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Word clouds to support reflection

When preparing our Persuasive 2010 paper on Three Good Things, we ended up cutting a section on using word clouds to support reflection. The section wasn’t central to this paper, but it highlights one of the design challenges we encountered, and so I want to share it and take advantage of any feedback.

Our Three Good Things application (3GT) is based on a positive psychology exercise that encourages people to record three good things that happen to them, as well as the reasons why they happened. By focusing on the positive, rather than dwelling on the negative, it is believed that people can train themselves to be happier.

Example 3GT tag clouds

When moving the application onto a computer (and out of written diaries), I wanted to find a way to leverage a computer’s ability to analyze a user’s previous good things and reasons to help them identify trends. If people are more aware of what makes them happy, or why these things happen, they might make decisions that cause these good things to happen more. In 3GT, I made a simple attempt to support this trend detection by generating word clouds from a participant’s good things and reasons. I used simple stop-wording, lowerizing, and no stemming.

Limited success for Word Clouds

When we interviewed 3GT users, we expected to find that the participants believed the word clouds helped them notice and reinforce trends in their good things. Results here were mixed. Only one participant we interviewed described how the combination of listing reasons and seeing them summarized in the word clouds had helped her own reflection:

“You’ve got tags that show up, like tag clouds on the side, and it kind of pulls out the themes… as I was putting the reasoning behind why certain [good] things would happen, I started to see another aspect of a particular individual in my life. And so I found it very fascinating that I had pulled out that information… it’s made me more receptive to that person, and to that relationship.”

A second participant liked the word cloud but was not completely convinced of its utility:

I like having the word cloud. I noticed that the biggest thing in my reason words is “cat”. (Laughs). And the top good words isn’t quite as helpful, because I’ve written a lot of things like ‘great’ and ‘enjoying’ – evidently I’ve written these things a lot of times. So it’s not quite as helpful. But it’s got ‘cat’ pretty good there, and ‘morning’, and I’m not sure if that’s because I’ve had a lot of good mornings, or I tend to write about things in the morning.

Another participant who had examined the word cloud noticed that “people” was the largest tag in his good things cloud and “liked that… [his] happiness comes from interaction with people,” but that he did not think that this realization had any influence over his behavior outside of the application.

One participant reported looking at the word clouds shortly after beginning to post. The words selected did not feel representative of the good things or reasons he had posted, and feeling that they were “useless,” he stopped looking at them. He did say that he could imagine it “maybe” being useful as the words evolved over time, and later in the interview revisited one of the items in the word cloud: “you know the fact that it says ‘I’m’ as the biggest word is probably good – it shows that I’m giving myself some credit for these good things happening, and that’s good,” but this level of reflection was prompted by the interview, not day-to-day use of 3GT.

Another participant did not understand that word size in the word cloud was determined by frequency of usage and was even more negative:

It was like you had taken random words that I’ve typed, and some of them have gotten bigger. But I couldn’t see any reason why some of them would be bigger than the other ones. I couldn’t see a pattern to it. It was sort of weird… Some of the words are odd words… And then under the Reason words, it’s like they’ve put together some random words that make no sense.

Word clouds did sometimes help in ways that we had not anticipated. Though participants did not find that they helped them identify trends that would influence future decisions, looking at the word cloud from her good things helped at least one participant’s mood.

I remember ‘dissertation’ was a big thing, because for a while I was really gunning on my dissertation, and it was going so well, the proposal was going well with a first draft and everything. So that was really cool, to be able to document that and see… I can see how that would be really useful for when I get into a funk about not being able to be as productive as I was during that time… I like the ‘good’ words. They make me feel, I feel very good about them.

More work?

The importance of supporting reflection has been discussed in the original work on Three Good Things, as well as in other work that has shown how systems that support effective self-reflection can improve users’ ability to adopt positive behaviors as well as increase their feelings of self-efficacy. While some users found benefit in word clouds to assist reflection, a larger portion did not notice them or found them unhelpful. More explanation should be provided about how word clouds are generated to avoid confusion. They should also perhaps not be shown until a participant has entered a sufficient amount of data. To help participants better notice trends, improved stop-wording might be used, as well as detecting n-grams (e.g. “didn’t smoke” versus “smoke”) and grouping of similar terms (e.g., combining “bread” and “pork” into “food”). Alternatively, a different kind of reflection exercise might be more effective, one where participants are asked to review their three good things posts and write a longer summary of the trends they have noticed.

{ 2 } Comments

  1. scritic | May 5, 2010 at 10:36 am | Permalink

    This is a very interesting project! I especially like how therapeutic the project’s goal is.

    A couple of suggestions+reasons for why the word clouds didn’t work:

    (1) On the surface, it might just be that you need a better filtering mechanisms for the words. Along with what you’ve suggested above, what about just keeping the nouns and the verbs?

    (2) A second reason could be that extracting words out of what are essentially narratives — to help your users find the similarities between them — doesn’t really work because the the words don’t evoke the narratives themselves. What if you extract the most frequently occurring words out but surround them by their context (say the sentence in which they occur)? I am thinking this will function better as a way to make your users think back over their experiences.

  2. sean | June 14, 2010 at 3:37 pm | Permalink

    Hi – thanks for the thoughts.

    (1) I think that filtering is worth exploring. Possibly at as coarse of a level as nouns and verbs, possibly trying to be more precise.

    (2) I like the context idea a lot. This might be done as a mouseover. Right now, people can click and it will filter their list to posts containing that word, but a click is more work. Some people also didn’t realize they were clickable… so that’s just poor UI on my part. Playing around with bigrams might also be a way to provide more context (particularly once people have a lot of posts) – “made” vs. “made _(what?)_”

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