This link has been bookmarked by 26 people . It was first bookmarked on 12 Oct 2008, by Jesus Hoyos.
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08 Jan 11
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Gabriela GrossecktwInfluence is a simple tool for measuring the combined influence of twitterers and their followers, with a few social network statistics thrown in as bonus.
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26 Jul 10
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rootwoman 123twInfluence is a simple tool using the Twitter API to to measure the combined influence of twitterers and their followers, with a few social network statistics thrown in as bonus.
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02 Dec 08
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28 Oct 08
Kevin MakiceCalculate the indirect influence of you and your followers on Twitter!
Twitter Tools statistics social research socialnetworking analysis reputation delicious
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20 Oct 08
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15 Oct 08
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13 Oct 08
Michel BauwenstwInfluence is a simple tool using the Twitter API to to measure the combined influence of a Twitterer and his/her followers, with a few social network statistics thrown in as bonus.
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12 Oct 08
Jesus HoyostwInfluence is a simple tool using the Twitter API to to measure the combined influence of a Twitterer and his/her followers, with a few social network statistics thrown in as bonus.
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11 Oct 08
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Michelle A. HoyletwInfluence is a simple tool using the Twitter API to to measure the combined influence of a Twitterer and his/her followers, with a few social network statistics thrown in as bonus.
We know that "A-List" Twitterers like Scoble, LeoLaporte, and BarackObatwitter twittertools socialnetworking analysis socialcapital
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Howard RheingoldtwInfluence is a simple tool using the Twitter API to to measure the combined influence of a Twitterer and his/her followers, with a few social network statistics thrown in as bonus.
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twInfluence is a simple tool for measuring the combined influence of twitterers and their followers, with a few social network statistics thrown in as bonus.
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We know that "A-List" Twitterers like Scoble, LeoLaporte, and BarackObama have a lot of influence on Twitter, because they have tens of thousands of followers. However, social network analysis teaches us that there is a "horizon of communication" that extends beyond your own direct contacts, and this is demonstrated whenever somebody "retweets" a message. The significance is that not all followers are equal.
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The Twitter API requires authentication to accomplish a couple of the tasks that twinfluence performs.
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Once a twitterer is analyzed, scores are cached for 48 hours before they will be recalculated in order to keep repeat requests to the Twitter API to a minimum (the cache is increased to two weeks for particularly large twitterers with 30,000 or more followers).
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Twinfluence only collects information on a twitterer's followers, and the raw follower counts for each of them. It does not crawl out and collect each followers' actual networks, comparing all of their connections to create a true bidirectional social network graph.
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From the perspective of graph theory, a Twitterer's followers would be considered their first-order network, and their "followers count" the same as their "degree". "Degree" is a simple form of centrality measurement that equates to "prestige" or "popularitiy"; different types of centrality can measure connectivity, authority, and control in a network.
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Reach is the number of followers a Twitterer has (first-order followers), plus all of their followers (second-order followers). In the diagram above, the reach would be 27 (there are 28 nodes, including the Twitterer).
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Reach is a measurement of potential audience and listeners, a best estimate of the number of people that a given Twitterer could quickly get a message to.
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Velocity merely averages the number of first- and second-order followers attracted per day since the Twitterer first established their account. The larger the number is, the faster that Twitterer has accumulated their influence.
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the more followers you get, the faster you get them, and the faster your reach builds through sort of a "snowball" effect!
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A high value indicates that most of that Twitterer's followers have a lot of followers themselves. Social Capital is scored from "very low" to "very high" relative to other twitterers at your network size.
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The following chart is generated dynamically and shows that as twitterers build their follower network, their social capital tends to start very high, build for a while, then slowly decrease. This is probably because as most people start tweeting, they follow a few high-profile twitterers who may reciprocate. Over time, however, they attract more and more people - and that means more and more people with few followers, including bots, spammers, and silent followers.
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In social network analysis, a high centralization indicates dependency of the network on just a few nodes to maintain the connectivity of the entire network. Twitterers with low centrality networks would not have their reach greatly reduced if a few high-profile people stopped following them. Centralization is scored from "very fragile" to "very resilient" relative to other twitterers at your network size, implying that a network with only a few high-profile followers is very sensitive to collapsing if those followers leave.
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he more people you follow, the more time you have to spend reading and filtering tweets.
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