How to Search Twitter Lists using FriendFeed

Searching Twitter Lists can be incredibly useful. What are the most influential tech people saying about Droid vs. iPhone? What are leading tech blogs saying about the Microsoft Azure platform? What do experienced investors think of the economy? The possibilities are endless, limited only by your imagination.

The problem? Twitter’s Advanced Search doesn’t support specifying Lists as a “From” parameter, because Twitter’s Search Operators don’t support Lists as of this writing. Neither do third-party search engines like Searchtastic, Twazzup, Topsy, etc. I came up with a workaround to this problem using FriendFeed and it works like a charm! Follow these two steps just once for each Twitter List you want to search.

I will use my Techmeme Leaderboard 50 list as an example.

Step 1: Get the RSS Feed for the Twitter List

Go to Twitter Lists To RSS and enter your Twitter List URL.

TwitterListToRSS

Get the RSS feed for your Twitter List and save the link.

RSS Feed Created

Step 2: Create a Group on FriendFeed

On FriendFeed, create a Group with a suitable name. You can choose to keep it private or make it public to share it with others.

Create FF Group

Add the RSS Feed created for your Twitter List in Step 1 as a Service of the Group.

Twitter List RSS Feed in FF Group

Search Your Twitter List!

You are all set. You can add the Group to your FriendFeed sidebar for quick access, and search any keywords as shown below.

Search FF Group

Here is how you will see the results, including the links in the tweet you can jump to directly.

Search FF Group Results

Found several “tech pundits” lists and you can’t decide which is the best one to use as a search reference? Simply add the RSS feeds for all of them to your FriendFeed Group! This way, you can become a “super-curator” of Twitter Lists created by others.

FriendFeed Groups are a powerful way to follow, search, mix, and share Twitter Lists. But we already knew FriendFeed was incredibly powerful, right?

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Since the introduction of Lists in Twitter, there has been some speculation about how Twitter Lists could help indicate Influence. See the following for some background:

It is clear that interest focuses on the ratio of your Lists to Followers.

I decided to assess whether this new metric correlates in any way to existing influence measurement tools. The objective was to assess whether the metric has any correlation with influence ranking algorithms that do not use Lists information. For my experiment, I considered influence measurement tools like Twinfluence, Twitalyzer, and Klout.

Is this a Big Deal?

Not for casual users. There can be important implications for serious users. Since the advent of Twitter, the number of followers has been considered to be a rough indicator of influence. As a result, very few have taken pains to actually filter their followers and weed out spammers and bots. In 12 Tips to Enhance Your Twitter Reputation, I had discussed how you should do this. If the Lists-Follower metric is widely used for influence measurement, you will see people actually scanning their Followers.

This can also become important because your influence may determine the ranking of your tweets in search results.

Influence Ranking Tool

My tool of choice was Klout, for the following reasons:

  • Speed. The tool had to process and rank influence for each member of my sample set quickly.
  • Twitalyzer gave unlikely influence ranks for some people I knew.
  • Klout is transparent in revealing what factors it considers and changes to their algorithm. This will be useful in revisiting this after it incorporates Lists information.
  • Klout Score uses 25-30 variables to be comprehensive, unlike Twitalyzer, which uses only 5.

Sample Selection

I used 40 Twitter users I follow for creating my dataset. I only considered accounts that represented people, and not brands. For my dataset, I selected:

  • Those with more than 10,000 followers
  • Those with a ratio of Followers:Friends > 10:1
  • Some more users at random to form a long tail for the analysis, all of whom have more than 1000 followers
  • I couldn’t resist including myself, as one user with <900 followers

The result of my experiment looks like this, with the accounts ordered by decreasing no. of followers:

LF Influence Results

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