Cómo los videos se hacen virales en Twitter

How Videos Go Viral On Twitter

COOPER SMITH

How Videos Go Viral On Twitter (Twitter Blog) Twitter's UK research team found that there are no rules to "virality," as some spread like wildfire across the Web almost immediately, while others grow in a slow, but consistent manner. One thing is for sure, videos do increase engagement on Twitter: Brand tweets containing a video are 42% likely to be retweeted, replied to, or mentioned. 

*Note: In the example at right, the blue nodes represent Tweets; the bigger they are, the larger the potential reach of that Tweet. The yellow dots represent retweets. In each case, reach takes into account not just followers, but also audience size and amplification by retweet. Read >

Facebook Makes Pages Viewed On Mobile Devices More Functional (TechCrunch)Following a redesign in April of Pages viewed on iOS and Android, Facebook has also improved its mobile site. The redesigned Pages come integrated with OpenTable to let users book restaurant reservations. If Facebook can make Pages more important to local businesses, it could become a threat to Yelp, Foursquare, and Google Search. Read >

Op-Ed: I'm 13 And None Of My Friends Use Facebook (Mashable) Ruby Karp, a thirteen-year-old living in New York, says her friends have accounts on Facebook, but they don't use the service because of the growing popularity of Instagram, Vine, Snapchat, and other social apps. "Part of the reason Facebook is losing my generation's attention is the fact that there are other networks now," said Karp. Read >

Pixability

The Top Performing Industries On YouTube (Pixability) There is a correlation between the number of videos a brand publishes on YouTube and the total amount of views that those videos receive, according to a recent study by Pixability. The Media and B2C Tech industries are the top performing types brands on YouTube. Read >

Google+ Users Can Now Embed Music In Posts (SoundCloud via Google+) SoundCloud, a music sharing service, has integrated with Google+ to bring embedded songs to the social network. The partnership is the first third-party developer that Google+ has teamed up with for plug-in audio files. Read >

"It's Time To End Social Exceptionalism" (Forrester Blog) Forrester research analyst Nate Elliot argues that marketers are far too caught up in the idea of social media as the solution to all problems, and thus treat it as an island within itself. "But social messages that don't match the rest of your marketing

Business Insider

Los lugares más felices de USA según los tweets

The Happiest Places in America




After parsing the results from a recent University of Vermont study of geotagged tweets, theHuffington Post has compiled a map showing just how happy — or unhappy — Americans are according to the state they live in.

The numbers you see in the above graphic were derived by scoring more than 10,000 words on a positive-negative scale and measuring their frequency in millions of tweets across the United States.

Context was deliberately ignored to eliminate experimental bias, as were tweets from out-of-towners and/or tourists (which may explain why Hawaii rocked the list!).


Katy Hall and Jan Diehm explain their map and the 'word happiness score':

What emerged was significant regional variation in happiness by this calculation, which correlates with other lifestyle measures such as gun violence, obesity and Gallup'straditional wellbeing survey. A sadness belt across the South includes states that have high levels of poverty and the shortest life expectancies. 

Geography is, of course, just one predictor of moods expressed on Twitter. The researchers also used their "hedonometer" to look at daily happiness averages over the past few years — and the peaks (holidays, especially Christmas) and valleys (tragedies including the Newtown shooting and Boston Marathon bombing) are not surprising.

It turns out that Louisiana is the saddest state. "Socioeconomic factors that influence happiness in the U.S., such as poverty, unemployment and government resources, also account for some of the variation in life satisfaction levels in countries around the world," write Hall and Diehm.

Be sure to head on over to the HuffPo article to learn more — and to see how the U.S. stacks up with the rest of the world on the Life Satisfaction Index.

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Efecto de red negativo, Facebook puede decrecer exponencialmente

Afirman que Facebook desaparecerá en tres años

“Cuando vaya cuesta abajo, será muy rápido”, dijo un experto. Clarín

El presidente de la Fundación de Software Libre de Europa (FSFE), Karsten Gerloff, afirmó que Facebook desaparecerá en tres años. El especialista dijo que las grandes empresas de tecnología le entregan información a los servicios secretos de las grandes potencias. Y la gente va a dejar de usar sus servicios.

“Si Google quiere sobrevivir a largo plazo, tendrá que reinventarse. Microsoft todavía no lo ha hecho. Por eso, creo que va a desaparecer dentro de cinco o diez años”, sorprendió Gerloff en una ponencia pronunciada en la Euskal Encounter, en Bilbao, España. El encuentro junta a miles de emprendedores tecnológicos, amantes de los videojuegos y público en general, se celebra cada año y ya va por la edición número 21.

Además, el especialista consideró que cuando Facebook “vaya cuesta abajo, será muy rápido. A Facebook le doy tres años. Es una ley matemática. Pasó con MySpace y sé que volverá a pasar”.

El experto alemán también se hizo tiempo para denunciar a varias empresas del sector. “Para Google y Facebook somos productos, no clientes” y alertó que esas mismas compañías de tecnología “sin ningún aviso previo, están nutriendo a los servicios secretos. Algunas corporaciones roban nuestra información. Nosotros no sólo les damos datos, sino también nuestra confianza, pues pensamos que van a proteger nuestra privacidad. Tus datos son el producto que vende Google”.

Gerloff añadió que estas multinacionales “han tomado nuestras computadoras, nuestras redes, y encima han creado estructuras de control”, y ha explicado que en este reparto de tareas “ Facebook define quiénes somos. Amazon establece lo que queremos y Google determina lo que pensamos ”. El presidente de FSFE ha calificado la situación actual de “traición”, y ha puesto como ejemplo “la entrega por parte de Yahoo! de información confidencial de sus clientes al Gobierno chino”. Y se lamentó: “Ahora lo hacen todos”.

El objetivo de la fundación que preside Gerloff es que “la gente sea consciente de que puede decidir cuál va a ser la tecnología de mañana y en qué tipo de mundo vamos a vivir”. Por ello, señaló que “siempre hay que estar alerta y observar los nuevos sistemas. Tienes que preguntarte: ¿Quién lo controla?”.

¿Cómo puede evitar los controles el usuario común? Gerloff explicó que se pueden hacer cosas muy sencillas. Por ejemplo construir sistemas “ que no tengan un punto central de control, en los que cada uno de nosotros hable con el otro. Eso ya no es una utopía”. Hay que animarse “a poner un servidor en cada casa. Los routers pueden funcionar como pequeños servidores”. De esta forma, ha dicho, “sería posible dar otros pasos, como salir de Facebook, o cambiar la cuenta de correo electrónico por otra de una compañía pequeña”.

Visualización de la información para el ARS

Information Visualization for Social Network Analysis, from University of Maryland

La felicidad es acostarse... más que tus amigos

Happiness is…thinking you get laid more often than your friends


SEXPAND

Sex makes us happy. More sex makes us happier. But a newly published study suggests that having more sex than we think other people are having makes us happiest of all.

In a recent issue of Social Indicators Research, sociologist Tim Wadsworth concludes that sex is a little like income*: the more of it people have, the happier they tend to say they are. By examining responses from more than 15,000 participants in the nationally representative General Social Survey, Wadsworth found that people who reported having sex regularly were more likely to report a higher level of happiness (on the rather amusing scale of "very happy, pretty happy or not too happy") than those who'd gone a year or more without any sex at all.

Survey participants who claimed to be having sex 2–3 times a month were 33% more likely to report higher levels of happiness. Participants having sex once a week were 44% more likely to report higher levels of happiness than those in the midst of a year-or-more dry spell. And those who reported having sex 2–3 times per week were 55% more likely to report higher levels of happiness. The numbers suggest that self-reported happiness tends to increase along with monthly nookie numbers.

But there's one big caveat: it's all relative. Wadsworth also found that self-reported happiness depended more on how much sex you thought other people were having than how much time you, yourself, were spending dancing the mattress jig. For instance, Wadsworth found that when people having sex thrice monthly believe their friends and peers are getting it on once a week, their probability of reporting a higher level of happiness falls by about 14%. If other words: if you think other people are having more sex than you, there's a chance it'll drag down your happiness.

“There’s an overall increase in sense of well-being that comes with engaging in sex more frequently, but there’s also this relative aspect to it,” said Wadsworth in a statement. “Having more sex makes us happy, but thinking that we are having more sex than other people makes us even happier.”

He goes on to concede that social comparisons may not be behind the effects he's observed, but that he "can't think of a better explanation for why how much sex other people are having would influence a person's happiness." After all, he says, we are social creatures. The vast majorities of our identities are defined within a social context.

Does this mean you should try to increase your happiness by trying to have more sex than your friends? Hardly. In fact, a better tactic might be to ignore their sex lives altogether.

The conflation of relative sexual activity with happiness (and the overarching social awareness that Wadsworth mentions), calls to mind a review co-authored in 2011 by Yale psychologist June Gruber, wherein she concludes that, when it comes to the pursuit of happiness, one of the best things you can do is stop trying to be happy.

Gruber and her colleagues explain that when you actively pursue happiness (making an effort to have as much sex or more than you think your peers are having, for example) "with the motivation or expectation that these things [you're doing] ought to make you happy," it can, paradoxically, "lead to disappointment and decreased happiness." The solution, then, is zen-like: to be happy, stop trying to be happy. Similarly, it seems that being less conscious of your sex life relative to the sex lives of others may be a more fundamental path to happiness than trying to keep up with the Jones' – and that's advice that extends well beyond the bedroom.

Wadsworth's paper, "Sex and the Pursuit of Happiness: How Other People's Sex Lives are Related to our Sense of Well-Being," is published in Social Indicators Research.

*Before someone gets on my case about this: The relationship between money and happiness (like the definition of something like "happiness," in and of itself) is a contentious one. Some of the latest studies on money's role as a social and/or psychological determinant of happiness have shown that self-reports of happiness do appear to increase in line with annual income, but that the effects seem to peter out as you approach a given salary. A recent study out of Princeton put that figure at $75,000/year.

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¿Twitter puede predecir el proceso electoral estadounidense?

A New Study Says Twitter Can Predict US Elections

The more a candidate is mentioned on Twitter, the better. (After you control for a lot.)

ROBINSON MEYER

The Atlantic

While the Indiana University study examined House of Representatives and not presidential candidates, according to its findings, a tweet from John Boehner about Barack Obama would predict the election of... Barack Obama. (Reuters)

Political consultants, university sociologists, and amateur statisticians are now one step closer to using data from Twitter like they use polling data.

An Indiana University study has pulled, from the noise and muck of the stream, a statistically significant relationship between Twitter data and US election results.

Specifically, the study found a correlation between the number of times a candidate for the House of Representatives was mentioned on Twitter in the months before an election and his or her performance in that election. The more a candidate is mentioned on Twitter, the better.

Previous studies have found a loose relationship between activity on Twitter and financial or political events. A 2010 study found that certain ways of analyzing Twitter could foretell a film's success at the box office better than a prediction market for that purpose. In 2011, a German Ph.D. student correlated stock market performance to certain analytic variables on Twitter. At the same time, activists are often disappointed when chatter about a candidate on Twitter fails to translate to electoral victory, and there's a sense that Twitter has almost no relation to political success

This study is notable because it proposes a meaningful relationship between Twitter and American election results. "From the beginning, we were looking to construct simple and easy to operationalize measures of political behavior and attitudes that could be useful to social scientists," Joe DiGrazia, one of the researchers, wrote to me in an email.

The study specifically controlled for the conditions surrounding an election. If a candidate is an incumbent, they would be mentioned on Twitter more, so the study discounted their position. Likewise, the study also discounted candidates who the press covered more, using the number of times a candidate's name was mentioned on CNN as an imprecise measurement of mainstream media hype.

The study's finding bears a number of implications. First, it implies that all Twitter chatter is good Twitter chatter: that if more people are talking about you, you're likely to do better at the polls. Second, it provides a working model for predictive Twitter analysis in future elections, especially in the US. As Alex Roarty wrote at National Journal:

In [one of the study's researchers Fabio] Rojas's view, the findings should revolutionize how campaigns conduct themselves. Rather than spending hundreds of thousands, or even millions, of dollars on surveys, campaigns could simply gauge their status on Twitter. That should help campaigns with fewer resources compete with well-heeled incumbents, he said.

While I'm intrigued by the idea of a pesky challenger defeating a "well-heeled incumbent" without polling data, running on Twitter analysis alone, I suspect professional political Twitter analysts will pop up soon enough. Twitter already sells huge bulks of tweets to economic and financial consultants, who use the aggregate feed to predict the stock market; the company would be foolish not to make money off this capability in politics, too. Rather, what struck me about the study was that its researchers thought they could sidestep problems which plague polling data: People will tell pollers they plan to vote for the fashionable candidate, for instance, when they really intend on voting for someone else. I doubt that Twitter, inherently public and performative, avoids that bias.

At last count, eight percent of American adults use Twitter daily; only 15 percent are on it at all. The authors recognize this, yet their algorithms and data remain valid. So I wonder about the effects we'll see from work like this in the next decade or so, from the politicization and commercialization of the digital public sphere we've made, in which the journaling agglomerate can predict the future, in which our textual debates are machine-readable.

Integrando Google Spreadsheet con R para ARS de etiquetas

Integrating Google Spreadsheet/Apps Script with R: Enabling social network analysis in TAGS

By Martin Hawksey

Increasingly I find myself creating Twitter hashtag archives using my TAGS Google Spreadsheet template as a means to identify who in that community has the most influence and ultimately use this intelligence to target people that might be able to help me disseminate my work. Marc Smith at the Social Media Research Foundation has a useful overview on ‘How to build a collection of influential followers in Twitter using social network analysis and NodeXL’.

I don’t go to the extreme of seeking people to follow and gaining influence with retweets, I usually just follow interesting people who follow me, but the post introduces the important concept of:

  “betweenness centrality” – a measure of how much a person acts a bridge between others.

(betweenness centrality (BC) was a big turning point in my interest and understanding of social network analysis, a moment captured by Sheila MacNeill)

To date the only way I could calculate BC on an archive of tweets was to download the data to my desktop, run it through NodeXL and review the data. This isn’t ideal as the data becomes orphaned. I have experimented with calculating BC using Google Apps Script using a modified version of some PHP code put together by Jonathan Cummings, but kept hitting timeouts before I could get anything back.

I forgot about pursuing this angle until that is I saw Tony Hirst’s A Quick View Over a MASHe Google Spreadsheet Twitter Archive of UKGC12 Tweets in which he uses the statistical computing and graphing tool ‘R’ to read a spreadsheet of archived tweets and produce some quick summary views (I highly recommend you read this post and also check the contribution from Ben Marwick in the comments). Reading this post made me think if it is that easy to read and analyse data using R could you also not somehow push the results back.

Fortunately, and I do mean fortunately, I have no experience of R, R Script, R Studio (I like having no preconceived ideas of what new tools can do – it far more rewarding to throw yourself into the unknown and see if you make it out the other side), but I do know a lot about Google Apps Script giving me a destination – just no way of getting there.

The idea, I think, is ingeniously simple. Read data, as Tony did, process it in R and then using Apps Script’s feature to be published as a service to simply POST the data back to the original spreadsheet.

As that is quite complicated I’ll recap. Fetch a Google Spreadsheet as a *.csv, do something with the data and then push the data back in the same way that you post a web form (and if you skipped the link the first timePOST the data back to the original spreadsheet).

Having sunk a day of my own time (and it is my own time because I get paid for the OER Visualisation project for the hours I work on it), I’m not going to go into the details of how to setup R (or in my case RStudio) to do this – hey I learned it in a couple of hours so can you – instead I’ll give you the bits and pieces you need and general instructions.  Before I start you might want to see if the result is worth it so here’s a sheet of SNA stats for the #ukgc12 archive.

Playing with some test data

To make it easier I start with a partially complete dataset. The scenario is I’ve got my archive and run options 1-3 in the TAGS – Advanced menu to get an Edges sheet of friend/follower information.

1. Open this Google Spreadsheet and File > Make a copy (this is a fully functioning – if I haven’t broken it of the next version of TAGS so if you clear the Archive and setup you can start collecting and using this with your own data).
2. Once you’ve copied select File > Publish to the web and publish the spreadsheet
3. In the new spreadsheet open Tools >  Script editor.. and Run > Setup (this get a copy of the spreadsheet id need to run as a service – in the normal scenario this is collected when the user authenticates the script with Twitter)
4. Open Share > Publish as service..  and check ‘Allow anyone to invoke’ with ‘anonymous access’, not forgetting to ‘enable service’. You’ll need a copy of the service URL for later on. Click ‘Save’ 
   
5. Back in the script editor on line 57 enter a ‘secret’ – this will prevent anyone from uploading data will in anonymous mode (you can choose to only enable the service when required for extra security.
6. Open your install of R and load a copy of this script.
7. There are four things to edit in this script
   1. key – spreadsheet key, the bit after https://docs.google.com/spreadsheet/ccc?key= and before the &hl… junk
   2. gid – the sheet number of the Edges sheet, unless you insert/use a different sheet should always be 105 for a TAGS spreadsheet
   3. serviceUrl – the url you got in step 4
   4. secret -  the same secret you entered in step 5
8. You might also need to install the packages used – most of them are standard but you may need to get igraph – used to get all social network data
9. Run the R script – it may take some time to read a write to Google Spreadsheets so be patient

That’s it. If you go back to the spreadsheet (you may need to refresh) the SNA Metrics and Vertices sheets should be populated with data generated from R

The Apps Script Magic

Here’s the Google Apps Script snippet used to handle the data being pushed from R:

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// Google Apps Script Snippet for POST handling data from R function doPost(e){ // PART OF SCRIPT WHICH HANDLES DATA POSTED FROM R // To use complete steps 1 -3 from TAGS - Advanced menu and File > Publish to the web.. // From this window select Share > Publish as service.. allowing anyone to invloke annonymously // Download the R script from https://gist.github.com/1682306 and modify variables for the key, sheet, urls and common secret set below // Run the R script and values will be entered into the spreadsheet var secret ="FzWGlpUkt1Tmc"; //must match secret in R script if (e.parameter.secret == secret){ var ss = SpreadsheetApp.openById(ScriptProperties.getProperty('active')); var sheet = ss.getSheetByName("Vertices"); // read the existing data from Vertices sheet var data = {} var datagrid = Utilities.jsonParse(e.parameter.datagrid); // handling datagrid object made in R var datagridlabels = Utilities.jsonParse(e.parameter.datagridlabels); // list of screen_names that match datagrid made in R for (i in datagrid){ datagrid[i].screen_name = datagridlabels[i]; data[datagridlabels[i]]=datagrid[i]; // index objects with screennames } var toptens = Utilities.jsonParse(e.parameter.toptens); // collect toptens list made in R var labels = Utilities.jsonParse(e.parameter.toptenslabels); //list of names of things calculated in R var exdata = getRowsData(sheet); // read the existing data from Vertices sheet for (i in exdata){ // add new data to existing data if (data[exdata[i].screen_name] != undefined){ for (j in labels){ exdata[i][labels[j]] = data[exdata[i].screen_name][labels[j]]; } } } setRowsData(sheet,exdata); // write individuals stats back to sheet var sheet = ss.getSheetByName("SNA Metrics"); // add the top ten's to SNA Metrics sheet var topsData = chunk(toptens,10); // probably could have found better way of writting sheet.getRange("A4:C13").setValues(topsData[0]); sheet.getRange("A17:C26").setValues(topsData[1]); sheet.getRange("A30:C39").setValues(topsData[2]); sheet.getRange("A43:C52").setValues(topsData[3]); sheet.getRange("A56:C65").setValues(topsData[4]); sheet.getRange("A69:C78").setValues(topsData[5]); } }

view rawz TAGS 3.1Snippet.js hosted with ❤ by GitHub

I’ve commented most of it so you can see what is happening. While Apps Script has a debugger which lets you monitor execution and variables it can’t intercept the POST so I used the original POST/GET code to dump the data into some cells then tweaked the script to read it from there to work out what needed to be done.

Final thoughts

I think this is a powerful model of reading selected, processing and then uploading data back to the source. I’m also only using the very basics of igraph and sure much more could be done to detect neighbourhoods, clusters and more. Also I wonder if more of the friendship data collection could be done in R with the TwitteR – (you R people really know how to make it hard to find info/help/support for your stuff ;) Right now I can get friend/follower info for a list of 250 users.

The intriguing aspect is just how much data can you push back to Apps Script and as there is a long list of Services could you also handle binary data like chart images (perhaps down the Blob and then Document Service route, or maybe just straight into Charts).

I welcome any comments you have about this technique and particularly value any feedback (I’m not a SNA expert so if there are errors in calculation or better measures I would welcome these)