ARS: Análisis de la campaña online del NYT

Florida and Ohio: The deciding factors in the race to the White House

I have been using Condor to do Social Network Analysis for the last few weeks to get some insights into people's thoughts on Barack Obama and Mitt Romney. Since the race is so close, I was getting inconclusive results. Both candidates were equally popular and had very similar sentiments going for them.

This morning I saw a beautiful info graphic on The New York Times website that showed the various possible paths to victory for both the candidates depending on which swing states they could win. After playing around with it for some time I realized that if Romney loses Florida, he would need to win all the other swing states (8 of them) to get to the white house. Moreover, we also know that no Republican has ever won the presidential election without winning Ohio. So here's the bottom line: If Romney is to win the election he pretty much needs to win both Ohio and Florida.

Taking this into consideration, I thought I'd restrict my cool hunting to just Ohio and Florida today. I have collected twitter feeds referencing Obama and Romney from Ohio and Florida only using Condor's twitter collector and restricting the collection using geocodes. Here are the results,

Ohio

Following image shows the network of people talking about Romney and Obama

We can see that more people are talking about Obama than about Romney. i.e. in SNA terminology, Obama has a higher betweenness centrality.

The following image shows the sentiment analysis for Obama,

The dots above the 0 represent positive sentiment and those below the 0 represent negative sentiment. The sentiment is definitely slightly positive overall.

The following image shows the sentiment analysis for Romney,

Romney also seems to have a slightly positive sentiment overall.

Now lets look at what terms appeared in the tweets the most.

For Obama,

"vote", "voting" and "tomorrow" are the most prominent terms.

For Romney,

"vote", "voting", "voting" are prominent, but "jobs" and "republicans" are even more prominent. Are people talking about the auto bailout which saved jobs in Ohio? I am not sure. But if they are, then its good news for Obama.

I used Condor's taxo tool to look for terms pertaining to emotions that appeared in the tweets. Here's Obama's analysis,

Of all the terms that appear 22% are related to purely positive emotions (hope, love, compassion, victory, etc) and 12.8% are purely negative (disgust, fear, etc)

Here's Romney's analysis,

Of all the terms that appear 24% are related to purely positive emotions and 12% are related to purely negative emotions.

Conclusions:
* Romney seems to be doing a bit better than Obama in terms of emotions, but not by much.
* Overall Obama is more popular than Romney in Ohio.
* There is no clear winner still. But since Obama is more popular than Romney and their sentiment and emotion analysis are quite similar, I am predicting a close Obama victory.

Florida

I did the same analysis as above for Florida. Here is a summary of the findings,

* Obama is more popular on twitter than Romney in Florida as well.
* Both Obama and Romney have similar sentiment profiles, slightly leaning towards positive.
* The prominent terms in  Obama related feeds: vote, voting, voted, tomorrow
* The prominent terms in Romney related feeds: vote, voting, win, wins, lol.
* Obama emotion analysis: 23.7% positive, 12.3% negative
* Romney emotion analysis: 24.3% positive, 11.3% negative

Conclusions:
* Romney is again marginally better than Obama in terms of emotions.
* Overall Obama is more popular than Romney.
* Again, there is no clear winner. But since Obama is more popular, I am predicting a close Obama victory.

Bottom line: Since the race is so close, my predictions are only 50% accurate. At this point, popularity is the only differentiating factor between the 2 candidates on twitter. I am curious to see when all else remains equal, does popularity alone determine a candidate's chances of winning. We will find out soon. :-)

Following images were created from Florida's data, similar to those created for Ohio earlier in the post:

Popularity:

 Obama's terms:

 Romney's Terms:

Obama's Emotions:

Romney's Emotions:

Género en las redes sociales digitales

Study: Males vs. females in social networks

Have you ever wondered how many of Twitter’s users are women? Or men? What about Facebook, MySpace, Digg, LinkedIn, and other sites in the social media sphere?

We have tracked down this information for a number of social network sites (19 of them). All the major ones have been included, like Facebook, MySpace and Twitter and also some of the most popular social news sites; Digg, Reddit and Slashdot.

Full list of sites in this mini study: Facebook, LinkedIn, MySpace, Twitter, Slashdot,Reddit, Digg, Delicious, StumbleUpon, FriendFeed, Last.fm, Friendster, LiveJournal, Hi5,Imeem, Ning, Xanga, Classmates.com, Bebo.

To determine the ratio between male and female users on these sites we used site demographics data for the United States gathered from Google’s Ad Planner service.

Male/female site user statistics

Before we move on to the chart, here are a few quick observations based on the results we got.

• 84% (16 out of 19) of the sites have more female than male users.
• The social news sites Digg, Reddit and Slashdot have significantly more male users than female. The standout here is Slashdot which takes male geekdom to new heights with 82% male users. 
• If we hadn’t included the three social news sites, all of the sites would have had more females than males.
• Twitter and Facebook have almost the same male-female ratio; Twitter with 59% female users and Facebook with 57%.
• The most female-dominated site? Bebo (66% female users), closely followed by MySpace and Classmates.com (64%).
• The average ratio of all 19 sites was 47% male, 53% female.

And here’s a chart with the male/female ratio for all the sites, for your viewing pleasure:

Pingdom

Dime que edad tienes y te diré que red social usas

Study: Ages of social network users

How old is the average Twitter or Facebook user? What about all the other social network sites, like MySpace, LinkedIn, and so on? How is age distributed across the millions and millions of social network users out there?

To find out, we pulled together age statistics for 19 different social network sites, and crunched the numbers.

Full list of sites in this study: Facebook, LinkedIn, MySpace, Twitter, Slashdot, Reddit,Digg, Delicious, StumbleUpon, FriendFeed, Last.fm, Friendster, LiveJournal, Hi5, Tagged,Ning, Xanga, Classmates.com, Bebo.

To get consistent age data for the various sites we used site demographics information for the United States gathered from Google’s Ad Planner service and then did some additional calculations to get all the data we needed.

Social network age distribution

What is the age distribution in the social media sphere?

We took the age distribution data we had collected and calculated what the age distribution looked like across all 19 sites counted together. The resulting chart is right here below.

A full 25% of the users on these sites are aged 35 to 44, which in other words is the age group that dominates the social media sphere. Only 3% are aged 65 or older.

That was the age distribution when looking at these 19 sites together. When looking at individual social network sites, the differences are significant, as you will see below.

Age distribution per site

Here below you can examine the age distribution for each of the 19 social network sites we included in this study. The list has been sorted by the average user age per site (see further down for that), with the “youngest” site showing at the top and the “oldest” at the bottom.

Some observations on age distribution:

• Bebo appeals to a much younger audience than the other sites with 44% of its users being aged 17 or less. For MySpace, this number is also large; 33%.
• Classmates.com has the largest share of users being aged 65 or more, 8%, and 78% are 35 or older.
• 64% of Twitter’s users are aged 35 or older.
• 61% of Facebooks’s users are aged 35 or older.

Dominant age groups

Most of the social networks we included are dominated by the age group 35-44, which was apparent in the first chart in this article. This group has become the most “social” age group out there. This is the generation of people who were in their 20s as the Web took off in the mid ‘90s.

If we look at which age groups are the largest for each site, we get the following distribution:

• 0 – 17: Tops 4 out of 19 sites (21%)
• 18 – 24: Tops no site
• 25 – 34: Tops 1 out of 19 sites (5%)
• 35 – 44: Tops 11 out of 19 sites (58%)
• 45 – 54: Tops 3 out of 19 sites (16%)
• 55 – 64: Tops no site
• 65 or older: Tops no site

It’s a bit surprising that not one single site had the age group 18 – 24 as its largest, but that can be explained by this interval being a bit smaller than the other ones (it spans seven years, not 10 as most of the others). That the two oldest age groups don’t top any of the sites probably doesn’t surprise anyone, though.

Average user age per site

As we promised in the introduction, we have calculated an estimate of the average age for each of the social network sites included in this study. The result is here below.

A few observations:

• The average social network user is 37 years old.
• LinkedIn, with its business focus, has a predictably high average user age; 44.
• The average Twitter user is 39 years old.
• The average Facebook user is 38 years old.
• The average MySpace user is 31 years old.
• Bebo has by far the youngest users, as witnessed earlier, with an average age of 28.

On the social web, age is a factor

Although we can’t say how this will change over time, at the moment the older generations are for one reason or another (tech savvy, interest, etc.) not using social networking sites to a large extent. This probably reflects general internet usage, but we suspect the difference is enhanced when it comes to the social media sphere where site usage tends to be more frequent and time-consuming than usual.

It is also noteworthy that social media isn’t dominated by the youngest, often most tech-savvy generations, but rather by what has to be referred to as middle-aged people (although at the younger end of that spectrum).

Pingdom

17 formas de visualizar tu red de Twitter

17 Ways to Visualize the Twitter Universe

by Nathan Yau

Flowing data

I just created a new Twitter account, and it got me to thinking about all the data visualization I've seen for Twitter tweets. I felt like I'd seen a lot, and it turns out there are quite a few. Here they are grouped into four categories - network diagrams, maps, analytics, and abstract.

Network Diagrams

Twitter is a social network with friends (and strangers) linking up with each other and sharing tweets aplenty. These network diagrams attempt to show the relationships that exist among users.

Twitter Browser

Twitter Social Network Analysis

The ebiquity group did some cluster analysis and managed to group tweets by topic.

Twitter Vrienden

Twitter in Red

I'm not completely sure how to read this one. I looks like it starts from a single user and then shoots out into the network.

Twitter Network

Maps

When you create a Twitter account, you can enter where you are located, so in my case, I put New York. Because Tweets often have location attached to them, maps naturally lend themselves to tweet visualization.

TwiterVision

Yeah, it's a Google Maps mashup, but a bit better than what you're used to seeing.

TwitterVision3d

It's TwitterVision taken to the next dimension.

Analytics

Maybe you don't care so much about the relationships or locations, but what you're really after is what everyone is Twittering about. These analytic visualization serve as a Twitter zeitgeist.

TweetStat

TweetVolume

TwitStat

TwitterMeter

Abstract

They're not quite maps, not quite network diagrams, and not quite analytic tools. Rather they all follow some metaphor and encourage exploration.

24 o'clocks

Is it just me, or does this sound like a really good name for a band?

TweetPad

Created at the Visualizar workshop, it's actually more than just curves. In fact the blobbies are meant more for background while the main event is playing with the tweets.

Twitter Fountain

Twitter Blocks

Created by the folks at Stamen. I posted about Blocks when it came out.

TwitterPoster

TwitterVerse

A lot, huh? All of these were made possible by the Twitter API that allows developers to access Twitter data for free. Did I miss any other Twitter visualizations? Please leave the link in the comments below.

Follow Me On Twitter

So now that you know what Twitter looks like, you can head on over and "follow" me; or if you don't have an account yet, you can create one in a few seconds.

If you don't know what Twitter is or wondering what the point is, here's a short video explaining Twitter in "plain English."

Share this:      

Minería en Twitter usando NodeXL

Twitter network analysis and visualisation II: NodeXL – Getting started with the @WiredUK friends network

Fuente

The other tool that I got wind of just after SocialBro was Network Overview, Discovery and Exploration for Excel – NodeXL . As indicated in the title NodeXL is an add-on for Microsoft Excel (Windows version) but the code is free and open source. Here’s the description from the website:

For a while I’ve been admiring Tony Hirst’s work visualising large networks like Twitter communities using the open source and cross-platform tool Gephi. Tony has lots of great posts for getting you started with Gephi including Visualising Twitter Friend Connections Using Gephi: An Example Using the @WiredUK Friends Network .

I’d been put off cooking something up myself until now because a) Tony has been doing a great job and I couldn’t see what I could add b) large network visualisations need large amounts of data (Tony has previously published his Twitter Community Grabbing Code – newt.py , but as I’m not whitelisted with the Twitter API I only get 350 hits/hr and not 20,000 which can be somewhat of a hindrance when getting follower relationships).

The advantage of NodeXL, particularly for graphing Twitter communities, is it has built-in features for grabbing the data for you. Not only that the coding is clever enough to handle the data collection for mere mortals, so when you hit your rate limit NodeXL waits until it should be able to get more data. NodeXL also has “built-in connections for getting networks from Flickr, YouTube, and your local email. Additional importers for Exchange Email , Facebook , and Hyperlink networks  are available”.  

To let you see how to use NodeXL and to allow me to make comparisons with Gephi I thought I’d re-run Tony’s WiredUK example (besides why should I break my habit of only ever building on Tony’s work ;).

In Tony’s original post the beginning (getting the data) is at the end. Fortunately with NodeXL we can start here. I’m assuming you’ve downloaded and installed NodeXL so we begin by starting a new template – I do this from the Windows Start menu and selecting the NodeXL Excel Template shortcut from the Microsoft NodeXL application folder. From the NodeXL ribbon select Import > From Twitter Users’s Network. In the import dialog box enter:

• Get the Twitter Network of the user with the username: wiredUK
• Add a vertex for each: Person followed by the user
• Levels to include: 1.5
• and what level of authentication you want to use

 

Once the data has been collected (you can see updates in the status bar of the import dialog box), when you click  ‘Show Graph’ you’ll get the raw form:

 

At this point Tony highlights that:

Sometimes a graph may contain nodes that are not connected to any other nodes. (For example, protected Twitter accounts do not publish – and are not published in – friends or followers lists publicly via the Twitter API.) Some layout algorithms may push unconnected nodes far away from the rest of the graph, which can affect generation of presentation views of the network, so we need to filter out these unconnected nodes. The easiest way of doing this is to filter the graph using the Giant Component filter.

NodeXL has some ‘Dynamic Filters’ that include bounding the graph by x and y which could be used to crop the image, but I couldn’t find a component filter

 

Next Tony colours the graph using “the modularity statistic. This algorithm attempts to find clusters in the graph by identifying components that are highly interconnected.” NodeXL doesn’t have a built-in function for calculating ‘modularity’ but we can cluster nodes into groups using other algorithms, in this case Clauset-Newman-Moore. From the Groups menu make sure this algorithm is selected then click ‘Group by Cluster’

 

When you Refresh Graph you’ll see the nodes have been colour coded as per group.

  

If you navigate to the Groups sheet there is a column where this colour is set (the right-click to set the colour doesn’t work for me but with the cell highlighted you can use the color picker within the Visual Properties part of the ribbon (top-right of the screenshot below)):

 

In Tony’s example he says: “While we have the Statistics panel open, we can take the opportunity to run another measure: the HITS algorithm. This generates the well known Authority and Hub values which we can use to size nodes in the graph.” NodeXL doesn’t have a statistics panel as such but can calculate some but not as many metrics.

 

Next Tony looks at graph layout. In NodeXL there aren’t as many options but enough to get started with (I stuck with Fruchterman-Reingo). To add Twitter IDs and have a varying node size we Autofill the Visual Properties. As NodeXL doesn’t have a HITS algorithm I’m using Betweeness Centrality (for an explanation of this see Sheila MacNeill’s Betweenness Centrality – helping us understand our networks  post).

 

Within the Graph Options there are some further adjustments you can do like changing the joining lines to curves and adjusting the label font (unfortunately the font-size is fixed, it’s just the node icon that scales relative to the betweenness centraility.

 

It’s still hard to see what is going on, but we have some more layout tricks. To start with we can layout graphs for groups in separate boxes and also adjust the strength of the repulsive force.

  

Once you’re happy if you right click on the graph there is an option to save it as an image.

 

And here is the final result

 

and for comparison here’s what Tony produced

 

Which is better Gephi or NodeXL? For entry level (if such a thing exists given the number of different algorithms and theories in network analysis) NodeXL ticks a lot of the boxes. Its easy to grab data and do basic processing. If you want to do more you might want to switch to Gephi. The good news is NodeXL can export the data files in Gephi supported formats so potentially you can get the best of both worlds.

Difusión vía grupos conectados

Wireless Companies Could Use your Friends
Mobile carriers might get marketing insights from studying whom you call and what device you use.

• By Tom Simonite 

Web connections: Social networks of iPhone users in late 2007 are shown in this diagram. It illustrates that many are socially connected to only a few fellow iPhone users

Each time you make a cell-phone call, your network provider knows whom you're calling, for how long, and what device you're using. Now researchers at one of the world's largest wireless carriers are exploring whether such information can help companies target their marketing pitches.

By analyzing billions of call records, the researchers at Telenor, a carrier in Scandinavia, mapped how social connections between people--measured partly by how often they called each other--correlated with the spread of Apple's iPhone after its 2007 debut. The research showed that socially connected groups of early adopters helped the iPhone spread rapidly. A person with just one iPhone-owning friend was three times more likely to own one themselves than a person whose friends had no iPhones. People with two friends who had iPhones were more than five times as likely to have sprung for the Apple device.

Now Telenor's team wants to translate insights like that into marketing campaigns. For instance, a company might send promotional text messages or ads to people whose friends already use a product--and who would presumably be more likely to buy the product as well.

"Marketing strategies based on this kind of analysis would be both useful and powerful compared to conventional ones," says Pal Sundsoy, one of the Telenor researchers. They collaborated with Rich Ling at the IT University of Copenhagen, Denmark, and will present their work at the International Conference on Advances in Social Networks Analysis and Mining next month.

They tracked the iPhone's spread using Telenor's database of CDRs--call detail records. A CDR is generated by every voice or text-message connection. Among other things, it contains the origin and destination number, the duration of a connection, and the unique mobile equipment identifier of the caller's handset. And rather than merely counting the growing number of iPhone users over time, the Telenor researchers performed what is known as social-network analysis. They examined the strength of the links between people by examining the number and duration of their calls and text messages.

Not only did they find that the spread of iPhones was greatly influenced by social circles, but also that this pattern differed from the way other devices and services rippled through the customer base. For instance, other Telenor products, including the Doro, a simple handset generally marketed at the elderly, did not exhibit the same kind of network effects. Neither did a video-calling feature introduced by Telenor in 2007. The video-calling service grew in popularity at first but suffered after a new pricing model was introduced.

Model image: This diagram shows the evolution of the largest network of Telenor iPhone users over time. Each node represents one subscriber, and its color indicates the model used. In this case, red equals 2G, green means 3G, and yellow means 3GS.

Cell-phone networks have some of the best data for developing such ideas about how to target marketing campaigns, says Cristophe Van den Bulte, an associate professor of marketing at the Wharton School of the University of Pennsylvania who uses social-network analysis to study the diffusion of products.

"They have clean, relevant data because people don't make calls to just anyone," he explains. "Other network connections can be close to meaningless--for example, my relationships with my Facebook friends."

One possible avenue for further research would be to identify which people are most able to persuade their connections to adopt a new product, Van den Bulte says. He has tried to study such patterns in the way doctors choose medical products. Alternatively, instead of trying to find trendsetters, he says, "you may actually want to target people connected to others that are easy to influence. That's an approach that needs to be looked at more."

Privacy regulations could be an obstacle. The Telenor data had to be "anonymized," or stripped of identifying information, before the researchers could use it. Privacy law in Europe in particular could make it hard, Van den Bulte says, for wireless companies there to sell marketing insights based on social-network analysis to other companies.

MIT Technology Review

Gephi...