Top analyst tweeters (via TweetLevel)
Using SageCircle’s excellent database combined with a good number of my own I have used TweetLevel to understand who are the most important analysts on Twitter. These names are included on the following Twitter list – link
This unique tool compiles twitter data from over 30 sources and feeds the data through an algorithm to rank an individual according to four weightings:
- Popularity (i.e. How many people follow you)
- Influence (i.e. What you say is interesting, relevant and many people listen)
- Engaged (i.e. You actively participate within your community)
- Trusted (i.e. People believe what you say)
Of course, the explanation above is a simplified definition of a complex algorithm(the full methodology of this is shown at the bottom of this post).
How TweetLevel can be used?
I use this list by looking at a micro topic area (such as SaaS) and understand who out of the 950+ analysts on Twitter are the ones that truly use this as a conversation tool. I am normally left with ten names who I now know are critical to engage with. What counts is that if my analysts use Twitter as a medium to engage in conversations then I need to know who they are and take part in the discussion with them.
(Blatant self-promotion – if you want me to sort a list for you that is automatically updated for your specific area (that can be as broad or as specific as you want – from BI to Starbucks) then let me know.)
The primary ranking metric is influence. However, it is interesting to see that when we analyse the same 950 names by influence we get a completely different top 10 list. Congrats to Michael Gartenberg for being the only one to hit both groups.
Once again a huge hat tip and pat on the back to Jeremiah Owyang who leads the way by a country mile. Everyone should take note how he uses Twitter to engage with his community and provide real value.
For the first time I have now seen a larger number of brands in the top five than individuals. eMarketer, Forrester and EConsultancy all fair extremely well. We should therefore recognise other people who punch well above their weight scoring exceptionally highly even though they are only in smaller firms. Kudos to James Governor (jumps from 5th to 3rd) and Michael Krigsman (new entry at 7th).
Even though the influence is the primary ranking metric, I also like to look at those analysts who are most engaged. To have a high level of influence is partially (but not massively) related to popularity. Engagement however is purely related to how someone engages with their community. My praise therefore goes out to Ron Shevlin who even with less than 1,000 followers is especially engaged with his community. This is where TweetLevel excels – normally people like Ron would be ignored on many influencer lists, but in this case it shows that his use of Twitter is exceptional for a particular niche area.
The other key finding from the engagement list is that everyone in the top 10 are individuals not brands. With the recent discussion in the analyst world regarding personal brand versus corporate brand, I will be interested to see whether we will see a move to more company-led tweeting.
Whenever these lists are published, there are several points that always get raised which I will address now…
- This twitter account is not from an analyst. The argument as to whom is an analyst or a consultant is becoming largely moot. In my opinion if someone is independent and directly influences technology procurement then they are an analyst – I for one therefore see Vinnie Mirchandani as an analyst. I know this will cause a huge amount of disagreement but as an outsider looking in this is the way I see the market. This is not to say that some analysts have different strengths over others, it is more a case that I think as an AR pro, I need to monitor the lot of you.
- The twitter handle is written by multiple authors. Some twitter accounts have several analysts writing them whereas others do not. The merits of a single twitter account author is something that I personally favour as this allows me to understand the tone of author without having to understand the many personalities that are associated with it. Regardless, for this table, my view has not been to argue this but merely to present the data.
- It is irrelevant showing all the tweeters as I am only interested in a specific topic– bingo, that is exactly right. My suggestion to all AR pros is to identify which of your analysts are on this and only look at those. If you would like to understand who is important for a particular micro-topic area, please let me konw.
- Hey – you have forgotten to include this list. Please let me know the name and if I will include it as an edit.
Top 10 Analyst Tweeters (ranked by influence and engagement)
|Rank||Ranked by Influence||Ranked by Engagement|
Top 500 Analyst Tweeters (ranked by Influence on TweetLevel)
These names are included on the following Twitter list – link
Algorithm and Methodology
Following – Twitter lists the number of people each user follows. The tendency for most celebrities is to only follow a few individuals. The more people that someone follows, there is an increased likelihood of them actively participating in conversations with the community instead of simply broadcasting to it. Following ranges were determined (i.e. more than 20, more than 30, etc.) and each range was assigned a number (0 to 30) that was used as part of the algorithm. Note: Twitter opened its API to TweetLevel so that data could be sourced easily and quickly to benefit the user.
Followers – Twitter lists the number of people that follow each user. Like subscribing to a feed, this is a clear indication of ‘popularity’ as it requires someone to actively request participation. Even though TweetLevel has a ranking of people based upon popularity, it is influence, engagement and trust that is more important. Due to the nature of logarithmic ranges, a change in the number of people that follow someone, such as from 500 – 1000, will give a far higher change in score than a move from 180K – 200K. Following ranges were determined (i.e. more than 20, more than 30, etc.) and each range was assigned a number (0 to 30) that was used as part of the algorithm. Note: Twitter opened its API to TweetLevel so that data could be sourced easily and quickly to benefit the user.</p>
Twitter Lists – without a doubt this feature addition to Twitter will significantly change the influence score. Even though Twitter has released their API to us, this particular metric is not yet included. When it is, a TweetLevel score will increase depending upon the number of times a user is included in a list, the number of people who follow that list and the authority of those people.
Updates – How often does someone update what they are doing. This number is purely objective as it scores someone highly no matter what the content of their post (i.e. how relevant is it). Nevertheless it is assumed that if someone posts frequently but has poor content then their ‘followers’ will decrease. Update ranges were determined (i.e. more than 20, more than 30, etc.) and each range was assigned a number (0 to 30) that was used as part of the algorithm.
Name Pointing – e.g. @name – How many people engage in conversation with a celebrity or point to their name. The clearest way to establish this is to run a search on the number of people who reference @username in a message. This calculation is based upon a one month period combined with a 24 hour period. The number of times this happens is calculated with each range was assigned a number (0 to 30) – again this was then used as part of the algorithm.
Retweets – Has a tweet caused sufficient interest that it is worth re-submitting by others? Despite a great deal of ‘noise’ (i.e. posts that are not relevant or interesting), when someone sees something that is of high interest, their post can be re-tweeted. The clearest way to establish this is to run a search on the number of people who reference RT @username in a message. This calculation is based upon a one month period combined with a 24 hour period. The number of times this happens is calculated with each range was assigned a number (0 to 50) – again this was then used as part of the algorithm.
Twitalyzer – “This is a unique (and online) tool to evaluate the activity of any Twitter user and report on relative influence, signal-to-noise ratio, generosity, velocity, clout, and other useful measures of success in social media.” This 3rd party tool is a useful method to combine automated metrics dependent upon criteria within posts and publicly available numbers. Where tools such as this are available, we incorporate them into the algorithm to achieve a more confident score. Twitalyzer gives users scores from 0 to 100. Ranges were determined (i.e. more than 20, more than 30, etc.) and each range was assigned a number (0 to 20) that was used as part of the algorithm.
Twitalyzer noise to signal ratio – Signal-to-noise ratio is a measure of the tendency for people to pass information, as opposed to anecdote. Signal can be references to other people (defined by the use of “@” followed by text), links to URLs you can visit (defined by the use of “http://” followed by text), hashtags you can explore and participate with (defined by the use of “#” followed by text), retweets of other people, passing along information (defined by the use of “rt”, “r/t/”, “retweet” or “via”). If you take the sum of these four elements and divide that by the number of updates published, you get the “signal to noise” ratio. Twitalyzer gives users scores from 0 to 100. Ranges were determined (i.e. more than 20, more than 30, etc.) and each range was assigned a number (0 to 20) that was used as part of the algorithm.
Twinfluence Rank – Twinfluence is an automated 3rd party tool that uses APIs to measure influence. For example: “Imagine Twitterer1, who has 10,000 followers – most of which are bots and inactives with no followers of their own. Now imagine Twitterer2, who only has 10 followers – but each of them has 5,000 followers. Who has the most real “influence?” Twitterer2, of course.” As with Twitalyzer, this index uses 3rd party tools to add greater confidence in the overall Twitter score. Similar to the other criteria, ranges were determined (i.e. less than 20, less than 30, etc.) and each range was assigned a number (0 to 20) that was used as part of the algorithm.
Twitter Grader – Twitter Grader is the final automated tool to add greater confidence to the final index. This site creates a score by evaluating a twitter profile. Similar to the other criteria, ranges were determined (i.e. less than 20, less than 30, etc.) and each range was assigned a number (0 to 20) that was used as part of the algorithm.
Involvement Index – The Involvement Index is unique Edelman IP that calculates a score based upon how an individual engages with their community. It is calculated by analysing the content of an individual posts. People who score highest in this category have frequent, relevant, high-quality content that actively involved the twitter community (asking questions, posting links or commenting on discussions) and did not purely consist of broadcasting. Ranges were determined (i.e. less than 20, less than 30, etc.) and each range was assigned a number (0 to 20) that was used as part of the algorithm.
Velocity Index – As more people engage on Twitter, it may become harder to keep activity going. The velocity index measures changes on a regular basis and assigns a score based on increased or decreased participation. Ranges were determined (i.e. less than 20, less than 30, etc.) and each range was assigned a number (0 to 20) that was used as part of the algorithm.
Weighting – Each specific variable listed above was given a standard score out of 10. Using a weighting scale I varied the importance of the each metric to establish an individual’s total score.
Weighted for Popularity – the key variable is the number of people someone has following them. There are many online tools that show this such as Twitterholic.
Weighted for Engagement – the key variables are an individual’s participation with the Twitter community (as measured by the Involvement Index), with additional emphasis on the frequency of people name pointing an individual (via @username), the numbers of followers and the signal to noise ratio. Other attributes were included in the final score but were given a lower weighting.
Weighted for Influence – the key variables in this instance is a combination of the number and authority of someone’s followers together with the frequency of people name pointing an individual (via @username) and the how many times and individuals posts are re-tweeted. Other attributes were included in the final score but were given a lower weighting.
Weighted for trust – the best measure of trust is whether an in individual is will to ‘trust’ what someone else has said sufficiently that they are also prepared to have what they tweeted associated with them. The key metric in this instance are a combination of retweets and number of followers. Other attributes were included in the final score but were given a lower weighting.
In the true spirit of ‘open sourcing’ this work, I welcome your comments, views and criticisms in how this approach can be as accurate as possible. Whereas I don’t believe for one moment that TweetLevel has found the holy grail of social media measurement, I think it is a good step forward and look forward to discussing this with you.
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