Twitter Reputation Statistics
OK, I figure it’s time to throw my hat into the ring.
I’ve posted in the past about Twitter spam and I run what I think to be a pretty fun website about Twitter Stats, but there seems to be a lot of conversation recently about Twitter and the noise ratio.
Obviously, people are trying to figure out how best to use Twitter given its recent surge in popularity and accompanying spaminess. Louis Gray made a blog post about his noise ratio and Stowe Boyd followed up with a post about the noise ratio and conversational index, but there’s one thing that seems to be common across both these posts:
There is a super-fantastic problem in that both posts discuss one, one ratio!
That’s right – one ratio to describe the entire activity of Twitterites. One ratio to rule them all, one ratio to find them, one ratio to bring them all and in the darkness bind them.
OK, perhaps these posts were intended to be their own personal way of determining a proper reputation structure on Twitter, but there is so much more data available to play with. Shall we? Yes, let’s take a look at all the numbers we have to play with:
- Friends
- Followers
- Favorites
- Updates
- Date joined Twitter
- Number of updates over time
- Number of updates in the past month vs. when the first joined Twitter
- % of updates that contain links
- % of updates that are replies
- Number of mentions of the word “awesome”
These are just a few of the numbers that Twitter provides and while the noise ratio is a nice statistics, it is most definitely not a holistic means of providing a method by which to rate the reputation of a Twitter user. And there never will be such a means. Myself and @wardspan had a conversation this evening where we discussed the top three things we use to determine if we’re going to follow somebody. I think we only shared one in common of our top 3 and we tend to be pretty similar-minded. But we use Twitter for different reasons.
And it is with this post that I call out for a reasonable reputation system across our many services. Twitter is one such example, but there have been others in the past (yes, those other social networks) that have dealt with the same reputational issue, not to mention spam.
And it’s not getting better. I signed up for FriendFeed today and created a profile of my real self’s online activity. The scary thing is…I could have created the same profile for anybody else and the question to ask yourself is would anybody have known any better? In addition, in their case – does it even matter? Or are they redirecting their trust to the other systems they are using to generate their content.
Just imagine, if we could create a reliable reputation system across the services that we use to provide us with better and more interesting, targeted content on a daily basis. If only…
[...] Damon Cortesi: Twitter Reputation Statistics (04/27/08) This entry was written by admin and posted on April 27, 2008 at 2:28 pm and filed [...]
April 27th, 2008 at 1:28 pmWell said. I keep hearing about how we should be utilizing aggregation services like friendfeed to keep tabs on our reputation/brand, but what’s the point when anyone can claim your site, feed, blog or profile? At least there are some services that require some form of authentication.
April 27th, 2008 at 10:59 pmFunny I was just thinking creating a script to grab what I think is the best indicator of a person worth following:
following / followers
Most spammers will have low number of followers. Therefore someone with a high ratio for this stat is worth a listen.
Thoughts?
April 28th, 2008 at 8:38 pm@Jon That is definitely a valid statistic and is a common indicator “most of the time”.
There will, of course, be outliers with a high ratio that are not spammers.
In addition, that statistic is important to you in terms of how you follow people, but the point I’m trying to make is that people utilize different information when determining who to follow. So while that may be important to you, other people may consider other factors and I’m attempting to figure out how to present those in a manner useful to many.
April 28th, 2008 at 11:28 pmIf you build it, they will come.
June 28th, 2008 at 11:21 am[...] This comment on the Get Satisfaction thread has some good advice for Twitter: Jason – it seems like Twitter is attempting to address spam in a manner that is either ineffective or has a high level of false positives, even with human interaction. Spam is a difficult problem, but when you start affecting your users on a regular basis, you are losing the battle. Either the tools aren’t working or the humans aren’t doing their job. While I don’t exactly know what steps Twitter is taking, I urge you to read a couple posts I’ve put together on this in the past. I’ve had experience with this type of situation and they may help prod the devs with some other ideas as to how to help prevent spam. http://dcortesi.com/2008/04/16/addressing-twitter-spam-through-statistical-analysis/ http://dcortesi.com/2008/04/27/twitter-reputation-statistics/ [...]
August 4th, 2008 at 9:54 amIt would be great to have a set of statistics available (following/followers, number of RTs in tweets/number of times their tweets are RTed, etc) plus different suggested methods of combining into one index to give
June 18th, 2009 at 2:18 am– those who are most fun
– those who are quickest to reveal new items
– those who are most reliable
If you could then tweak the combination methods for yourself and add external data, you could create your own indices to find the people you want to follow