Feb 11th 2010

Continuing my series of TweetPsych based data points, this is based on analysis of over 100,000 accounts and looks at the “Negative Remarks” category. Negative remarks include things like sadness, aggression, negative emotions and feelings, and morbid comments.

As it turns out, nobody likes to follow a Debbie Downer accounts with lots of followers don’t tend to make many negative remarks. If you want more followers, cheer up!

Jan 25th 2010

If you like this post, or any of my work, please, nominate me for a Shorty Award.

The linguistic analysis engine behind TweetPsych has given me a bunch of cool data points to analyze, so I’ve begun to look at various factors and their relationship with follower counts. Using a database of over 30,000 accounts that have been analyzed with TweetPsych, the first dimension I’ve looked at is “Social Behavior”.

The “Social Behavior” category includes inclusive language like “we” and “you”, as well as language that describes relationships and communication. As it turns out, accounts with more followers, tended to be using more social language.

Over the next week or two, I’ll be posting about the rest of …

Jan 4th 2010

After I first launched the Twitter psychological profiling tool TweetPsych, some of the most common feedback I got was that it was hard to understand the results. So I designed a new reporting mechanism and design to solve that problem. The new TweetPsych uses “meta dimensions” which are combination of related factors from the two linguistic algorithms (RID and LIWC) the application uses. Each of these comes with a description and is represented on a bar graph. Each user’s profile is compared against the average user and the report explains which dimensions occur more or less frequently than the average.

I also launched a new feature for the site. TweetPsych for Lists allows you to do the …

Oct 27th 2009

Want more clicks? My new data suggests that you should Tweet your links in afternoons, evenings and on weekends.

Continuing the study of Twitter clickthrough rates I started last week, I added over 100 more of the most followed Twitter accounts to my database and indexed click data on over 20,000 bit.ly links Tweeted by those accounts. In all of the data below, I measured CTR as the number of clicks a link received, divided by the number of followers the sending account had on the day it Tweeted it. As I noted in my other post, this number can be over 100% due to ReTweets that may use the same bit.ly link.

The graphs below shows the …

Oct 26th 2009

Earlier this year I read a paper called “Modeling Blog Dynamics” in which they propose a method of modeling the spread of links through the blogosphere using zero-crossing random walks and exploitation vs. exploration applied to a logical flowchart model:

The authors suggested that the model could be used in influence maximization algorithms which aim to identify key, influential individuals in a given social network for the purposes of viral marketing. I was intrigued by the possibilities and have been tossing around a possible flowchart model of how individuals decide to ReTweet specific Tweets since reading that paper. Here’s my first attempt:

There are three steps in the process where a marketer can increase the chances of a …