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Definitions

Clout, as defined by Twitalyzer, is the relative likelihood that an individual's Twitter username will appear when searched for in Twitter.

Generosity, as defined by Twitalyzer, is the percentage of updates in which a user retweets other people.

Effective Reach multiplies a user and each of their retweeting user's follower count by their calculated influence (the likelihood that that user will be retweeted or mentioned) to determine a likely and realistic representation of any user's reach in Twitter at any given time.

Engagement provides a measure of the type of interaction the user has in Twitter by examining the ratio of people referenced by the user to the number of people referencing them.

Tweet Impressions is an estimate of the exposure of a tweet, term, or hashtag used recently in Twitter. For example, the estimate of impressions of any tweet is the number of followers of the author plus the number of followers in his or her network of followers plus retweeters and their networks.

Followers are the number of followers reported by Twitter for the user the last time an analysis was conducted. If the number of followers looks wrong to you, update the user's profile.

Following is the number of people the user is following as reported by Twitter for the user the last time an analysis was conducted. If the number of people being followed looks wrong to you, update the user's profile.

Hashtags Cited are the number of updates that included at least one hashtag (evidenced by the presence of "#") published in the seven days prior to the last analysis.

Impact, as defined by Twitalyzer, is a combination of the following factors:

  • The number of followers a user has
  • The number of references and citations of the user
  • How often the user is retweeted
  • How often the user is retweeting other people
  • The relative frequency at which the user posts updates

Influence is the likelihood that a Twitter user will either A) retweet something the user has written or B) reference the user. While this definition is similar to clout, influence takes both retweets and references into account, whereas clout only looks at references.

Twitalyzer Influencer Types are a categorization we derive from work done by Lisa Barone (@lisabarone). The five types we track include:

  • Everyday Users have a small circle of influence but great potential
  • Reporters are connected people and great communicators
  • Social Butterflies are very active within their individual networks
  • Trendsetters are early adopters who love to share new ideas
  • Thought Leaders are the voices people listen to most

For more information about Lisa Barone's five influencer types we recommend reading "Five Types of Influencers on the Web".

Klout's K Score is a measure of the impact of opinions, links and recommendations across an individual's social graph. This score is calculated and provided by our friends at Klout.

Lists reports the number of Twitter lists the user was a member of when the last analysis was conducted. If the number of lists looks wrong to you, update the user's profile.

Potential Reach sums a user's follower count and the sum of followers for any user retweeting any of their Tweets during the previous seven days to estimate the total potential reach in Twitter at any given time.

Referenced reports the number of times the user had been referenced or cited by other people in the seven days prior to the last analysis based on the presence of a “@” proceeding their Twitter username.

Referencing Others reports the number of times the user wrote or responded directly to other people as indicated by the presence of an @username at the beginning of the update.

Retweeted reports the number of times the user had been retweeted by other people as indicated by the use of "rt @", "rt@", "rt:@", "rt: @" , "retweet @", "via @", "retweet :@", or "r/t" in the seven days prior to the last analysis.

Retweeting Others reports the number of times the user had retweeted other people as indicated by their use of "rt @", "rt@", "rt:@", "rt: @" , "retweet @", "via @", "retweet :@", or "r/t" in the seven days prior to the last analysis.

Retweets per 1,000 Followers normalizes the number of retweets of the user relative to their follower base.

References per 1,000 Followers normalizes the number of references to the user relative to their follower base.

Retweet Ratio reports the relationship between retweets of the user to retweets of others by the user.

Reference Ratio reports the relationship between references of the user to references to others by the user.

Signal, also referred to as "Signal to Noise Ratio", indicates that one of the following is present in measured updates:

  • A hyperlink containing "http://"
  • A reference to another person as evidenced by the "@" symbol
  • A hashag as indicated by the "#" symbol
  • Some type of indicator that the update is retweeting someone else

Twitter Ratio is the ratio of followers to people the user is following. The Twitter Ratio has been characterized as a way to determine how much heat a Twitter user is packing.

Unique Referrers is the number of unique people who referenced the user in the seven days prior to the last analysis.

Unique References to Others indicates the number of unique individuals the user wrote or responded directly to in the seven days prior to the last analysis as indicated by the presence of an @username at the beginning of the update.

Unique Retweeted (or, more succinctly, "Unique Retweets of Others") is the number of unique people the user retweeted in the seven days prior to the last analysis. We are also now tracking Twitter-specific retweets.

Unique Retweeters is the number of unique people who retweeted the user in the seven days prior to the last analysis. We are also now tracking Twitter-specific retweets.

Updates is the number updates published in Twitter in the seven days prior to the last analysis. If the number of updates looks wrong to you, update the user's profile.

URLs Cited is the number of updates that included at least one URL (evidenced by the presence of "http://") published in the seven days prior to the last analysis.

Velocity, as defined by Twitalyzer, is an indication of the relative frequency at which a user publishes updates in Twitter.

Who is behind Twitalyzer?

The Twitalzyer is one of those projects that you sometimes start and a few weeks later look back on and say "Wow, that certaintly didn't go exactly as I had planned." Authored by Eric T. Peterson, author, blogger, and founder of the web analytics consulting group Web Analytics Demystified, and supported by a great community of analytics wonks around the globe, Twitalyzer has taken on a life of its own already. In May 2009 Jeff Katz joined Eric in a product management capacity at Twitalyzer and is diligently helping Eric plan for product growth and system scalability.


Eric Peterson


Eric T. Peterson, CEO, Web Analytics Demystified

Eric has worked in the web analytics industry since 1998 where he got his start at WebTrends Corporation in Portland, Oregon. During the last economic meltdown he moved to San Diego to take a job as a Strategic Business Consultant for WebSideStory (now part of Omniture) prior to writing the widely read and critically acclaimed Web Analytics Demystified.

Since then Eric has authored two other books (Web Site Measurement Hacks with O'Reilly & Associates) and The Big Book of Key Performance Indicators) and any number of white papers, research, and blog posts largely on the subject of measurement and analytics.

You can follow Eric in Twitter (@erictpeterson) and if you're really, really into measurement you may dig his blog, Web Analytics Demystified.


Jeff Katz


Jeff Katz, Head of Product, Twitalyzer LLC

Jeff has over 17 years of combining business and technology insight in various product development, strategic consulting, and data analysis roles. Jeff has been an independent consultant since 2006 where he has worked closely with his clients to ensure that their online business initiatives become a reality via comprehensive digital strategies.

Prior to joining Twitalyzer Jeff was a product manager at WebTrends in Portland, Oregon. Previous to that he was worked for large and very small technology companies in Silicon Valley, Route 128 (go #redsox and #celtics) and Tel Aviv, Israel in various product management and data analytic capacities.

You can follow Jeff in Twitter (@katzpdx)


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Twitalyzer is Copyright 2011 Twitalyzer, LLC and is a product of Web Analytics Demystified, Inc.