• Personality as Revealed by Tweet Cloud

    Screenshot of a Wordle word cloud of Michelle's most commonly tweeted words
    Credit: Wordle by Michelle A. Hoyle under an Attribution-ShareAlike 3.0 Generic license
    Image: Michelle’s tweet cloud. Most used words: thanks, RT, good, learning and marking.

    Niall Sclater posted an article on his blog yesterday about personality and tweet clouds.  Inspired, I ran used Wordle of my top used words. TweeetStats, like many text analysis tools, uses a stop list, removing common words like “and”, “the”, “a”, etc. TweetStats gave me the option of additionally removing the names of people to whom I was replying, so terms like “@psychemedia” and “@AJCann” have also been excluded. It could use some better stemming. “Courses” and “course” appear as separate entries, as do “game” and “games”.

    Graph of Michelle's Twitter client usage
    Credit: Screenshot by Michelle A. Hoyle under an Attribution-ShareAlike 3.0 Generic license
    Image: Graph of Michelle’s Twitter client usage. Most common is Twitter for iPhone at over 1500 tweets, followed by Syrinx (for the Mac) at 1000 and then Tweetie for the Mac.

    Looking at the generated stats, I apparently reply a lot.  54% of my tweets, according to TweetStats, have been replies.  I’m not sure the data is complete though.  There’s a big gap in the chart (see image below), making it look like I didn’t have any tweets between January 2009 and January 2010.  That’s definitely wrong! Missing an entire year’s worth of tweets likely influences my tweet cloud considerably. I suspect the missing bits are due to restrictions that Twitter has on the amount of requests for information that can be sent. I am not convinced it even reflects all of this year’s tweets. Perhaps someone more knowledgeable could comment on that.

    Although I tried Twitter for the iPhone for a day or two, I never really used it that much. I believe that Tweetie, which I did use, got rebranded as Twitter for iPhone, so my past usage of that client. The interface usage statistics also include “Syrinx”, which I don’t remember using beyond opening it up. I certainly used Tweetie for Mac far more than I’ve used Syrinx. I suppose it’s possible that’s another renamed Twitter client. Prior to using Tweetie for the Mac, I used Twhirl. That doesn’t show at all and I used that for a long time. Another victim of the missing time? Possibly.

    Michelle's timeline of tweets since 2008
    Credit: Screenshot by Michelle A. Hoyle under an Attribution-ShareAlike 3.0 Generic license
    Image: Michelle’s timeline of tweets since 2008.

    Examining the word cloud at the top of this post, I obviously spend too much time marking or talking about marking.  To be fair, some could be assessment principles or assessment research.  After all, learning, students, and research are quite high too. That said, other than I’m Canadian with a penchant for “good” “think”ing, what does this say about my personality? Does it really reflect my obsessions? I do think a lot, but I also read a lot and play games. They feature in there, but not as much as I would expect. I maintain multiple Twitter accounts, so perhaps my interests have been split somewhat and this main account distributes my interests more evenly, making it harder to represent me on the basis of my tweets.

    I see I am stereotypically Canadian; “thanks” figures quite prominently. I also share and acknowledge the words of others, because “rt”, short for retweet, frequently occurs. Something is interesting from what doesn’t appear: negative terms. “good” and “interesting” are there, but words of complaint or dislike or other negative emotions do not feature often enough to show up on the word cloud. That suggests I am a positive person. I would disagree with that. I think I can be highly critical and frequently negative, but I tend to keep it to myself rather than impulsively blurting it out on Twitter.

    Are you what you tweet? What does a tweet cloud reveal about your personality and interests?


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