On an absolute basis, Twitter gets more visitors in the United States than anywhere else in the world. But if you handicap it by the amount of people on the web in each country, the United States isn't the biggest, says comScore. Below is Twitter's reach by country. This represents the amount of unique visitors to Twitter.com as a percentage of the countries overall users.

As you can see, Brazil is number one. The U.S. is twelve.

6.7% of all US online ad spending to go toward social networks this year

Recent work done at HP Labs, the exploratory and research group for Hewlett Packard, shows what most of us suspected as being true all all along: that just because a person has a lot of followers, it doesn’t mean they have a lot of influence.

In September 2009, using an algorithm they devised called the IP (Influence/Passivity) algorithm, a team of researchers from HP Labs continuously queried the Twitter Search API for 300 straight hours for all tweets containing the string of letters 'http'. Finding this string in a tweet would indicate the presence of a URL, and demonstrate that a web page was being shared or retweeted by means of a link.

In that time period, they acquired 22 million tweets with URLs present. This accounted for 1/15th of the entire activity of Twitter at the time. The URLs were checked for validity, and by revisiting the Twitter API they could determine who the user for each URL was, and in particular who their followers and followees were as well. From that information, a complete social graph was constructed from the dataset generated by the users sampled.

The research team worked on the following assumptions which are taken from their report "Influence and Passivity in Social Media":

• A user’s influence score depends on the number of people she influences as well as their passivity.
• A user’s influence score depends on how dedicated the people she influences are. Dedication is measured by the amount of attention a user pays to a given user as compared to everyone else.
• A user’s passivity score depends on the influence of those who she’s exposed to but not influenced by.
• A user’s passivity score depends on how much she rejects other user’s influence compared to everyone else.

A whole industry has grown up around Twitter with the aim of developing various tools that enable Twitter users to increase their number of followers. But now all these efforts seem to have been in vain. An average Twitter user retweets only one in 318 URLs. It seems most users are passive information consumers, and do not forward the content to the network at any kind of rate that could be described as 'little more than partially engaged'. Consequently, having a large follower count is not a lot of use from a message propagation perspective if most of the followers are made up of these passive users.

If you want to be a person of influence on Twitter, then the way to do it is to acquire engaged followers who are themselves active on Twitter. That would at the very least mean being active and engaged yourself.

Of course, this makes life difficult for marketers and others engaged in viral activity who want to take advantage of the enormous reach that Twitter has. They can no longer rely on a single dubious metric, follower count, as a guide to how far their message gets out.It also means that to find active and engaged people, they will have to become active and engaged themselves. Fun, maybe. Time-consuming, certainly. But it is only through interacting with highly connected people that they will be able to propagate themselves and their message through the social network. Through the process of finding the most influential people on Twitter, the team also managed to turn up the most passive users on the service as well. The majority of these users tended to be spammers and robot users. It is as important to identify the highly-passive Twitter user because “they provide a barrier to propagation that is often hard to overcome.” It’s good to know where the Twitter dead ends are as it gives us a useful benchmark to contrast with someone who is influential. This information aids navigation through the vastness of the Twitter network, and knowing where not to go can be every bit as useful as knowing where to go.

The HP Lab report finishes with the following conclusion:

“This study shows that the correlation between popularity and influence is weaker than it might be expected. This is a reflection of the fact that for information to propagate in a network, individuals need to forward it to the other members, thus having to actively engage rather than passively read it and cease to act on it. Moreover, since our measure of influence is not specific to Twitter it is applicable to many other social networks. This opens the possibility of discovering influential individuals within a network which can on average have a further reach than others in the same medium regardless of their popularity.” In a way, it is understandable that the findings from this research on Twitter would be applicable to other social networks as well. Influential people tend to be influential wherever they are. The IP algorithm that has been developed by the HP Labs team is going to be very useful across all of the social media domains, wherever people gather and exchange ideas and news via links.The key metric to determining how effective any given person is in propagating information is to measure how often a URL that they tweet or retweet is clicked. And it is important to allow for the fact that not everyone is very adept at giving credit for their shared links, and some shared links go beyond the Twitterverse on to other services.But unless you have access to a given individual’s bit.ly account or other some search service which keeps tabs on retweets, the only sure way to know if they are a person of influence is to get to know them.

Happy twittering.

Daily users comment and upload content at least twice as much as average. eMarketer estimates there are 26 million monthly users of Twitter in 2010. That makes users of the microblogging service a relatively small minority of internet users, at 14.6%, and daily users are naturally even fewer in number. But their voice is disproportionately loud. According to ExactTarget, daily Twitter users are highly active across the social web. They are about three times as likely as internet users on average to upload photos, four times as likely to blog, three times as likely to post ratings and reviews, and nearly six times as likely to upload articles.

They create, share and comment on content at high rates, making them valuable to marketers for much more than their potential influence on Twitter alone.

“Consumers active on Twitter are clearly the most influential online,” said Morgan Stewart, principal at ExactTarget’s research and education group, in a statement. “What happens on Twitter doesn’t stay on Twitter. While the number of active Twitter users is less than Facebook or email, the concentration of highly engaged and influential content creators is unrivaled—it’s become the gathering place for content creators whose influence spills over into every other corner of the internet.” ExactTarget also explored Twitter users’ motivations for following companies and brands on the service. In a deeper drilldown into consumer sentiment than previous research has conducted, the April 2010 study supported the general findings that microbloggers have many reasons to follow brands they like. While discounts and sales are toward the top of the list, finding out news and information about the company and its products come out ahead.

According to 360i, 75% of marketers’ tweets are informational, suggesting brands are responding to what consumers want—though they largely neglect to participate in conversations. Such deeper engagement might help them harness the power of frequent Twitter users across their other social activities as well.

A group of researchers from Northeastern and Harvard universities have gathered enough data from Twitter to give us all a snapshot of how U.S. residents feel throughout a typical day or week.Not only did they analyze the sentiments we collectively expressed in 300 million tweets over three years against a scholarly word list, these researchers also mashed up that data with information from the U.S. Census Bureau, the Google Maps API and more. What they ended up with was a fascinating visualization showing the pulse of our nation, our very moods as they fluctuate over time.

The researchers have put this information into density-preserving cartograms, maps that take the volume of tweets into account when representing land area. In other words, in areas where there are more tweets, those spots on the map will appear larger than they do in real life. It will surprise almost no one to learn that there is a general mood slump mid-day and mid-week, when we are most likely to be at work. Our tweets show that we’re happiest in the early morning and late evening; during the week, our mood tends to peak on Sunday morning. Less predictable, perhaps, is the fact that West Coast tweets were “happier” than tweets from the East Coast. Although West Coast Twitter users expressed emotions in the same cycles as the East Coast users (with a three-hour gap, of course, because of time zone differences), the West Coasters didn’t dip as low in mood as the East Coasters by a significant margin.

For the infographic fans among you, here’s a lovely visualization of some of the data displayed:

You can also check out a cool video below that illustrates how Twitter mood expressions change over the course of a day in the U.S. We’re pretty fascinated by visualizations like these; what other data or topics would you like to see these researchers tackle next? What use do you think we could get out of the current information they’ve generated?

via : http://claudiamai.posterous.com/time-lapse-twitter-visualization-shows-americ-2