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Clustering Memes in Social Media

ukituki's insight:

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 11:25 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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Complex network study of Brazilian soccer players

Complex network study of Brazilian soccer players | Social Network Analysis #sna | Scoop.it
Although being a very popular sport in many countries, soccer has not received much attention from the scientific community. In this paper, we study soccer from a complex network point of view. First, we consider a bipartite network with two kinds of vertices or nodes: the soccer players and the clubs. Real data were gathered from the 32 editions of the Brazilian soccer championship, in a total of $13\phantom{\rule{0.2em}{0ex}}411$ soccer players and 127 clubs. We find a lot of interesting and p
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The Rise of Twitter in the Political Campaign: Searching for Intermedia Agenda-Setting Effects in the Presidential Primary

The Rise of Twitter in the Political Campaign: Searching for Intermedia Agenda-Setting Effects in the Presidential Primary | Social Network Analysis #sna | Scoop.it
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Questions exist over the extent to which social media content may bypass, follow, or attract the attention of traditional media. This study sheds light on such dynamics by examining intermedia agenda-setting effects among the Twitter feeds of the 2012 presidential primary candidates, Twitter feeds of the Republican and Democratic parties, and articles published in the nation's top newspapers. Daily issue frequencies within media were analyzed using time series analysis. A symbiotic relationship was found between agendas in Twitter posts and traditional news, with varying levels of intensity and differential time lags by issue. While traditional media follow candidates on certain topics, on others they are able to predict the political agenda on Twitter.

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Investigating M&A Potential Through Network Analytics: Visualizing the NXP and Freescale Deal « Kenedict Innovation Analytics

Investigating M&A Potential Through Network Analytics: Visualizing the NXP and Freescale Deal « Kenedict Innovation Analytics | Social Network Analysis #sna | Scoop.it
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Social Network Attention Analysis

Analysis of 25 verticals looking at the most prominent social network within each based on shares, we analysed 366,596 posts and 130,279,586 total shares.
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ImmortalGraph: A System for Storage and Analysis of Temporal Graphs - Microsoft Research

ImmortalGraph is a storage and execution engine designed and optimized specifically for temporal graphs. Locality is at the center of ImmortalGraph’s design: temporal graphs are carefully laid out in both persistent storage and memory, taking into account data locality in both time and graph-structure dimensions. ImmortalGraph introduces the notion of locality-aware batch scheduling in computation, so that common “bulk” operations on temporal graphs are scheduled to maximize the benefit of in-memory data locality
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Social Network Analysis Reveals Full Scale of Kremlin’s Twitter Bot Campaign · Global Voices

Social Network Analysis Reveals Full Scale of Kremlin’s Twitter Bot Campaign · Global Voices | Social Network Analysis #sna | Scoop.it
Visualised data on nearly 20,500 pro-Kremlin Twitter "bot" accounts reveals the massive scale of information manipulation attempts on the RuNet.
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Social network analysis and visualization: Moreno’s Sociograms revisited

Social network analysis and visualization: Moreno’s Sociograms revisited | Social Network Analysis #sna | Scoop.it
CAPTION Left: original network published in Moreno (1934) Who Shall Survive?

Via AymericBds, Karlo Jara
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Knowledge Graphs: The New Type of Document for the 21st Century

Knowledge Graphs: The New Type of Document for the 21st Century | Social Network Analysis #sna | Scoop.it

Text documents and Excel tables are essentially story-telling devices. They are very useful to communicate information in a logical and chronologically coherent way. However, as the digital networks proliferate, complexity of the stories that need to be told also increases. That’s why it makes sense to embrace networks as the new useful story-telling device

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Scraawl - Social Media Analytics tool for Everyon

Scraawl - Social Media Analytics tool for Everyon | Social Network Analysis #sna | Scoop.it
Scraawl - Social Media Analytics for Everyone
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The Collective Intelligence Handbook (MIT CCI)

The Collective Intelligence Handbook (MIT CCI) | Social Network Analysis #sna | Scoop.it

"

The Collective Intelligence Handbook [tentative title]Thomas W. Malone and Michael S. Bernstein (Editors)

Collective intelligence has existed at least as long as humans have, because families, armies, countries, and companies have all--at least sometimes--acted collectively in ways that seem intelligent. But in the last decade or so a new kind of collective intelligence has emerged: groups of people and computers, connected by the Internet, collectively doing intelligent things. In order to understand the possibilities and constraints of these new kinds of intelligence, a new interdisciplinary field is emerging.

This book will introduce readers to many disciplinary perspectives on behavior that is bothcollective and intelligent. By collective, we mean groups of individual actors, including, for example, people, computational agents, and organizations. By intelligent, we mean that the collective behavior of the group exhibits characteristics such as, for example, perception, learning, judgment, or problem solving."


Via Claude Emond, Howard Rheingold
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Carine Garcia's curator insight, June 16, 2014 4:44 AM

This book will introduce readers to many disciplinary perspectives on behavior that is bothcollective and intelligent. The goal of this edited volume is to help catalyze research in the field of collective intelligence by laying out a shared set of research challenges and methodological perspectives.

Geemik's curator insight, June 17, 2014 5:29 AM

"This book will introduce readers to many disciplinary perspectives on behavior that is both collective and intelligent.  By collective, we mean groups of individual actors, including, for example, people, computational agents, and organizations.  By intelligent, we mean that the collective behavior of the group exhibits characteristics such as, for example, perception, learning, judgment, or problem solving. "

Tannah Gravelis's curator insight, August 22, 2014 4:20 AM

This article is an extremely good look into what the modern day era has created in terms of collective intelligence. The article comes from an extremely credible source, and is an extensive and comprehensive study into the topic. I\Of all the articles i have found, this is the one I would recommend to someone who wanted to gain a better understanding on the topic.

 

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College Faculties Have a Serious Diversity Problem | WIRED

College Faculties Have a Serious Diversity Problem | WIRED | Social Network Analysis #sna | Scoop.it
“ To be a professor is to belong to a select few—an insider’s club of vanishing tenured faculty positions. It’s no secret that a fancy diploma can help grads vying for those coveted spots. But while working on his PhD and contemplating his career prospects, computer scientist Aaron Clauset wanted to know just how much weight a…”
Via Niklaus Grunwald
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Understanding users through Twitter data and machine learning | Twitter Blogs

Understanding users through Twitter data and machine learning | Twitter Blogs | Social Network Analysis #sna | Scoop.it
MonkeyLearn is a text mining platform that uses machine learning to help developers easily extract and classify information from text. Here’s how to use the MonkeyLearn API to analyze Twitter data to understand user interests.
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Analyzing R-Bloggers’ posts via Twitter

Analyzing R-Bloggers’ posts via Twitter | Social Network Analysis #sna | Scoop.it
For those who don’t know, every time a new blog post gets added to R-Bloggers, it gets a corresponding tweet by @Rbloggers, which gets seen by Rbloggers’ ~20k followers fairly fast. And every time my post gets published, I can’t help but check up on how many people gave that tweet some Twitter love, ie. “favorite”d or “retweet”ed it. It’s even more exciting than getting a Facebook “like” on a photo from Costa Rica!

Seeing all these tweets and how some tweets get much more attention than others
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Social Media v. Social Network Ties: Understanding EuroMaidan Protest Participation

Social Media v. Social Network Ties: Understanding EuroMaidan Protest Participation | Social Network Analysis #sna | Scoop.it
Social Media v. Social Networks: EuroMaidan Protest Participation This article presents the first academic mapping of the EuroMaidan protests employing original on-­‐‑site protest participant survey data collected by the author in Kyiv, between 26
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Exposure to Diverse Information on Facebook

Exposure to Diverse Information on Facebook | Social Network Analysis #sna | Scoop.it
Our latest data science research, released today in Science, quantifies exactly how much individuals could be and are exposed to ideologically diverse news and information in social media.
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Introducing Pound: Process for Optimizing and Understanding Network Diffusion

Introducing Pound: Process for Optimizing and Understanding Network Diffusion | Social Network Analysis #sna | Scoop.it
Seeing the forest from the trees.
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Math for eight-year-olds: graph theory for kids!

Math for eight-year-olds: graph theory for kids! | Social Network Analysis #sna | Scoop.it
This morning I had the pleasure to be a mathematical guest in my daughter's third-grade class, full of inquisitive eight- and nine-year-old girls, and we had a wonderful interaction. Following up o...
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NetCrime

Structure and Mobility of Crime Symposium
NetSci2015 Satellite
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Network Science Book

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Network Science, a textbook for network science, is freely available under the Creative Commons licence. Follow its development onFacebook, Twitter or by signining up to our mailing list, so that we can notify you of new chapters and developments.

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Data Mining Indian Recipes Reveals New Food Pairing Phenomenon | MIT Technology Review

Data Mining Indian Recipes Reveals New Food Pairing Phenomenon | MIT Technology Review | Social Network Analysis #sna | Scoop.it
By studying the network of links between Indian recipes, computer scientists have discovered that the presence of certain spices makes a meal much less likely to contain ingredients with flavors in common.
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Food pairing seems to be common in North American and Western European cuisines but absent in cuisines from southern Europe and East Asia.

Today, Anupam Jain and pals at the Indian Institute of Technology Jodhpur say the opposite effect occurs in Indian cuisine. In this part of the world, foods with common flavors are less likely to appear together in the same recipe. And the presence of certain spices make the negative food pairing effect even stronger.

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SocioViz: a free Social Network Analysis tool for Twitter from @alessandrozonin

SocioViz: a free Social Network Analysis tool for Twitter from @alessandrozonin | Social Network Analysis #sna | Scoop.it

SocioViz is new born social media analytics platform powered with Social Network Analysis metrics; actually is available for Twitter but in the near future will be extended to other main social media channels.

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Science vs Conspiracy: Collective Narratives in the Age of Misinformation

Science vs Conspiracy: Collective Narratives in the Age of Misinformation | Social Network Analysis #sna | Scoop.it
“ The large availability of user provided contents on online social media facilitates people aggregation around shared beliefs, interests, worldviews and narratives. In spite of the enthusiastic rhetoric about the so called collective intelligence unsubstantiated rumors and conspiracy theories—e.g., chemtrails, reptilians or the Illuminati—are pervasive in online social networks (OSN). In this work we study, on a sample of 1.2 million of individuals, how information related to very distinct narratives—i.e. main stream scientific and conspiracy news—are consumed and shape communities on Facebook. Our results show that polarized communities emerge around distinct types of contents and usual consumers of conspiracy news result to be more focused and self-contained on their specific contents. To test potential biases induced by the continued exposure to unsubstantiated rumors on users’ content selection, we conclude our analysis measuring how users respond to 4,709 troll information—i.e. parodistic and sarcastic imitation of conspiracy theories. We find that 77.92% of likes and 80.86% of comments are from users usually interacting with conspiracy stories.”
Via Ashish Umre, Frédéric Amblard
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We find that 77.92% of likes and 80.86% of comments are from users usually interacting with conspiracy stories.
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What Connects Dutch Corporates? Linked Innovation in the Netherlands « Kenedict Innovation Analytics

What Connects Dutch Corporates? Linked Innovation in the Netherlands « Kenedict Innovation Analytics | Social Network Analysis #sna | Scoop.it
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