Influence et contagion
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Influence et contagion
L'influence et la contagion dans la cyberculture
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Determinants of #Meme Popularity | #influence #twitter

Determinants of #Meme Popularity | #influence  #twitter | Influence et contagion | Scoop.it

Online social media have greatly affected the way in which we communicate with each other. However, little is known about what are the fundamental mechanisms driving dynamical information flow in online social systems. Here, we introduce a generative model for online sharing behavior and analytically show, using techniques from mathematical population genetics, that competition between memes for the limited resource of user attention leads to a type of self-organized criticality, with heavy-tailed distributions of meme popularity: a few memes "go viral" but the majority become only moderately popular. The time-dependent solutions of the model are shown to fit empirical micro-blogging data on hashtag usage, and to predict novel scaling features of the data. The presented framework, in contrast to purely empirical studies or simulation-based models, clearly distinguishes the roles of two distinct factors affecting meme popularity: the memory time of users and the connectivity structure of the social network.

 

Determinants of Meme Popularity
James P. Gleeson, Kevin P. O'Sullivan, Raquel A. Baños, Yamir Moreno

http://arxiv.org/abs/1501.05956


Via Complexity Digest
luiy's insight:

In summary, despite its simplicity, the model matches the empirical popularity distribution of hashtags on Twitter remarkably well; this is consistent with random-copying models of human decision-making [28] where the quality of the product—here, the “interestingness” of the meme—is less important than the social influence of peers’ decisions[29]. The generalization of the model (as shown in the SM) to incorporate (i) heterogeneous user activity rates and (ii) a joint distribution p jk of the number of users followed j and the number of followers k, remains analytically tractable and confirms the robustness of our main finding: that competition between memes for the limited resource of user attention induces criticality in the vanis hing-innovation limit, giving power-law popularity distributions and epochs of linear-in-time popularity growth. We believe that theanalytical results and potential for fast fitting to data will render this a useful null model for further investigations of the entangled effects of memory, network structure, and competition on information spread through social networks [30].

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How Videos Go #Viral part | / #metrics #SNA #contagion

How Videos Go #Viral part | / #metrics #SNA #contagion | Influence et contagion | Scoop.it
luiy's insight:

This is a big post with a lot of variables and data. So let’s recap on what we’re saying overall. How do viral videos spread socially?

We can see there are 2 broad patterns of content diffusion. One model we call “spike” – the sudden ‘explosion’ of sharing activity – and the other we call “growth”, where popularity is a slower and steadier grower.  The metrics we’ve discussed, such as velocity, variability and social currency, provide a way to identify which kind of virality you’re looking:

 

 

http://www.facegroup.com/blog/how-videos-go-viral.html

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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, 2014 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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The selfish gene | #memes #book

luiy's insight:

 MEMES: THE NEW REPLICATORS 

 

"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.' "

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luiy's curator insight, May 31, 2014 7:38 AM

 MEMES: THE NEW REPLICATORS 

 

"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. 

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#Algorithm Distinguishes #Memes from Ordinary Information | #contagion

#Algorithm Distinguishes #Memes from Ordinary Information | #contagion | Influence et contagion | Scoop.it
Network theorists have developed a way to identify the top memes in science and study how they evolved 
luiy's insight:

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.

  

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“The Cuckoo”: #Chaos and performative #memes » Cyborgology

“The Cuckoo”: #Chaos and performative #memes » Cyborgology | Influence et contagion | Scoop.it

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.


Via Andrea Naranjo, arslog
luiy's insight:

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."

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#Predicting Successful #Memes using Network and Community Structure | #SNA #contagion

#Predicting Successful #Memes using Network and Community Structure | #SNA #contagion | Influence et contagion | Scoop.it
luiy's insight:

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.

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António F Fonseca's curator insight, April 2, 2014 6:01 AM

Another paper about popularity prediction.

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Information Evolution in Social Networks | #sna #memes #di ffusion

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

http://arxiv.org/abs/1402.6792


Via Complexity Digest, Claude Emond
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António F Fonseca's curator insight, March 1, 2014 2:00 PM

Memes are the information science counterpath of particles to physics.

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Study maps Twitter’s information ecosystem | #clusters #memes #SNA

Study maps Twitter’s information ecosystem | #clusters #memes #SNA | Influence et contagion | Scoop.it
New research outlines the six types of communities on the social network and what that means for communication
luiy's insight:

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?” 

 

 

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António F Fonseca's curator insight, March 1, 2014 7:59 AM

What community do you belong to?

Eli Levine's curator insight, March 1, 2014 4:24 PM

Indeed, we each live in our own world, not in the real world per se.

 

Some, however, have a more accurate understanding of the real world and are willing to acknowledge their shortcomings.

 

The others, who are less inclined to explore and are more focused on their own self-production, just happen to be known as conservative in our culture.  Hence, they area always hindered from perceiving the real world in the strictest of senses, and are not likely to change in light of new information received from the outside world.

 

Non-adapting humans will equal a dead and dying species.  It's a shame, though, that we can be dragged down by them for our lack of effective effort and action.

 

Sad.

 

Think about it.

Fàtima Galan's curator insight, March 3, 2014 2:44 AM

"The topographical "maps" of these communities, generated by Pew using the data visualization tool NodeXL, aren’t just maps of relationships. They represent the channels of information in Twitter’s vast ecosystem, the roads and throughways, stoops and street corners in each topical neighborhood where users congregate and swap news and anecdotes."

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#Virality Prediction and Community Structure in Social Networks | #SNA #memes #contagion

#Virality Prediction and Community Structure in Social Networks | #SNA #memes #contagion | Influence et contagion | Scoop.it
How does network structure affect diffusion? Recent studies suggest that the answer depends on the type of contagion. Complex contagions, unlike infectious diseases (simple contagions), are affected by social reinforcement and homophily.
luiy's insight:

Our method aims to discover viral memes. To label viral memes, we rank all memes in our dataset based on numbers of tweets or adopters, and define a percentile threshold. A threshold of θT or θUmeans that a meme is deemed viral if it is mentioned in more tweets than θT% of the memes, or adopted by more users than θU% of the memes, respectively. All the features are computed based on the first 50 tweets for each hashtag h. Two baselines are set up for comparison. Random guessselects nviral memes at random, where nviral is the number of viral memes in the actual data.Community-blind prediction employs the same learning algorithm as ours but without the community-based features. We compute both precision and recall for evaluation; the former measures the proportion of predicted viral memes that are actually viral in the real data, and the latter quantifies how many of the viral memes are correctly predicted. Our community-based prediction excels in both precision and recall, indicating that communities are helpful in capturing viral memes (Fig. 5). For example, when detecting the most viral memes by users (θU = 90), our method is about seven times as precise as random guess and over three times as precise as prediction without community features. We achieve a recall over 350% better than random guess and over 200% better than community-blind prediction. Similar results are obtained using different community detection methods or different types of social network links (see SI).

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#Clustering #memes in social media streams | #algorithms #sna

#Clustering #memes in social media streams | #algorithms #sna | Influence et contagion | Scoop.it
luiy's insight:

The problem of clustering content in social media has pervasive applications, including the identification of discussion topics, event detection, and content recommendation. Here we describe a streaming framework for online detection and clustering of memes in social media, specifically Twitter.

 

A pre-clustering procedure, namely protomeme detection, first isolates atomic tokens of information carried by the tweets. Protomemes are thereafter aggregated, based on multiple similarity measures, to obtain memes as cohesive groups of tweets reflecting actual concepts or topics of discussion.

 

The clustering algorithm takes into account various dimensions of the data and metadata, including natural language, the social network, and the patterns of information diffusion. As a result, our system can build clusters of semantically, structurally, and topically related tweets.


The clustering process is based on a variant of Online K-means that incorporates a memory mechanism, used to "forget" old memes and replace them over time with the new ones. The evaluation of our framework is carried out by using a dataset of Twitter trending topics.


Over a one-week period, we systematically determined whether our algorithm was able to recover the trending hashtags. We show that the proposed method outperforms baseline algorithms that only use content features, as well as a state-of-the-art event detection method that assumes full knowledge of the underlying follower network. We finally show that our online learning framework is flexible, due to its independence of the adopted clustering algorithm, and best suited to work in a streaming scenario.

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The Harlem Shake Story: Birth of a #Meme | #SNA #virality #datascience

The Harlem Shake Story: Birth of a #Meme | #SNA #virality #datascience | Influence et contagion | Scoop.it
A series of remixed videos along with a number of key communities around the world triggered a rapid escalation, giving the meme widespread global visibility. Who were the initial communities behind this mega-trend? SocialFlow took a look at 1.9 million tweets during a two-week period that included the words ’harlem shake’, or some versions of it. 
luiy's insight:

Social Flow looked at the social connections amongst users who were posting to the meme. This gave them the ability to identify the underlying communities engaging with the meme at a very early stage. In the graph above each node represents a user that was actively posting and referencing the Harlem Shake meme on Feb 7 or 8 to Twitter. Connections between users reflect follow/friendship relationships. The graph is organized using a force directed algorithm, and colored based on modularity, highlighting dominant clusters - regions in the graph which are much more interconnected. These clusters represent groups of users who tend to have some attribute in common. The purple region in the graph (left side) represents African American Twitter users who are referencing Harlem Shake in its original context. There's very little density there as it is not really a tight-knit community, but rather a segment of users who are culturally aligned, and are clearly much more interconnected amongst themselves than with other groups.

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The evolution of #memes on Facebook | #SNA #contagion

The evolution of #memes on Facebook | #SNA #contagion | Influence et contagion | Scoop.it
luiy's insight:

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[1]. 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.

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Internet #Memes: The Mythology of #Augmented Society | #semiotics

Internet #Memes: The Mythology of #Augmented Society | #semiotics | Influence et contagion | Scoop.it
luiy's insight:

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.

 
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#Stigmergic dimensions of Online Creative Interaction | #algorithms #memes

#Stigmergic dimensions of Online Creative Interaction | #algorithms #memes | Influence et contagion | Scoop.it
This paper examines the stigmergic dimensions of online interactive creativity through the lens of Picbreeder. Picbreeder is a web-based system for collaborative interactive evolution of images. Th...
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luiy's curator insight, May 2, 2014 2:56 PM

Creativity as stigmergy

 

If stigmergy happens when an agent’s effect on the environment “stimulates and guides” the work of others, then certainly creative communities must be subject to some kind of stigmergy. No creative endeavor exists in a vac- uum, and being inspired and stimulated by the work of another is so fundamental to creative communities of artists, academics, engineers, etc., that it is difficult to imagine these communities functioning any other way.

 

Closely related to the concept of stigmergy is the concept of self-organization. The reason that it is remarkable that one user’s work stimulates another’s is the emergence of patterns that appear as if that they could be centrally controlled. Often, a mix of direct communication and con- trol as well as emergent properties of the social structure give rise to collaborative creative activities. Fig. 4 suggests an informal ordering of the amount direct communication and coordination involved in several different types of creative processes, with emergent creative processes on the left end, and highly coordinated processes on the right

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News Information Flow Tracking, Yay! (NIFTY) : System for large scale real-time tracking of #memes | #datascience #algorithms

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#Truthy : Information diffusion research | #political #memes #patterns #SNA

#Truthy : Information diffusion research | #political #memes #patterns #SNA | Influence et contagion | Scoop.it
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luiy's curator insight, March 21, 2014 6:50 PM

Information diffusion research at Indiana University

 

Truthy is a research project that helps you understand how communication spreads on Twitter. 

 

We currently focus on tweets about politics, social movements and news.

 

 

Political Topics

Interactive visualizations of U.S. political conversation on Twitter :

 

- How does sentiment change over time in response to political events?

- What is most popular over time?

- Who are the most influential users?

- How does information spread in the social network?

 

 

Sentiment Timeline

- How does sentiment change over time in response to political events?

 

 

Gallery Descriptions of interesting memes:  http://truthy.indiana.edu/gallery

 

 

Meme Patterns:

What other memes are related to this one?  http://truthy.indiana.edu/memedetail?id=783&resmin=45&theme_id=4

 

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Competition among #memes in a world with limited attention | #SNA #ABM #prediction

Competition among #memes in a world with limited attention | #SNA #ABM #prediction | Influence et contagion | Scoop.it
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.
luiy's insight:

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.

 

-----------------------------

Limited attention


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.

 

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John Caswell's curator insight, March 2, 2014 8:23 AM

Very intetesting! Attention spans!

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Twitter Trends Help Researchers Forecast Viral #Memes | #SNA #contagion

Twitter Trends Help Researchers Forecast Viral #Memes | #SNA #contagion | Influence et contagion | Scoop.it
Researchers are forecasting which memes will spread far and wide
luiy's insight:

What makes a meme— an idea, a phrase, an image—go viral? For starters, the meme must have broad appeal, so it can spread not just within communities of like-minded individuals but can leap from one community to the next. Researchers, by mining public Twitter data, have found that a meme's “virality” is often evident from the start. After only a few dozen tweets, a typical viral meme (as defined by tweets using a given hashtag) will already have caught on in numerous communities of Twitter users. In contrast, a meme destined to peter out will resonate in fewer groups.

 

“We didn't expect to see that the viral memes were going to behave very differently from nonviral memes at their beginnings,” says Lilian Weng, a graduate student in informatics at Indiana University Bloomington. Those differences allowed Weng and her colleagues to forecast memes that would go viral with an accuracy of better than 60 percent, the team reported in a 2013 study.

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Les bases neurologiques des “mèmes” : comment les idées deviennent-elles contagieuses ? | #contagio #viralidad

Les bases neurologiques des “mèmes” : comment les idées deviennent-elles contagieuses ? | #contagio #viralidad | Influence et contagion | Scoop.it
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luiy's insight:

Comment les idées deviennent-elles contagieuses ? La thèse comparant certaines idées à des “virus du cerveau” ne date pas d’hier. Dans son livre Le Gène égoïste, paru en 1976, Richard Dawkins avait créé la notion de mèmes, analogues “mentaux” des gènes, qui étaient capables de s’autorépliquer d’un cerveau à l’autre, et qui, à l’instar des créatures vivantes, cherchaient avant tout à maximiser leur capacité de reproduction. Par la suite, certains avaient essayé de donner corps à une nouvelle science, la mémétique, se basant sur cette notion. L’idée n’a jamais vraiment pris, et peu de chercheurs (à l’exception peut être du philosophe Daniel Dennett et de l’anthropologue des religions Pascal Boyer) ont vraiment continué à travailler sur ces bases. En 2005, le Journal of Memetics fermait définitivement ses portes après huit années d’existence.

 

En revanche, les mèmes sont devenus un élément constitutif de la pop culture internet. Restait cependant à savoir si cette contagion des idées possède de véritables bases neurales ou si elle n’est rien d’autre… qu’un mème.

 

Des recherches effectuées à l’UCLA, sous direction du psychologue Matthew Lieberman, donnent aujourd’hui à penser qu’il y aurait une réalité biologique à l’oeuvre dans ce processus de “contamination” intellectuelle. Des chercheurs ont en effet étudié les mécanismes cérébraux impliqués dans le “buzz”.

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