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.
At Box, we constantly measure customers’ engagement with our product to understand how to enhance user experience and help businesses be more productive and collaborative. With 25 million users at 225,000 businesses interacting with content 2.5 billion times quarterly, we have a unique vantage point on how enterprises in nearly every sector leverage the cloud.
But what, if anything, can the patterns in how businesses share information tell us about how they operate more generally? Zooming out, what might these patterns signal about entire industries and their relative preparedness for adapting to an increasingly information-driven economy? We’re entering an era where a company’s competitiveness is determined by its return on information – how democratized its access is, how fast it moves, and how quickly it can be updated and leveraged to generate value.
For our first Information Economy Report, we started by visually mapping the flow of information within customer organizations. Every red node represents an employee, every blue node an external collaborator, and every line a transfer of content, with thicker lines indicating more frequent sharing. The results were beautiful, and also telling.
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.
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.
Heat maps of viral content show what compels us to share.
Create content the strikes the correct emotional chords
While there is a good deal of evidence to suggest that strong emotions are key to viral sharing, there are a scarce few that indicate which emotions work best.
To this end, one of the best ways we’ve found to understand the emotional drivers of viral content is to map the emotions activated by some of the Internet’s most viral content.
In order to understand the best emotional drivers to use in the content we create, we looked at 30 of the top 100 images of the year from imgur.com as voted on Reddit.com (one of the top sharing sites in the world). We then surveyed 60 viewers to find out which emotions each image activated for them. We used Robert Plutchik’s comprehensive Wheel of Emotion as our categorization.
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.
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.
An open wiki of Network Research Centers, originally curated by John Maloney and Raffaele Vacca. It includes a list and a map of centers. Please use the NRC Submit Form to add or adjust entries in the list.
The map can be edited by anyone in Google Maps. Note: In most cases, the "Year Created" variable (year in which the center was created) is an estimate based on the publication date of the oldest center publication listed in the center website. Please correct it if you have more accurate information on a specific center.
To be added to the site/wiki access lists contact Colabria. This open Website and curated lists are supported by your donations.
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.
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.
A meme, as first termed and defined by biologist Richard Dawkins in 1976, is a cultural unit that spreads from person to person through copy or imitation. Memes both reflect and shape cultural discourse, mood, and behavioral practice. The evolutionary process of memes is compared by Dawkins and others to natural selection in genes, whereby reproductive success of a given meme is linked to variation, mutation, competition, and inheritance. In other words, memes that outperform other memes and shift appropriately with cultural sentiments will thrive and persist, while memes that fail to proliferate will fall into extinction.
Internet memes refer to these cultural units (catch phrases, images, fashions, expressions etc.) that spread rapidly via internet technologies, constructing, framing, and revealing cultural realities. Lolcats, for example, a quite successful internet meme, reflects a growing affection between humans and companion animals, and has created the normative linguistic practice of asking if one “can haz” something. In a less innocuous example, the numerous #OWS memes (described in PJ Rey’s post linked above) portray, reinforce, and aid in the construction of what Nathan Jurgenson describes an atmosphere of augmented dissent.
Network theorists have developed a way to identify the top memes in science and study how they evolved
Memes are the cultural equivalent of genes: units that transfer ideas or practices from one human to another by means of imitation. In recent years, network scientists have become increasingly interested in how memes spread.
This kind of work has led to important insights into the nature of news cycles, into information avalanches on social networks and into the role that networks themselves play in this spreading process.
But what exactly makes a meme and distinguishes it from other forms of information is not well understood. Today, Tobias Kuhn at ETH Zurich in Switzerland and a couple of pals say they’ve developed a way to automatically distinguish scientific memes from other forms of information for the first time. And they’ve used this technique to find the most important ideas in physics and how they’ve evolved in the last 100 years.
The word ‘meme’ was coined by the evolutionary biologists Richard Dawkins in his 1976 book The Selfish Gene. He argued that ideas, melodies, behaviours and so on, all evolve in the same way as genes, by means of replication and mutation, but using human culture rather than biology as the medium of evolution.
Resuming from last time, I've made some updates to the philosophers' social network including publishing two interactive maps. Quick introduction: you know that sidebar on wikipedia where it tells you someone was influenced by someone else, linking to them? These graphs are generated from asking wikipedia for a comprehensive list of every philosopher's influence on every other. There are some sample-bias issues and data problems I went over in the first part of the series, but overall it's both beautiful and interesting.
The first lets you zoom dynamically and makes it easier to see local networks. When you hover over individual philosophers, those who are not linked to them or from them disappear. This uses a tool called sigma.js.
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.
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.
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.
Happiness and other emotions have recently been an important focus of attention in a wide range of disciplines, including psychology, economics, and neuroscience , , , . 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 . Experiments have demonstrated that people can “catch” emotional states they observe in others over time frames ranging from seconds to months , , 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 .
Longitudinal data from face-to-face social networks has established that emotions as diverse as happiness , loneliness , and depression  are correlated between socially-connected individuals, and related work suggests that these correlations also exist online , , , , . 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) .
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 . 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.
Emotional states can be transferred to others via emotional contagion, leading people to experience the same emotions without their awareness. Emotional contagion is well established in laboratory experiments, with people transferring positive and negative emotions to others. Data from a large real-world social network, collected over a 20-y period suggests that longer-lasting moods (e.g., depression, happiness) can be transferred through networks [Fowler JH, Christakis NA (2008) BMJ 337:a2338], although the results are controversial. In an experiment with people who use Facebook, we test whether emotional contagion occurs outside of in-person interaction between individuals by reducing the amount of emotional content in the News Feed. When positive expressions were reduced, people produced fewer positive posts and more negative posts; when negative expressions were reduced, the opposite pattern occurred. These results indicate that emotions expressed by others on Facebook influence our own emotions, constituting experimental evidence for massive-scale contagion via social networks. This work also suggests that, in contrast to prevailing assumptions, in-person interaction and nonverbal cues are not strictly necessary for emotional contagion, and that the observation of others’ positive experiences constitutes a positive experience for people.
People who were exposed to fewer emotional posts (of either valence) in their News Feed were less expressive overall on the following days, addressing the question about how emotional expression affects social engagement online. This observation, and the fact that people were more emotionally positive in response to positive emotion updates from their friends, stands in contrast to theories that suggest viewing positive posts by friends on Facebook may somehow affect us negatively, for example, via social comparison (6, 13). In fact, this is the result when people are exposed to less positive content, rather than more.
A meme is an idea that is readily transmitted from person to person. But we humans are not perfect transmitters. While sometimes we repeat the idea exactly, often we change the meme, either unintentionally, or to embellish or improve it.
Take for example, the meme:
“No one should die because they cannot afford health care, and no one should go broke because they get sick. If you agree, post this as your status for the rest of the day”.
In September of 2009, over 470,000 Facebook users posted this exact statement as their status update. At some point someone created a variant by prepending "thinks that'' (which would follow the individual's name, e.g., “Sam thinks that no one…”), which was copied 60,000 times. The third most popular variant inserted "We are only as strong as the weakest among us'' in the middle. “The rest of the day” at one point (probably in the late evening hours) became “the next 24 hours”. Others abbreviated it to “24 hrs”, or extended it to “the rest of the week”.
Modeling memes as genes
So can memes really be modeled as genes? After all, Richard Dawkins originally coined the word "meme” to draw the analogy to genes when describing how ideas or messages replicate and evolve. How would one test the hypothesis that memes undergo a process akin to biological evolution? First, tracing biological evolution is notoriously difficult because one must discern the lineage of specific genetic sequences through generations, without having the genetic sequence of many intermediate instances. But when studying Facebook memes, we have a very unique opportunity* to actually trace when copies and mutations occurred, and these are the two basic ingredients in the evolutionary process.
The GLEAM Simulator system consists of the GLEAM Server and the GLEAMviz Client application.
The GLEAM Server uses GLEAM as the engine to perform the simulations. This server runs on high-performance computers managed by the GLEAM project.
The GLEAMviz Client is a desktop application through which users interact with the GLEAM Server. It provides a simple, intuitive and visual way to set up simulations, develop disease models, and evaluate simulation results using a variety of maps, charts and data analysis tools.
Visualisation and analysis
GLEAMviz offers three types of visualization. The first shows the spread of the infection on a zoomable 2D map while charts show the number of new cases at various levels of detail.
"The new soup is the soup of human culture. We need a name for the new replicator, a noun that conveys the idea of a unit of cultural transmission, or a unit of imitation. 'Mimeme' comes from a suitable Greek root, but I want a monosyllable that sounds a bit like 'gene'. I hope my classicist friends will forgive me if I abbreviate mimeme to meme* If it is any consolation, it could alternatively be thought of as being related to 'memory', or to the French word meme. It should be pronounced to rhyme with 'cream'.
Examples of memes are tunes, ideas, catch-phrases, clothes fashions, ways of making pots or of building arches. Just as genes propagate themselves in the gene pool by leaping from body to body via sperms or eggs, so memes propagate themselves in the meme pool by leaping from brain to brain via a process which, in the broad sense, can be called imitation. If a scientist hears, or reads about, a good idea, he passes it on to his colleagues and students. He mentions it in his articles and his lectures. If the idea catches on, it can be said to propagate itself, spreading from brain to brain. As my colleague N. K. Humphrey neatly summed up an earlier draft of this chapter:'... memes should be regarded as living structures, not just metaphorically but technically.* When you plant a fertile meme in my mind you literally parasitize my brain, turning it into a vehicle for the meme's propagation in just the way that a virus may parasitize the genetic mechanism of a host cell. And this isn't just a way of talking—the meme for, say, "belief in life after death" is actually realized physically, millions of times over, as a structure in the nervous systems of individual men the world over.' "
Last week I spoke at Measurefest. The topic of my talk was, "Network mapping and visualising relative influence"
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
The story in question is “The Cuckoo” by Sean Williams, which appears in this month’s issue of Clarkesworld. The basic premise is simple enough: In 2075, after we’ve developed basic matter-transportation technology capable of allowing humans to travel from one place to another, a person or persons unknown uses April 1st as an opportunity to launch a prank. “More than one thousand commuters traveling via d-mat arrive at their destinations wearing red clown noses; they weren’t wearing them when they left.” More pranks follow in the years after and take on a life of their own – a cult grows up around what becomes popularly termed “The Fool”, complete with festivals, fans, erotic fanfiction, copycats, critical social analysis, and endless speculation.
Jenny Davis writes on internet memes as the “mythology of augmented society”, sites where meaning is produced and reproduced, where we tell stories to ourselves about ourselves, often – though not always – with political significance:
"We can see clearly that the myth and the meme share a semiotic structure in which the first order sign becomes the mythic and/or memetic signifier. The Guy Fawkes mask, for example, is simultaneously the sign of an historical moment, a popular film, and the hacker group Anonymous, as well as a signifier of the contested relation between political institutions and the anonymous components that make up “the masses.” Moreover, the meme, like the myth, is divorced from its construction, stated instead as indisputable fact. Just as Barth’s saluting Black soldier does not offer up a viewpoint for debate, the Guy Fawkes mask does not make an argument, it asserts a cultural refusal to be oppressed."
Submitted by Deborah Meehan on Tue, 04/29/2014 - 15:17 Over the past several months the Leadership Learning Community has had the opportunity to partner with the Health Foundation for Western and Central New York to conduct a Social Network Analysis of their Health Leadership Fellows ...
LLC contracted with Ken Vance Borland, Executive Director of the Conservation Planning Institute because of his experience with SNA software and mapping. He also understands the ‘so what’ of producing maps which is to help people in the network learn how to use the information provided in the maps to make their network stronger. Together we developed a survey that went out to the first four cohorts. An advisory group of Health Leadership Fellows tested the survey, gave us feedback on the questions and helped mobilize other fellows from their cohort to complete the survey.
The fellows taking the survey were provided the names of everyone taking the survey and asked to check names of other fellows with whom they had developed a new relationship, shared resources and information and collaborated with on health related projects. In addition, survey respondents were asked a number of demographic questions about where they worked, their cohort, the issues they focused on in their work, and their professions. These questions made it possible not only to produce maps of who was collaborating but to also see how people were connected across their regions or their cohorts. To get good and reliable data from an SNA it’s important to have at least a 75% participation rate. The Health Leadership Fellows program has a very impressive 89% response rate.
Computer scientists have discovered a way to number-crunch an individual’s own preferences to recommend content from others with opposing views. The goal? To burst the “filter bubble” that surrounds us with people we like and content that we agree with.
The term “filter bubble” entered the public domain back in 2011when the internet activist Eli Pariser coined it to refer to the way recommendation engines shield people from certain aspects of the real world.
Pariser used the example of two people who googled the term “BP”. One received links to investment news about BP while the other received links to the Deepwater Horizon oil spill, presumably as a result of some recommendation algorithm.
This is an insidious problem. Much social research shows that people prefer to receive information that they agree with instead of information that challenges their beliefs. This problem is compounded when social networks recommend content based on what users already like and on what people similar to them also like.
This is the filter bubble—being surrounded only by people you like and content that you agree with.
And the danger is that it can polarise populations creating potentially harmful divisions in society.