksgiving ingredient network leftovers | Social Network Analysis #sna | Scoop.it
Michaeleen Doucleff just wrote a very fun article on our recipe network paper for NPR’s the Salt. It made me realize that Edwin Teng, Yuru Lin and I have some leftover plots that may be Thanksgiving appropriate. If you don’t have quite the right ingredients handy while cooking Thanksgiving dinner, here is a network of [...]
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Alexander Semenov's curator insight, November 29, 2014 8:15 PM

Our International laboratory of applied social network analysis has similar project

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See how red tweeters and blue tweeters ignore each other on Ferguson

See how red tweeters and blue tweeters ignore each other on Ferguson | Social Network Analysis #sna | Scoop.it
There are red tweets and blue tweets…
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Alexander Semenov's curator insight, December 17, 2014 2:51 AM

I haven't seen a meaningful hairball in a while. Here is one of them: echo-chamber effect in tweets about Ferguson.

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Data science with F#: Social network analysis - Twitter case study by @evelgab

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In this session we will work through the whole process of social network analysis: from downloading connections using Twitter REST-based API, to implementing our own PageRank algorithm which finds the most central Twitter accounts. In the process you’ll see how we can use F# type providers to access data and harness the power of the statistical language R to run some machine learning algorithms.

At the end, you’ll know how to run your own analysis on data from Twitter and how to use data science tools to gain insights from social networks.

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