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antropologiaNet, dataviz, collective intelligence, algorithms, social learning, social change, digital humanities
Curated by luiy
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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. 

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luiy's curator insight, May 31, 2014 7:35 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. 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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13 #memes around Sisi's presidential bid | #Egypt #cyberculture #politics

13 #memes around Sisi's presidential bid | #Egypt #cyberculture #politics | e-Xploration | Scoop.it
Egyptians discuss Sisi's decision to run in the May election.
luiy's insight:

Abdel Fattah al-Sisi opponents are meme-ifying their disapproval of the retired army chief's bid to become Egypt's next president. A campaign against Sisi's decision to run surfaced following his announcement last Wednesday. The hashtag #انتخبوا_العرص or "Vote for the pimp" has gained more than 100 million impressions. 

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When Campaigns Manipulate Social Media | #memes #SNA #politics

When Campaigns Manipulate Social Media | #memes #SNA #politics | e-Xploration | Scoop.it
Social media was supposed to usher in an age of digital democracy. But savvy politicos find it a tool for manipulating voters.
luiy's insight:

Filippo Menczer, Associate Professor of Informatics and Computer Science at the Indiana University, Bloomington, serves as the principal investigator for Truthy, a research project devoted to tracking the spread of memes online. Named after Stephen Colbert's from-the-gut "truthiness," the Truthy team uses an algorithm based on election-specific keywords and mood indicators -- a type of sentiment analysis very similar to the one used at the University of Indiana to predict changes in the stock market-- to follow political misinformation campaigns on Twitter. The Truthy team, inspired by the Massachusetts election, decided to track digital astroturf campaigns during election years.

"We have a 90 percent success rate at tracking this sort of abnormal behavior on social networks, and it happens frequently" says Menczer. "People are being manipulated without realizing it because a meme can be given instant global popularity by a high search engine ranking, in turn perpetuating a falsehood."

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

Twitter Trends Help Researchers Forecast Viral #Memes | #SNA #datascience | e-Xploration | Scoop.it

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.

 


Via Claudia Mihai
luiy's insight:

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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june holley's curator insight, January 23, 2014 8:31 AM

Some important ideas here for people interested in change.

Premsankar Chakkingal's curator insight, January 30, 2014 8:58 AM

Forecasting the Future Twitter Trends in hashtags

Christian Verstraete's curator insight, February 3, 2014 4:48 AM

Twitter, what happens when things go viral?

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Los 13 #Memes del 2013 en México I #Cyberculture #Virality

Los 13 #Memes del 2013 en México I #Cyberculture #Virality | e-Xploration | Scoop.it
Ciudad de México, 20 de diciembre (SinEmbargo).– El recuento del año que está a punto de terminar también se puede narrar en imágenes que circularon po
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#Stigmergic dimensions of Online Creative Interaction | #algorithms #memes

#Stigmergic dimensions of Online Creative Interaction | #algorithms #memes | e-Xploration | 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...
luiy's insight:

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

#Truthy : Information diffusion research | #political #memes #patterns #SNA | e-Xploration | Scoop.it
luiy's insight:

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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¿Bots rezando por Venezuela? Un análisis de #PrayForVenezuela | #SNA #controverses via @AlbertoEscorcia

¿Bots rezando por Venezuela? Un análisis de #PrayForVenezuela | #SNA #controverses via @AlbertoEscorcia | e-Xploration | Scoop.it
Esto es un ejercicio para tratar de entender las recientes protestas ocurridas en Venezuela desde los pasados 12 y 13 de Febrero donde a través de etiquetas y tendencias de Twitter han llegado masivos reportes de violencia, represión de protestas, de supuesta censura e incluso las afirmaciones y el llamado …
luiy's insight:

Entiéndase esto pues como una interpretación, no como una afirmación  y menos como una postura. Solo en Venezuela los venezolanos saben qué ocurre, en el mundo tratamos de entender a la distancia y que sirva este esfuerzo par abonar al entendimiento porque existen muchas preguntas.

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Looking at Culture through a Big Data Lens I #patterns #memes

Looking at Culture through a Big Data Lens I #patterns #memes | e-Xploration | Scoop.it
I’m excited about all the (possible) breakthroughs we see happening in cultural research.
luiy's insight:

Predicting by looking at narratives


Other Big Data cultural research examples will be presented tomorrow at a conference organized by the Dutch Meertens Institute “Patterns in narrative texts“. The data that will be discussed range from narrative journalistic texts to orally transmitted folktales. In the study of history, diachronic corpora can be mined to discover how historical events are reflected in language use. In folk narrative research, patterns of interest include the stability and variability of ‘narrative building blocks’ (motifs, memes) in oral transmission, and geographical dispersion of folk beliefs in the supernatural. Establishing links between narrative texts is a common factor in all this research.

 

One of the pieces of research that will be discussed is “Mining the Twentieth Century’s History from the TIME Magazine Corpus”. Mike Kestemont & Folgert Karsdorp are going to explain how to predict Times’s Person of the Year. In their research they have paid special attention to the intriguing interplay between this list of influential personalities and the manner in which they are discussed in the magazine’s own archive. They will have a lot to explain, looking at their top-10 list for 2013, since they’ve missed the person that has won this year, Pope Fransicus. But still the researchers have a hit-rate of more than 20%.

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Rescooped by luiy from Social Network Analysis #sna
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#Clustering Memes in Social Media | #datascience #SNA_indatcom | @jabawack

#Clustering Memes in Social Media | #datascience #SNA_indatcom | @jabawack | e-Xploration | Scoop.it

Via ukituki
luiy's insight:

The increasing pervasiveness of social media creates new opportunities to study human social behavior, while challenging our capability to analyze their massive data streams. One of the emerging tasks is to distinguish between different kinds of activities, for example engineered misinformation campaigns versus spontaneous communication. Such detection problems require a formal definition of meme, or unit of information that can spread from person to person through the social network. Once a meme is identified, supervised learning methods can be applied to classify different types of communication. The appropriate granularity of a meme, however, is hardly captured from existing entities such as tags and keywords. Here we present a framework for the novel task of detecting memes by clustering messages from large streams of social data. We evaluate various similarity measures that leverage content, metadata, network features, and their combinations. We also explore the idea of pre-clustering on the basis of existing entities. A systematic evaluation is carried out using a manually curated dataset as ground truth. Our analysis shows that pre-clustering and a combination of heterogeneous features yield the best trade-off between number of clusters and their quality, demonstrating that a simple combination based on pairwise maximization of similarity is as effective as a non-trivial optimization of parameters. Our approach is fully automatic, unsupervised, and scalable for real-time detection of memes in streaming data.

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ukituki's curator insight, October 12, 2013 2:43 PM

The increasing pervasiveness of social media creates new opportunities to study human social behavior, while challenging our capability to analyze their massive data streams. One of the emerging tasks is to distinguish between different kinds of activities, for example engineered misinformation campaigns versus spontaneous communication. Such detection problems require a formal definition of meme, or unit of information that can spread from person to person through the social network. Once a meme is identified, supervised learning methods can be applied to classify different types of communication. The appropriate granularity of a meme, however, is hardly captured from existing entities such as tags and keywords. Here we present a framework for the novel task of detecting memes by clustering messages from large streams of social data. We evaluate various similarity measures that leverage content, metadata, network features, and their combinations. We also explore the idea of pre-clustering on the basis of existing entities. A systematic evaluation is carried out using a manually curated dataset as ground truth. Our analysis shows that pre-clustering and a combination of heterogeneous features yield the best trade-off between number of clusters and their quality, demonstrating that a simple combination based on pairwise maximization of similarity is as effective as a non-trivial optimization of parameters. Our approach is fully automatic, unsupervised, and scalable for real-time detection of memes in streaming data.