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.
Have you ever wondered what a Twitter conversation looks like from 10,000 feet? A new report from the Pew Research Center, in association with the Social Media Research Foundation, provides an aerial view of the social media network. By analyzing many thousands of Twitter conversations, we identified six different conversational archetypes. Our infographic describes each type of conversation network and an explanation of how it is shaped by the topic being discussed and the people driving the conversation.
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.
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.
Science may be difficult, but it definitely doesn’t have to be ugly. These images, from a new exhibit at the British Library, show how beautiful scientific data can be.
The exhibit features classic illustrations dating to 1603, including John Snow’s map of London’s SoHo that’s credited with revealing a contaminated water pump as the source of a 1854 cholera outbreak. There also are beautiful modern visualizations of data from satellites and gene sequencers. The exhibit, Beautiful Science: Picturing Data, Inspiring Insight, runs through May 26.
“Some games (both single-player and massive multiplayer) already include complex economic systems as part of their design mechanic. It would be easy to embed real-world, real-time financial data (such as commodity prices) within those games. Companies could track how players react to the data, then aggregate the reactions to predict real-world economic events accordingly. And, as massive multiplayer games with rich, dynamic economies become increasingly popular, opportunities to learn from player behavior will be enhanced accordingly.” (Edery, 2006)
Lewis and colleagues analyzed the donation and recruitment activity of more than 1 million members of the Save Darfur Cause between May 2007 and January 2010. About 80 percent of the members had been recruited by other members and about 20 percent had joined independently.
Of these 1 million-plus members, 99.76 percent never donated any money and 72.19 percent never recruited anyone else.
The Save Darfur Cause on Facebook raised only about $100,000. While the average donation amounts were similar to more traditional fundraising methods ($29.06), the donation rate was much smaller: 0.24 percent. Compare that to mail solicitations which typically yield donation rates of 2 to 8 percent. The larger Save Darfur campaign, the researchers note, raised more than $1 million through direct-mail contributions in fiscal year 2008 alone.
Interestingly, those that had joined the Facebook cause independently were both more likely to donate and to recruit.
Social and financial contributions, though rare on both counts, also tended to go hand-in-hand. Those individuals that did recruit were nearly four times as likely as non-recruiters to donate. And donors were more than twice as likely as non-donors to recruit.
The data contained no demographic information on the cause's members. Nor, the researchers write, could they estimate "the personal significance of [the joining] gesture to participants or the symbolic impact of the movement to onlookers.
"It is possible," they add, "that the individuals in our data set contributed to Save Darfur in other meaningful but unobserved ways."
Still, Lewis and colleagues believe the study gives some valuable insights into collective action in a digital age.
In most social and information systems the activity of agents generates rapidly evolving time-varying networks. The temporal variation in networks' connectivity patterns and the ongoing dynamic processes are usually coupled in ways that still challenge our mathematical or computational modelling. Here we analyse a mobile call dataset and find a simple statistical law that characterize the temporal evolution of users' egocentric networks. We encode this observation in a reinforcement process defining a time-varying network model that exhibits the emergence of strong and weak ties. We study the effect of time-varying and heterogeneous interactions on the classic rumour spreading model in both synthetic, and real-world networks. We observe that strong ties severely inhibit information diffusion by confining the spreading process among agents with recurrent communication patterns. This provides the counterintuitive evidence that strong ties may have a negative role in the spreading of information across networks.
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.
"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.
Social networks are visual in nature. Visualization techniques have been applied in social analysis since the field began. We aim to develop interactive visual analytic tools for complex social networks.
In this agile marketing infographic, possible influencer touchpoints are connected by what we call “cultivation bands” of tactical flow. Each band is broken down into initial contact point with a potentially engaged follower in social channels – who is then generated as a qualified lead, nurtured with engaging content and then madesocial media advocate – before the user becomes the most positive aspect of all digital marketing user phases…a contributor.
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.
Social influence is the process by which individuals adapt their opinion, revise their beliefs, or change their behavior as a result of social interactions with other people. In our strongly interconnected society, social influence plays a prominent role in many self-organized phenomena such as herding in cultural markets, the spread of ideas and innovations, and the amplification of fears during epidemics. Yet, the mechanisms of opinion formation remain poorly understood, and existing physics-based models lack systematic empirical validation.
Here, we report two controlled experiments showing how participants answering factual questions revise their initial judgments after being exposed to the opinion and confidence level of others. Based on the observation of 59 experimental subjects exposed to peer-opinion for 15 different items, we draw an influence map that describes the strength of peer influence during interactions. A simple process model derived from our observations demonstrates how opinions in a group of interacting people can converge or split over repeated interactions.
In particular, we identify two major attractors of opinion: (i) the expert effect, induced by the presence of a highly confident individual in the group, and (ii) the majority effect, caused by the presence of a critical mass of lay people sharing similar opinions. Additional simulations reveal the existence of a tipping point at which one attractor will dominate over the other, driving collective opinion in a given direction.
These findings have implications for understanding the mechanisms of public opinion formation and managing conflicting situations in which self-confident and better informed minorities challenge the views of a large uninformed majority.
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.
We investigate the predictability of successful memes using their early spreading patterns in the underlying social networks. We propose and analyze a comprehensive set of features and develop an accurate model to predict future popularity of a meme given its early spreading patterns. Our paper provides the first comprehensive comparison of existing predictive frameworks. We categorize our features into three groups: influence of early adopters, community concentration, and characteristics of adoption time series. We find that features based on community structure are the most powerful predictors of future success. We also find that early popularity of a meme is not a good predictor of its future popularity, contrary to common belief. Our methods outperform other approaches, particularly in the task of detecting very popular or unpopular memes.
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...
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.
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.
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
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).
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.
Social networks readily transmit information, albeit with less than perfect fidelity. We present a large-scale measurement of this imperfect information copying mechanism by examining the dissemination and evolution of thousands of memes, collectively replicated hundreds of millions of times in the online social network Facebook. The information undergoes an evolutionary process that exhibits several regularities. A meme's mutation rate characterizes the population distribution of its variants, in accordance with the Yule process. Variants further apart in the diffusion cascade have greater edit distance, as would be expected in an iterative, imperfect replication process. Some text sequences can confer a replicative advantage; these sequences are abundant and transfer "laterally" between different memes. Subpopulations of the social network can preferentially transmit a specific variant of a meme if the variant matches their beliefs or culture. Understanding the mechanism driving change in diffusing information has important implications for how we interpret and harness the information that reaches us through our social networks.
Information Evolution in Social Networks Lada A. Adamic, Thomas M. Lento, Eytan Adar, Pauline C. Ng
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” . 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 .
The wide adoption of social media has increased the competition among ideas for our finite attention. We employ a parsimonious agent-based model to study whether such a competition may affect the popularity of different memes, the diversity of information we are exposed to, and the fading of our collective interests for specific topics. Agents share messages on a social network but can only pay attention to a portion of the information they receive. In the emerging dynamics of information diffusion, a few memes go viral while most do not. The predictions of our model are consistent with empirical data from Twitter, a popular microblogging platform. Surprisingly, we can explain the massive heterogeneity in the popularity and persistence of memes as deriving from a combination of the competition for our limited attention and the structure of the social network, without the need to assume different intrinsic values among ideas.
Here we outline a number of empirical findings that motivate both our question and the main assumptions behind our model. We then describe the proposed agent-based toy model of meme diffusion and compare its predictions with the empirical data. Finally we show that the social network structure and our finite attention are both key ingredients of the diffusion model, as their removal leads to results inconsistent with the empirical data.
We first explore the competition among memes. In particular, we test the hypothesis that the attention of a user is somewhat independent from the overall diversity of information discussed in a given period. Let us quantify the breadth of attention of a user through Shannon entropy S = −Σi f(i) log f(i) where f(i) is the proportion of tweets generated by the user about meme i. Given a user who has posted n messages, her entropy can be as small as 0, if all of her posts are about the same meme; or as large as log n if she has posted a message about each of n different memes. We can measure the diversity of the information available in the system analogously, defining f(i) as the proportion of tweets about meme i across all users. Note that these entropy-based measures are subject to the limits of our operational definition of a meme; finer or coarser definitions would yield different values.
New research outlines the six types of communities on the social network and what that means for communication
Fil Menczer, a professor at the University of Indiana Bloomington School of Informatics and Computing, has researched the potential applications of this type of analysis for years. Menczer’s research touches on every aspect of Twitter’s role as a mirror for human communities, like examining the relationship between social data and the stock market, the spread of infectious diseases and how political campaigns manipulate data to spread misleading information. In a 2012 paper on the spread of memes on Twitter, Menczer and his team sought to demystify how information spreads on unrelated topics, yielding similar network structures to those uncovered by Pew.
One of the major lessons of network analysis, both Pew and Menczer emphasize, is that the Twitter commons hasn’t necessarily made society as democratic as techno-utopians would have you believe. Twitter isn’t a wide-open space, free of boundaries or obstacles: It’s a "mirror," as Menczer says, for the social structures of the real world.
“One of the presumptions about the rise of social media is that it’s changed everything,” says Himelboim. “In fact, if you look at the broadcast networks and brand clusters (two archetypes described by Pew), big, important and powerful institutions that wield tremendous influence offline still do on the Internet. This is really a reality check against those louder voices who claim the world has somehow been transformed."
“It makes you wonder about polarization in political discourse: Is this something that social media is responsible for?” asks Menczer. “Is more polarization easier because of social media, or are we observing what was already there with new technology? Or, even simpler: Would our discourse be better if Twitter and Facebook just didn’t exist?”
(Phys.org) —The question of how an economic system should be structured in order to best promote fairness and equality is one of the most debated subjects of all time. By approaching the complexities of this question from the field of network science, researchers from MIT and other institutions have ...
In their study, the researchers constructed a model in which individuals can earn income in two ways: by producing content or by distributing the content produced by others. A system in which more income is earned by production than by distribution is labeled as meritocratic, while one in which more income is earned by distribution is called topocratic. Importantly, the income earned by distribution depends not on what an individual produces but rather on an individual's position in the network.
Using this simple model, the researchers showed that the connectivity of the network determines whether the income is earned in a meritocratic or topocratic manner: densely connected networks are more meritocratic, while sparsely connected networks are more topocratic.
The difference makes sense, since individuals in densely connected networks can sell what they produce directly to others, and therefore do not need to share much of their proceedings with middlemen. On the other hand, in sparsely connected networks, individuals do not have direct connections with buyers and must rely on middlemen to help them connect.