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Influence et contagion
L'influence et la contagion dans la cyberculture
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Socilab - LinkedIn Social Network Visualization, Analysis, and Education | #SNA #influence

Socilab - LinkedIn Social Network Visualization, Analysis, and Education | #SNA #influence | Influence et contagion | Scoop.it
luiy's insight:

Socilab is a free tool that allows users to visualize, analyze, and download data on their LinkedIn network. It works with the LinkedIn API to a) calculate structural hole metrics such as network density, hierarchy and constraint - and displays your percentile compared to other users of the tool, b) display a dynamic/interactive visualization of your ego network with node coloring by industry and an option to enable/disable connections to self using D3.js, and c) produce a CSV adjacency matrix or Pajek edgelist for download and import into your favorite SNA package. Users might find it useful for class tutorials and/or quickly and cheaply fielding crude network surveys. Former users of the now deprecated LinkedIn inMaps may find this to be a useful alternative.

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Ebola Twitter Network | #SNA #influence #gephi

Ebola Twitter Network | #SNA #influence #gephi | Influence et contagion | Scoop.it
Introduction During the week beginning 15 September I collected 240k Tweets containing the word ‘ebola’. The following information was extracted from the Twitter Search API: Tweet: Text, Created At, Favorites User: Name, Followers, Following, Location ReTweets Mentions Tags Reply To...
luiy's insight:

TOOLS

 

More to follow in next post but here is a brief list of the tools used to create this post. 

 

- Data Collection: Python 

 

- Graph Selection: Neo4j graph database to query nodes and create the network. 

 

- Data Analysis: R 

 

- Visualisation: Gephi

 

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Who is central to a social network? It depends on your centrality #measure | #sna #influence #basics

Who is central to a social network? It depends on your centrality #measure | #sna #influence #basics | Influence et contagion | Scoop.it
luiy's insight:

One important feature of networks is the relative centrality of individuals in them.  Centrality is a structural characteristic of individuals in the network, meaning a centrality score tells you something about how that individual fits within the network overall.  Individuals with high centrality scores are often more likely to be leaders, key conduits of information, and be more likely to be early adopters of anything that spreads in a network. 

 

- Individuals who are highly connected to others within their own cluster will have a high closeness centrality.

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Truthy: Information Diffusion in Online Social Networks | #influence #virality #SNA

Truthy: Information Diffusion in Online Social Networks | #influence #virality #SNA | Influence et contagion | Scoop.it
luiy's insight:

The focus of this research project is understanding how information propagates through complex socio-technical information networks. Leveraging large-scale public data from online social networking platforms, we are able to analyze and model the spread of information, from political discourse to market trends, from news to social movements, and from trending topics to scientific results, in unprecedented detail.

 

We study how popular sentiment, user influence, attention, social network structure, and other factors affect the manner in which information is disseminated. Additionally, an important goal of the Truthy project is to better understand how social media can be abused, for example by astroturfing.

 

Our work to date includes a number of core research themes:

 

1. We study how individuals’ limited attention span affects what information we propagate and what social connections we make, and how the structure of social networks can help predict which memes are likely to become viral.

 

2. We explore social science questions via social media data analytics. Examples of research to date include analyses of geographic and temporal patterns in movements like Occupy Wall Street, societal unrest in Turkey, polarization and cross-ideological communication in online political discourse, partisan asymmetries in online political engagement, the use of social media data to predict election outcomes and forecast key market indicators, and the geographic diffusion of trending topics.

 

3. Truthy is an ensemble of web services and tools to demonstrate applications of our data mining research, from visualizing meme diffusion patterns to detecting social bots on Twitter.

 

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BeerBergman's curator insight, September 3, 5:45 PM

"luiy's insight:

The focus of this research project is understanding how information propagates through complex socio-technical information networks. Leveraging large-scale public data from online social networking platforms, we are able to analyze and model the spread of information, from political discourse to market trends, from news to social movements, and from trending topics to scientific results, in unprecedented detail.

 

We study how popular sentiment, user influence, attention, social network structure, and other factors affect the manner in which information is disseminated. Additionally, an important goal of the Truthy project is to better understand how social media can be abused, for example by astroturfing."

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#freeBook: Social Media Mining | #datascience #SNA #influence

#freeBook: Social Media Mining | #datascience #SNA #influence | Influence et contagion | Scoop.it
luiy's insight:

The Social Media Mining book is published by Cambridge University Press in 2014. Please see Cambridge’s page for the book for more information or if you are interested in obtaining an examination copy.

 

Download a complete pre-publicaiton draft of the Social Media Mining book in PDF format. The reader is allowed to take one copy for personal use but not for further distribution (either print or electronically). The book is available for purchase from Cambridge University Press and other distribution channels.

 

You can also download each chapter below:

 

• Chapter 1. Introduction to social media mining

 

Part I: Essentials
• Chapter 2. Graph essentials
• Chapter 3. Network measures
• Chapter 4. Network models
• Chapter 5. Data mining essentials

 

Part II: Communities and Interactions
• Chapter 6. Community analysis
• Chapter 7. Information diffusion in Social Media

 

Part III: Applications
• Chapter 8. Influence and homophily
• Chapter 9. Recommendation in social media
• Chapter 10. Behavior analytics

 

Download the Bibliography

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Evolution of Online User Behavior During a Social Upheaval | #datascience #diregeziparki

Evolution of Online User Behavior During a Social Upheaval | #datascience #diregeziparki | Influence et contagion | Scoop.it
luiy's insight:

Social media represent powerful tools of mass communication and information diffusion. They played a pivotal role during recent social uprisings and political mobilizations across the world. Here we present a study of the Gezi Park movement in Turkey through the lens of Twitter. We analyze over 2.3 million tweets produced during the 25 days of protest occurred between May and June 2013. We first characterize the spatio-temporal nature of the conversation about the Gezi Park demonstrations, showing that similarity in trends of discussion mirrors geographic cues. We then describe the characteristics of the users involved in this conversation and what roles they played. We study how roles and individual influence evolved during the period of the upheaval. This analysis reveals that the conversation becomes more democratic as events unfold, with a redistribution of influence over time in the user population. We conclude by observing how the online and offline worlds are tightly intertwined, showing that exogenous events, such as political speeches or police actions, affect social media conversations and trigger changes in individual behavior.

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#Influence Explorer : explore how foreign entities influence #policy and public #opinion in the U.S. | #ddj

#Influence Explorer : explore how foreign entities influence #policy and public #opinion in the U.S. | #ddj | Influence et contagion | Scoop.it
Influence Explorer connects the dots of political contributions on the federal and state level allowing you to track influence by lawmaker, company or prominent individual.
luiy's insight:

Foreign Influence Explorer

 

After months of research, technical development and manual data entry, we are proud to unveil Foreign Influence Explorer—a new database housed within Influence Explorer that lets users explore how foreign entities influence policy and public opinion in the U.S.

The data comes from the Department of Justice and is collected according to the Foreign Agents Registration Act, which places stringent reporting requirements on foreign governments, political parties, businesses and other organizations that aim to influence policy here in the States.

 

The new database also includes a feed of proposed arms sales documents from the Defense Security Cooperation Agency. This data is included because so much foreign lobbying revolves around arms sales, which creates a nexus of influence between countries that want to buy U.S. arms and U.S. manufacturers that want to sell them.

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How Advanced #Socialbots Have Infiltrated Twitter | #influence #diffusion

How Advanced #Socialbots Have Infiltrated Twitter | #influence #diffusion | Influence et contagion | Scoop.it
Automated bots can not only evade detection but gather followers and become influential among various social groups, say computer scientists who have let their bots loose on Twitter.

 

If you have a Twitter account, the chances are that you have fewer than 50 followers and that you follow fewer than 50 people yourself. You probably know many of these people well but there may also be a few on your list who you’ve never met.

 

So here’s an interesting question: how do you know these Twitter users are real people and not automated accounts, known as bots, that are feeding you links and messages designed to sway your opinions?

 

You might say that bots are not very sophisticated and so easy to spot. And that Twitter monitors the Twittersphere looking for, and removing, any automated accounts that it finds. Consequently, it is unlikely that you are unknowingly following any automated accounts, malicious or not.

 

If you hold that opinion, it’s one that you might want to revise following the work of Carlos Freitas at the Federal University of Minas Gerais in Brazil and a few pals, who have studied how easy it is for socialbots to infiltrate Twitter.

 

Their findings will surprise. They say that a significant proportion of the socialbots they have created not only infiltrated social groups on Twitter but became influential among them as well. What’s more, Freitas and co have identified the characteristics that make socialbots most likely to succeed.


Via Ashish Umre
luiy's insight:

The worry is that automated bots could be designed to significantly influence opinion in one or more of these areas. For example, it would be relatively straightforward to create a bot that spreads false rumors about a political candidate in a way that could influence an election.


...but with an estimated 20 million fake Twitter accounts already set up, Twitter’s researchers have plenty of data to work with.

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Investors Europe Stock Brokers's curator insight, September 1, 1:31 AM

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#R, iGraph, and handsome graphs | #SNA #influence

These are my 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 ...
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PLOS ONE #Complex systems articles | #ABM #netwoks #research

PLOS ONE #Complex systems articles | #ABM #netwoks #research | Influence et contagion | 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.


Via Bryan Knowles, Bernard Ryefield, Luciana Viter, Roger D. Jones, PhD
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Mapping Twitter Topic Networks: From Polarized Crowds to Community #Clusters | #politics #SNA #influence

Mapping Twitter Topic Networks: From Polarized Crowds to Community #Clusters | #politics #SNA #influence | Influence et contagion | Scoop.it
People connect to form groups on Twitter for a variety of purposes. The networks they create have identifiable contours that are shaped by the topic being discussed, the information and influencers driving the conversation, and the social network structures of the participants.
luiy's insight:

Polarized Crowds: Political conversations on Twitter

Conversations on Twitter create networks with identifiable contours as people reply to and mention one another in their tweets. These conversational structures differ, depending on the subject and the people driving the conversation. Six structures are regularly observed: divided, unified, fragmented, clustered, and inward and outward hub and spoke structures. These are created as individuals choose whom to reply to or mention in their Twitter messages and the structures tell a story about the nature of the conversation.

 

Conversational archetypes on Twitter

The Polarized Crowd network structure is only one of several different ways that crowds and conversations can take shape on Twitter. There are at least six distinctive structures of social media crowds which form depending on the subject being discussed, the information sources being cited, the social networks of the people talking about the subject, and the leaders of the conversation. Each has a different social structure and shape: divided, unified, fragmented, clustered, and inward and outward hub and spokes.

After an analysis of many thousands of Twitter maps, we found six different kinds of network crowds.

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The 26 #VC s who sit on the most Tech IPO Pipeline Boards | #SNA #influence

The 26 #VC s who sit on the most Tech IPO Pipeline Boards | #SNA #influence | Influence et contagion | Scoop.it
The VC Partner social graph helps quantify and illustrate the level of connectivity among VC partners and their ties to successful (and not successful companies).
luiy's insight:

The Venture Capital Partner Social Graph (alpha)

Given the importance of networks in VC performance (aka network centrality), we've begun building a VC social graph which helps to quantify and illustrate the level of connectivity among VC partners and their ties to successful (and not successful companies). The end goal is to use network centrality and other measures to help LPs (and even firms themselves) understand who are the partners that create value and who might be the proverbial "dead wood". This is not dissimilar to our Investor Mosaic algorithms but applied to individual VC partners.

 

This extract of the VC Partner Social Graph looks at a set of elite VCs - specifically 26 individual VCs who sit on the Boards of 5 or more Tech IPO Pipeline companies we identified at the end of 2013. The VC Partner Social Graph also uses various algorithms to asses betweenness, reach and influence of individuals. And for fun, we applied some facial recognition algorithms to understand which of these 26 investors seemed happiest based on their pictures.

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What Fuels the Most Influential Tweets? | #influence #SNA #datascience

What Fuels the Most Influential Tweets? | #influence #SNA #datascience | Influence et contagion | Scoop.it
The number of followers you have and the exact wording matter less than you think. What makes a difference is having the right message for the right people.
luiy's insight:

"Influence" doesn't necessarily mean what you think it does. In the age of the social-media celebrity, a glut of Twitter followers or particularly pugnacious sampling of pithy updates are often the hallmarks of an influencer. But new research suggests that influence is situational at best: as people compete for the attention of the broader online ecosystem, the relevance of your message to the existing conversation of those around you trumps any innate "power" a person may have.

 

.... According to co-author Vespignani, having millions of followers does not denote an important message. Rather, the messages with the most immediate relevance tend to have a higher probability of resonating within a certain network than others. Think of it as "survival of the fittest" for information: those tweets that capture the most attention, whether related to a major geopolitical or news event or a particular interest, are likely to persist longer. This competition sounds bad, but it's generally good for messages in general: thousands of tweets about Japan's 2011 earthquake or the ongoing conflict in Syria don't cancel each other out, but help refocus the attention of the wider Twitter audience on those issues, which in turn provides an added lift to individual messages over other off-topic ones.

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The #Neuroscience of Social #Influence | Beautiful Minds

The #Neuroscience of Social #Influence | Beautiful Minds | Influence et contagion | Scoop.it
Before I wrote this article, I went through two stages. In the first stage, I cruised the academic journals for interesting papers. Once I found a ...
luiy's insight:

Can the pattern of neurons firing in my brain predict how much this article will be retweeted on twitter?

 

A recent study conducted by Emily Falk, Matthew Lieberman, and colleagues gets us closer to answering these important questions. The researchers recruited undergraduate participants and randomly assigned them to two groups: the “interns” and the “producers.” The 20 interns were asked to view ideas for television pilots and provide recommendations to the 79 producers about which shows should be considered for further development and production. All of the interns had their brains scanned by fMRI while they viewed the videos, and they were then videotaped while they discussed the merits of each pilot show idea. The producers rated which ideas they would like to further recommend. How was neural activity related to the spread of ideas?

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Nicholas Christakis: The #Sociological Science Behind Social #Networks and Social #Influence | #SNA

If You're So Free, Why Do You Follow Others? The Sociological Science Behind Social Networks and Social Influence. Nicholas Christakis, Professor of Medical ...

luiy's insight:

If you think you're in complete control of your destiny or even your own actions, you're wrong. Every choice you make, every behavior you exhibit, and even every desire you have finds its roots in the social universe. Nicholas Christakis explains why individual actions are inextricably linked to sociological pressures; whether you're absorbing altruism performed by someone you'll never meet or deciding to jump off the Golden Gate Bridge, collective phenomena affect every aspect of your life. By the end of the lecture Christakis has revealed a startling new way

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Bill Aukett's curator insight, September 29, 8:34 PM

Human networks as complex systems?

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#Culturegraphy: the Cultural Influences and References between Movies | #DH #influence #dataviz

#Culturegraphy: the Cultural Influences and References between Movies | #DH #influence #dataviz | Influence et contagion | Scoop.it
luiy's insight:

Culturegraphy [culturegraphy.com], developed by "Information Model Maker" Kim Albrecht reveals represent complex relationships of over 100 years of movie references.

 

Movies are shown as unique nodes, while their influences are depicted as directed edges. The color gradients from blue to red that originate in the1980s denote the era of postmodern cinema, the era in which movies tend to adapt and combine references from other movies.

 

Although the visualizations look rather minimalistic at first sight, their interactive features are quite sophisticated and the resulting insights are naturally interesting. Therefore, do not miss out the explanatory movie below.

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A Look Inside Those 1.1 Million Open-Internet Comments | #datascience #complexity #SNA

A Look Inside Those 1.1 Million Open-Internet Comments | #datascience #complexity #SNA | Influence et contagion | 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.
luiy's insight:

How To Read This Cluster Map

 

- Similar nodes typically cluster together and clusters are grouped by color

- Each node represents a news story; a node sized by degree represents number of connections (i.e., similarity) to other nodes

- Connections represent similar language used across nodes

- A node bridging two clusters can indicate a story that synthesizes multiple topics

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US #military studied how to #influence Twitter users in #Darpa-funded research

US #military studied how to #influence Twitter users in #Darpa-funded research | Influence et contagion | Scoop.it
Defense Department spent millions researching users, including studies on Occupy and Middle East residents, and how to better spread propaganda

Via Pierre Levy
luiy's insight:

The activities of users of Twitter and other social media services were recorded and analysed as part of a major project funded by the US military, in a program that covers ground similar to Facebook’s controversial experiment into how to control emotions by manipulating news feeds.

 

Research funded directly or indirectly by the US Department of Defense’s military research department, known as Darpa, has involved users of some of the internet’s largest destinations, including Facebook, Twitter, Pinterest and Kickstarter, for studies of social connections and how messages spread.

 
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Detecting #Emotional #Contagion in Massive Social #Networks

Detecting #Emotional #Contagion in Massive Social #Networks | Influence et contagion | 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.
luiy's insight:

Happiness and other emotions have recently been an important focus of attention in a wide range of disciplines, including psychology, economics, and neuroscience [1], [2], [3], [4]. Some of this work suggests that emotional states can be transferred directly from one individual to another via mimicry and the copying of emotionally-relevant bodily actions like facial expressions [5]. Experiments have demonstrated that people can “catch” emotional states they observe in others over time frames ranging from seconds to months [6], [7], and the possibility of emotional contagion between strangers, even those in ephemeral contact, has been documented by the effects of “service with a smile” on customer satisfaction and tipping [8].

 

Longitudinal data from face-to-face social networks has established that emotions as diverse as happiness [9], loneliness [10], and depression [11] are correlated between socially-connected individuals, and related work suggests that these correlations also exist online [4], [12], [13], [14], [15]. However, it is difficult to ascertain whether correlations in observational studies result from influencing the emotions of social contacts (contagion) or from choosing social contacts with similar emotions (homophily) [16].

 

Here, we propose an alternative method for detecting emotional contagion in massive social networks that is based on instrumental variables regression, a technique pioneered in economics [23]. In an experiment we would directly control each user's emotional expression to see what impact it has on their friends' emotional expression. However, since this is infeasible in our massive-scale setting, we identify a source of variation that directly affects the users' emotional expression (this variable is called an “instrument”). For this instrument, we use rainfall. Importantly, rainfall is unlikely to be causally affected by human emotional states, so if we find a relationship it suggests that rainfall influences emotional expression and not vice versa. We then measure whether or not the changes induced by the instrument predict changes in the friends' emotional expression. Instead of changing the user's emotion directly with an experimental treatment, we let rainfall do the work for us by measuring how much the rain-induced change in a user's expression predicts changes in the user's friends' expression.

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How to Find the Best Connected Individual in Your Social Network | #SNA #influence

How to Find the Best Connected Individual in Your Social Network | #SNA #influence | Influence et contagion | Scoop.it
Field experiments in rural India have revealed a cheap and simple way to find the best connected individuals in any social network–just ask the people.
luiy's insight:

Banerjee and co made their discovery by studying the network of links between individuals in 75 rural villages in southwest India. They measured these networks by asking people who they visited, who visited them, who they were related to, who they borrowed money from, who they lent money to, and so on.

 

They then asked people in 35 villages the following question: “If we want to spread information about a new loan product to everyone in your village, to whom do you suggest we speak?”

 

The results provide a fascinating insight into the knowledge humans build up about their social networks. When people answered this question (and substantial numbers didn’t), they unerringly identified central individuals within their village.

 

 

 

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Measurefest: network mapping and visualising relative #influence | #SNA #dataviz #tools

Measurefest: network mapping and visualising relative #influence | #SNA #dataviz #tools | Influence et contagion | Scoop.it
Last week I spoke at Measurefest. The topic of my talk was, "Network mapping and visualising relative influence"
luiy's insight:

Using conversational data from influencer networks to inform and evaluate content strategy.

 

Use an author based query (not keyword based) to grab everything they’re saying (e.g. our primary @measurefest influencer list). Then without bias, we can see what topics are being discussed right now amongst this group. This can then be used to inform content planning decisions.

 

Inform:What are your target audience / influencers talking about?Evaluate:Have you managed to influence the conversation with your content?What volume of mentions from your target audience relate to your content?Basic influencer identificationFinds generally influential people onlineWho define themselves as experts, or talk a lot about a topicAdvanced influencer network mappingConsiders the relevance of an influencer within a niche networkCreates a visual to illustrate the value of the method to senior stakeholders

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Proxy #Networks - Analyzing One Network To Reveal Another | #SNA #political

Proxy #Networks - Analyzing One Network To Reveal Another | #SNA #political | Influence et contagion | Scoop.it
Proxy Networks--Analyzing One Network To Reveal Another

Via ukituki
luiy's insight:

This article uses this network tie information to construct social networks of "buddy books". A lthough the actual political affiliation of each book purchaser is not known, the structure of the buddy book network shows that there are two clearly divided groups: a larger and morediffuse left-of-center readership, and a smaller and more closely tied right-of-centerreadership. Types or networks of readers linked to a specific author are also studied.

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ukituki's curator insight, April 15, 5:56 PM

Two books are linked if they were bought together at a major retailer on the web. I call these "buddy books". A link was drawn if either book of a pair listed the other as a buddy. The data made public by the retailer shows just the "best buddies" — the strongest ties. Other patterns may emerge with investigation of weaker ties. Amazon reveals only the top five or six books bought concurrently with a particular book. Seeing dozens of buddy books for each book would reveal some of the weaker ties and no doubt affect the structure of our network.

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Twitter #bots in class. You're here because of a robot | #datascience #agents #influence

Twitter #bots in class. You're here because of a robot | #datascience #agents #influence | Influence et contagion | Scoop.it
Note: This post is co-written with Piotr Sapieżyński Is it possible for a small computer science course to exert measurable influence (trending topics) on Twitter, a massive social network with hun...
luiy's insight:

A large part of our motivation for investigating Twitter bots in class is that the amount of manipulation that humans are experiencing on line is ever increasing. Think, for example, about how Facebook’s time-line filtering algorithm shapes the world view of hundreds of millions around the globe. And that’s just the most main stream example.

 

Social influence

 

As the course progressed, we focused on creating bots that could use machine learning to recognize “good” content for tweeting and retweeting. Bots that are able to detect topics within their tweet-stream … and distinguish between real, human accounts and robots among their followers.

However, the question remained: Can those thousands of followers  be converted to influence on Twitter? For the class’ final project, we decided to put that to the test.

The overall goal was to for each team to build a convincing bot, get human followers, and  at a specified time, for everyone work together to make specific hashtags trend on twitter. So how to achieve that goal? Here’s an overview of what each team has worked on:

 

- Build convincing avatars and use the high follower-counts as part of the disguise. 

 

- Use machine learning to tell who’s a bot and who’s not (in order to focus only on humans and ignoring bots). 

 

- Use natural language processing & machine learning to discover quality content to re-tweet and tweet. 

 

- Use network theory, to explore the network surrounding existing followers, making sure that bot actions reach entire communities.

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Origin of Peer #Influence in Social #Networks

Social networks pervade our everyday lives: we interact, influence, and are influenced by our friends and acquaintances. With the advent of the World Wide Web, large amounts of data on social networks have become available, allowing the quantitative analysis of the distribution of information on them, including behavioral traits and fads. Recent studies of correlations among members of a social network, who exhibit the same trait, have shown that individuals influence not only their direct contacts but also friends’ friends, up to a network distance extending beyond their closest peers. Here, we show how such patterns of correlations between peers emerge in networked populations. We use standard models (yet reflecting intrinsically different mechanisms) of information spreading to argue that empirically observed patterns of correlation among peers emerge naturally from a wide range of dynamics, being essentially independent of the type of information, on how it spreads, and even on the class of underlying network that interconnects individuals. Finally, we show that the sparser and clustered the network, the more far reaching the influence of each individual will be.
DOI: http://dx.doi.org/10.1103/PhysRevLett.112.098702

Origin of Peer Influence in Social Networks
Phys. Rev. Lett. 112, 098702 – Published 6 March 2014
Flávio L. Pinheiro, Marta D. Santos, Francisco C. Santos, and Jorge M. Pacheco


Via Complexity Digest
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Eli Levine's curator insight, March 10, 5:16 PM

Indeed, we are all interconnected in very profound and subtle ways, whether we accept it or not.


This one's for the Libertarians and conservatives out there, who don't seem to think that their actions effect the other, or that the other can effect them, or that the actions done onto the other will effect the actions that are done onto them by the other.

 

Kind of like how they blame the poor for being angry at the rich, after the poor produced the wealth that engorges the rich.

 

Silly people....

 

Think about it.

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#Violence Is Contagious | #diffusion

#Violence Is Contagious | #diffusion | Influence et contagion | Scoop.it
What goes around really does come around.

Via Marinella De Simone
luiy's insight:

As for modes of transmission, violence seems to spread not just via human connections but also via media, even when the violence portrayed is fictional. One study found a correlation between a child’s television-viewing habits and aggressive behavior 15 years later. “Men who were high TV-violence viewers in childhood,” the authors wrote, “were convicted of crimes at over three times the rate of other men” [6]. And in 2010, a major review of research involving more than 130,000 participants reported a causal relationship between exposure to violence in video games and aggressive behavior [7].

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