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Clustering Memes in Social Media

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The increasing pervasiveness of social media creates new opportunities to study human social behavior, while challenging our capability to analyze their massive data streams. One of the emerging tasks is to distinguish between different kinds of activities, for example engineered misinformation campaigns versus spontaneous communication. Such detection problems require a formal definition of meme, or unit of information that can spread from person to person through the social network. Once a meme is identified, supervised learning methods can be applied to classify different types of communication. The appropriate granularity of a meme, however, is hardly captured from existing entities such as tags and keywords. Here we present a framework for the novel task of detecting memes by clustering messages from large streams of social data. We evaluate various similarity measures that leverage content, metadata, network features, and their combinations. We also explore the idea of pre-clustering on the basis of existing entities. A systematic evaluation is carried out using a manually curated dataset as ground truth. Our analysis shows that pre-clustering and a combination of heterogeneous features yield the best trade-off between number of clusters and their quality, demonstrating that a simple combination based on pairwise maximization of similarity is as effective as a non-trivial optimization of parameters. Our approach is fully automatic, unsupervised, and scalable for real-time detection of memes in streaming data.

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luiy's curator insight, October 14, 2013 8:25 AM

The increasing pervasiveness of social media creates new opportunities to study human social behavior, while challenging our capability to analyze their massive data streams. One of the emerging tasks is to distinguish between different kinds of activities, for example engineered misinformation campaigns versus spontaneous communication. Such detection problems require a formal definition of meme, or unit of information that can spread from person to person through the social network. Once a meme is identified, supervised learning methods can be applied to classify different types of communication. The appropriate granularity of a meme, however, is hardly captured from existing entities such as tags and keywords. Here we present a framework for the novel task of detecting memes by clustering messages from large streams of social data. We evaluate various similarity measures that leverage content, metadata, network features, and their combinations. We also explore the idea of pre-clustering on the basis of existing entities. A systematic evaluation is carried out using a manually curated dataset as ground truth. Our analysis shows that pre-clustering and a combination of heterogeneous features yield the best trade-off between number of clusters and their quality, demonstrating that a simple combination based on pairwise maximization of similarity is as effective as a non-trivial optimization of parameters. Our approach is fully automatic, unsupervised, and scalable for real-time detection of memes in streaming data.

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Social Computing Data Repository at ASU

Social Computing Data Repository at ASU | Social Network Analysis #sna | Scoop.it
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Network Data

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Breaking the Cell #WebExpo

Talk given at Using Google Apps Script and Sheets for social network data mining and analysis Examples used in this presentation bundled at http://bit.ly/break…;
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The Strength of Weak Ties: New animation from Dalton Conley - YouTube

It is the people with whom we are the least connected who offer us the most opportunities. Latest animation from Dalton Conley's You May Ask Yourself. Learn ...
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Mapping Twitter’s Python and Data Science Communities

Mapping Twitter’s Python and Data Science Communities | Social Network Analysis #sna | Scoop.it
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Collective Learning and Optimal Consensus Decisions in Social Animal Groups

Collective Learning and Optimal Consensus Decisions in Social Animal Groups | Social Network Analysis #sna | Scoop.it

Learning has been studied extensively in the context of isolated individuals. However, many organisms are social and consequently make decisions both individually and as part of a collective. Reaching consensus necessarily means that a single option is chosen by the group, even when there are dissenting opinions. This decision-making process decouples the otherwise direct relationship between animals' preferences and their experiences (the outcomes of decisions). Instead, because an individual's learned preferences influence what others experience, and therefore learn about, collective decisions couple the learning processes between social organisms. This introduces a new, and previously unexplored, dynamical relationship between preference, action, experience and learning. Here we model collective learning within animal groups that make consensus decisions. We reveal how learning as part of a collective results in behavior that is fundamentally different from that learned in isolation, allowing grouping organisms to spontaneously (and indirectly) detect correlations between group members' observations of environmental cues, adjust strategy as a function of changing group size (even if that group size is not known to the individual), and achieve a decision accuracy that is very close to that which is provably optimal, regardless of environmental contingencies. Because these properties make minimal cognitive demands on individuals, collective learning, and the capabilities it affords, may be widespread among group-living organisms. Our work emphasizes the importance and need for theoretical and experimental work that considers the mechanism and consequences of learning in a social context.

 


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Social Media Mining book

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Connecting Core Percolation and Controllability of Complex Networks : Scientific Reports : Nature Publishing Group

Connecting Core Percolation and Controllability of Complex Networks : Scientific Reports : Nature Publishing Group | Social Network Analysis #sna | Scoop.it
“ Core percolation is a fundamental structural transition in complex networks related to a wide range of important problems. Recent advances have provided us an analytical framework of core percolation in uncorrelated random networks with arbitrary degree distributions. Here we apply the tools in analysis of network controllability. We confirm analytically that the emergence of the bifurcation in control coincides with the formation of the core and the structure of the core determines the control mode of the network. We also derive the analytical expression related to the controllability robustness by extending the deduction in core percolation. These findings help us better understand the interesting interplay between the structural and dynamical properties of complex networks.”
Via Shaolin Tan, Becheru Alexandru
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Sibout Nooteboom's curator insight, July 13, 12:52 AM

Fascinating advances

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The Collaborative Organization: How to Make Employee Networks Really Work | MIT Sloan Management Review

The Collaborative Organization: How to Make Employee Networks Really Work | MIT Sloan Management Review | Social Network Analysis #sna | Scoop.it
Once managers grasp the patterns of employee interactions, they can reduce network inefficiencies.
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Network data

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Understanding Types of Users on Twitter

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The Bechdel Test of Social Media

The Bechdel Test of Social Media | Social Network Analysis #sna | Scoop.it
The Bechdel test is a popular tool to analyze the role of women in movies, defining three conditions for a movie to pass the test: It contains two female characters Who talk to each other About something besides a man…
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Celebrity twitter followers by gender

Celebrity twitter followers by gender | Social Network Analysis #sna | Scoop.it
The most popular accounts on twitter have millions of followers, but what are their demographics like? Twitter doesn’t collect or release this kind of information, and even things like name and location are only voluntarily added to people’s profiles. Unlike Google+ … Continue reading →
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R, igraph and handsome graphs

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Slides from a presentation to the Chicago R User Group on Oct 3, 2012. It covers how to use R and Gephi to visualize a map of influence in the history of philosophy. 

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Exploring Telenovela with DBpedia, R and Gephi - David Sherlock

Exploring Telenovela with DBpedia, R and Gephi - David Sherlock | Social Network Analysis #sna | Scoop.it
Today I discovered Telenovelas. Telenovelas are short limited run programs similar to soap opera, they are popular in Spanish language counties and they are serious business. I stumbled across a clip on youtube and was instantly hooked. Check this out:…Read more ›
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Jacky Degueldre's curator insight, September 19, 4:10 PM

key concepts cybermapping

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Multilayer Networks tutorial #netsci2014

These are the slides for a tutorial talk about "multilayer networks" that I gave at NetSci 2014. I walk people through a review article that I wrote with my …
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Agricultural Science in the Wild: A Social Network Analysis of Farmer Knowledge Exchange

Agricultural Science in the Wild: A Social Network Analysis of Farmer Knowledge Exchange | Social Network Analysis #sna | Scoop.it

PLOS ONE: an inclusive, peer-reviewed, open-access resource from the PUBLIC LIBRARY OF SCIENCE. Reports of well-performed scientific studies from all disciplines freely available to the whole world.

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A Fascinating Look Inside Those 1.1 Million Open-Internet Comments

A Fascinating Look Inside Those 1.1 Million Open-Internet Comments | Social Network Analysis #sna | Scoop.it
These cluster maps give us a two-dimensional look at the complex arguments Americans posted on the topic of net neutrality. One theme in the comments had to do with the American dream.
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Watch how the centers of Western culture migrated over 2,000 years

Watch how the centers of Western culture migrated over 2,000 years | Social Network Analysis #sna | Scoop.it
If you want to map cultural hubs throughout time, you can track where history's most notable figures—like Leonardo da Vinci, Jane Austen, and Steve Jobs—were born and died. That was the thinking of Dr. Maximilian Schich, associate professor for art and technology at the University of Texas at Dallas. Schich and his team took data on more than 100,000 notable...
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Complexity Rising: From Human Beings to Human Civilization

Complexity Rising: From Human Beings to Human Civilization | Social Network Analysis #sna | Scoop.it
The New England Complex Systems Institute (NECSI) is an independent educational and research institution dedicated to advancing the study of complex systems.

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AleksBlumentals's curator insight, July 8, 1:53 AM

This schematic history of human civilization reflects a growing complexity of the collective behavior of human organizations. Some will see in the graphics what intuitively seems correct, namely that areas of grey an lateral connections matter increasingly more:

"The internal structure of organizations changed and is changing, whether this is built in formally or not.  Going from the large branching ratio hierarchies of ancient civilizations, through decreasing branching ratios of massive hierarchical bureaucracies, to hybrid systems where lateral connections appear to be more important than the hierarchy."


Without new means to connect these groups the isolation and silos make it impossible to overcome local optimums:


"As the importance of lateral interactions increases, the boundaries between subsystems become porous. The increasing collective complexity also is manifest in the increasing specialization and diversity of professions."


 

Jean-Guy Frenette's curator insight, July 15, 6:40 PM

PDGMan

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Efficiently inferring community structure in bipartite networks

Efficiently inferring community structure in bipartite networks | Social Network Analysis #sna | Scoop.it
Bipartite networks are a common type of network data in which there are two types of vertices, and only vertices of different types can be connected. While bipartite networks exhibit community structure like their unipartite counterparts, existing approaches to bipartite community detection have drawbacks, including implicit parameter choices, loss of information through one-mode projections, and lack of interpretability. Here we solve the community detection problem for bipartite networks by formulating a bipartite stochastic block model, which explicitly includes vertex type information and may be trivially extended to $k$-partite networks. This bipartite stochastic block model yields a projection-free and statistically principled method for community detection that makes clear assumptions and parameter choices and yields interpretable results. We demonstrate this model's ability to efficiently and accurately find community structure in synthetic bipartite networks with known structure and in real-world bipartite networks with unknown structure, and we characterize its performance in practical contexts.
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Social Network of World Cup 2014 Players

Social Network of World Cup 2014 Players | Social Network Analysis #sna | Scoop.it
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FIFA World Cup 2014, the biggest sporting event in four years (sorry Olympics) is starting today. The tournament holds 736 players from 32 countries. When the players are not playing for their national teams, they play in 301 different clubs. Players from different national teams meet in these clubs. For example, Manchester United has players from 9 different national teams. This means that players in the World Cup who play in Manchester United know players from at least eight different national teams. Why is this important? If two players belong to the same team (national or club), they have a social connection. Using 

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Mini Lecture: Social Network Analysis for Fraud Detection

Mini Lecture: Social Network Analysis for Fraud Detection | Social Network Analysis #sna | Scoop.it
In this mini lecture, Véronique Van Vlasselaer talks about how social networks can be leveraged to uncover fraud. Véronique is working in the DataMiningApps group led by Prof. dr. Bart Baesens at the KU Leuven (University of Leuven), Belgium.
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Learn and talk about Social network analysis, Community building, Cultural economics, Information economics, Methods in sociology

Learn and talk about Social network analysis, Community building, Cultural economics, Information economics, Methods in sociology | Social Network Analysis #sna | Scoop.it
Learn and talk about Social network analysis, and check out Social network analysis on Wikipedia, Youtube, Google News, Google Books, and Twitter on Digplanet. Digplanet gathers together information and people from all over the Internet, all focused on Social network analysis, and makes it easy to learn, explore, and join the Digparty and talk to real people who are also interested in Social network analysis.
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sofipapadi's curator insight, August 13, 12:07 AM

check out Social network analysis 

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Nonlinear Dynamics and Chaos - Steven Strogatz, Cornell University - YouTube

Nonlinear Dynamics and Chaos - Steven Strogatz, Cornell University - YouTube | Social Network Analysis #sna | Scoop.it

This course of 25 lectures, filmed at Cornell University in Spring 2014, is intended for newcomers to nonlinear dynamics and chaos. It closely follows Prof. Strogatz's book, "Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering." The mathematical treatment is friendly and informal, but still careful. Analytical methods, concrete examples, and geometric intuition are stressed. The theory is developed systematically, starting with first-order differential equations and their bifurcations, followed by phase plane analysis, limit cycles and their bifurcations, and culminating with the Lorenz equations, chaos, iterated maps, period doubling, renormalization, fractals, and strange attractors. A unique feature of the course is its emphasis on applications. These include airplane wing vibrations, biological rhythms, insect outbreaks, chemical oscillators, chaotic waterwheels, and even a technique for using chaos to send secret messages. In each case, the scientific background is explained at an elementary level and closely integrated with the mathematical theory. The theoretical work is enlivened by frequent use of computer graphics, simulations, and videotaped demonstrations of nonlinear phenomena. The essential prerequisite is single-variable calculus, including curve sketching, Taylor series, and separable differential equations. In a few places, multivariable calculus (partial derivatives, Jacobian matrix, divergence theorem) and linear algebra (eigenvalues and eigenvectors) are used. Fourier analysis is not assumed, and is developed where needed. Introductory physics is used throughout. Other scientific prerequisites would depend on the applications considered, but in all cases, a first course should be adequate preparation

 

Nonlinear Dynamics and Chaos - Steven Strogatz, Cornell University

https://www.youtube.com/playlist?list=PLbN57C5Zdl6j_qJA-pARJnKsmROzPnO9V


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Jean-Michel Livowsky's curator insight, June 2, 12:22 AM

Nonlinear Dynamics and Chaos...

Jean-Michel Livowsky's curator insight, June 2, 12:23 AM

Nonlinear Dynamics and Chaos

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Bitcoin Transaction Network Dataset

Bitcoin Transaction Network Dataset | Social Network Analysis #sna | Scoop.it

Click here to edit the title

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luiy's curator insight, May 27, 8:09 AM

1.  Overview:

 

Bitcoin (bitcoin.org) is a digital, cryptographically secure currency. Transactions between public-key "addresses" maintained in a distributed, verified public ledger form a transaction network that can be studied by network scientists. This code processes binary-format Bitcoin .dat files generated by the Bitcoin client (bitcoin.org, tested on v0.5.3.1 or lower) into human-readable flat-file formats, retaining all available information. Furthermore, we provide a data model to facilitate storage and querying in a relational database. 

 

 

 

2.  Bitcoin transaction overview:

 

The bitcoin digital currency allows users to securely prove ownership of a portion of coins that cascade through the network as a chain of re-assigned ownershiptransactions over time.A transaction on the bitcoin network is a many-to-many function, executed by a user who has ownership to (potentially many) outputs of previous transactions; the user takes this owned value and writes ownership to (potentially many) output nodes (users, represented by addresses in the network).