Find Your Twitter Psychological Matches

I think the most powerful potential feature of a system like TweetPsych is its ability to match people based on their cognitive processes, so I’ve added two features to the still beta TweetPsych.

People That Think Like You

When you generate a profile for yourself or someone else, TweetPsych will also show you a list of 5 users who it believes share similar psychological characteristics. This matching is not done topically, therefore the other users you’re presented with may not Tweet about the same things as you.

These users come only from the list of users that the system has analyzed so far, so the results will get better as it analyzes more accounts. Starting this week, I am automatically profiling accounts starting with a few prioritized lists, including most ReTweeted users and most followed users to help build a large dataset for comparison.

Site Profiling

The second feature I added this weekend is site profiling. When you enter a URL TweetPsych will create a psychological profile of the content on that page and match it against its database of user profiles, returning the 50 closest matches.

Again, this matching is not done on a topical basis, meaning the users presented might not tweet about the same subjects the page is about. The goal is to help you find users that may be mentally aligned with the psycho-graphic profile of the web page you provided.

And just to reiterate, TweetPsych is still beta stuff and I’m aware there are issues, specifically around explaining and presenting the features in a more understandable way, but my first priorities were making the system stable under the huge traffic load (and my host MediaTemple has been awesome helping me) and fleshing out the potential power of the technology. I’m very open to new feature suggestions as I continue working on TweetPsych.

I am contemplating the possibility of releasing an API but I’m still thinking about how to handle the possibly high server resource demands. What features would you like to see in an API?

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Psychological Profiling Via Twitter

This weekend I was playing with a bunch of different linguistic analysis methods to better understand ReTweets, and while I uncovered a ton of cool new data which I’ll be sharing a little later this week, I also came upon an idea I think is pretty awesome, probably groundbreaking, and definitely worth Twittering about.

Communication is a window into a person’s mind, and the way a person talks can tell you a lot about how they think. Linguists have developed two methods to decoding the written word into a meaningful profile of a person’s cognitive processes.

One method is called the Regressive Imagery Dictionary (RID). This coding scheme is designed to measure the amount and type of three categories of content: primordial (the unconscious way you think, like in dreams), conceptual (logical and rational though) and emotional.

Significantly more primordial content has been found in the poetry of poets who exhibit signs of psychopathology than in that of poets who exhibit no such signs (Martindale, 1975). There is also more primordial content in the fantasy stories of creative as opposed to uncreative subjects (Martindale & Dailey, 1996), in psychoanalytic sessions marked by therapeutic “work” as opposed to those marked by resistance and defensiveness (Reynes, Martindale & Dahl, 1984), and in sentences containing verbal tics as opposed to asymptomatic sentences (Martindale, 1977). A cross-cultural study of folktales from forty-five preliterate societies revealed, as predicted from the “primitive mentality” hypothesis of Lévy-Bruhl (1910) and Werner (1948), that amount of primary process content in folktales is negatively related to the degree of sociocultural complexity of the societies that produced them (Martindale, 1976). Martindale and Fischer (1977) found that psilocybin (a drug that has about the same effect as LSD) increases the amount of primordial content in written stories. Marijuana has a similar effect (West et al., 1983). Research has also revealed more primordial content in verbal productions of younger children as compared with older children (West, Martindale, & Sutton-Smith, 1985) and of schizophrenic subjects as compared with control subjects (West & Martindale, 1988).

The other method is Linguistic Inquiry and Word Count (LIWC). In development for over 15 years, the LIWC measures the cognitive and emotional properties of a person based on the words they use.

In order to provide an efficient and effective method for studying the various emotional, cognitive, and structural components present in individuals’ verbal and written speech samples, we originally developed a text analysis application called Linguistic Inquiry and Word Count, or LIWC.

I’ve combined these two systems with a Porter stemming algorithm and my own Twitter analysis infrastructure to create TweetPsych.com.

TweetPsych uses the LIWC and RID to build a psychological profile of a person based on the content of their Tweets. It compares the content of a user’s Tweets to a baseline reading I’ve built by analyzing an ever-expanding group of over 1.5 million random Tweets, then highlighting areas where the user stands out.

The service analyzes your last 1000 Tweets; as such, it works best on users who have posted more than 1000 updates. It is also better suited for running analyses on accounts that are operated by a single user and use Twitter in a conversational manner, rather than simply a content distribution platform. It takes a few moments to analyze an account the first time, but subsequent views of a profile will load faster.

I’ve tried to translate the codes that come from the two linguistic systems into more meaningful explanations, but I may have missed a few. I will continue to expand these definitions, while also refining the system and algorithm to better analyze Twitter-specific content.

I think the possibilities of a system like this are enormous, from matching like-minded users to identifying users that exhibit certain useful or desirable traits. I’d love to hear your thoughts on where this could be improved or where I could take this technology next.

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How to Get ReTweets: The Presentation

The internet has accelerated social communications and memetics more than it has fundamentally changed it (though it has altered some of the selection pressures on individual memes, namely around memory retention and expression). It has also, through mechanisms like Twitter and specifically ReTweets, made the exchange of cultural units much more open to quantitative analysis and testing. Through the keyhole of ReTweeting I believe it is possible to get a glimpse of the answers to the larger question of why and how humans spread information in a way that was never before possible.

I’ve studied characteristics from pre-web memetic channels (like urban legends, rumors, slang, oral tradition and proverbs) and many of the traits I found there I’ve also found when looking into ReTweets. Namely concepts like communal recreation, social proof, information cascades, knowledge gaps, novelty and utility.

I’ve found myself telling the Snow Crash story a lot recently to explain what I see as the true power of what I call viral marketing science. Here’s two versions of it.

Being that I come at this opportunity from a marketing background, I look to this analysis to build a framework for repeatably creating contagious memes, so this presentation from PubCon Austin aims to do just that for ReTweets.

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