Least Squares

just trying to minimize error

How My Neighborhood Reacted to the Opening of A Homeless Shelter

Posted by Scott on May 19, 2011

Last fall* a homeless shelter operated by the SHARE organization opened in my neighborhood of Wallingford in Seattle. Wallingford is a left-leaning Seattle neighborhood, the kind of neighborhood that supports causes and organizations like homeless shelters almost without saying. Or does it?

The details of the shelter were found to be unpalatable to some neighbors. Mainly they opposed the lack of oversight on the shelter (it’s self-governing), the lack of background checks on residents of the shelter (SHARE adamantly opposes background checks on its shelter residents), and the fact that there is a preschool also in the church that was to house the shelter. Also, the opening of the shelter was announced less than two weeks before the shelter was to open.

Can’t put your neighborhood where your ideology is? The discussion raged at several “townhall” type meetings, and to a surprising extent on our neighborhood blog, Wallyhood. The blog really played a critical role in raising awareness, spreading information, and in providing a discussion forum.

I thought it would be interesting to poke at the blog comments to see what they said about how people participated in this discussion. To do so, I grabbed all the posts and comments from the month of September, separated out the posts about the shelter (there were 5 of them: 1, 2, 3, 4, 5) and then compared the comments on the Share posts to those on the remaining posts.

First, there were 90 posts total, yet the number of comments on the 5 Share posts (484) was far greater than the number of comments on the 85 No Share posts (327). How about the distributions of commenters to comments?

These curves look fairly similar, but actually they’re pretty different. The Share curve is driven much more by a few extreme posters, while the No Share curve is a much “softer” distribution. In fact, if you fit these as power laws (neither of these are great fits, but just for illustrative purposes), the alphas are quite different: 1.79 for No Share versus 2.12 for Share. So, the typical commenting pattern is more evenly distributed than is the commenting pattern for the Share topic.

OK, so a small group is really driving the conversation about this topic. What is the tone of what they are saying? To check this, I ran the text of the comments through LIWC:

This shows how different types of words were used in the Share versus No Share post comments. For instance, people used more ‘anger’ words in the Share post comments. The height of the bar indicates the relative difference in use of the word type between the two post types; the bars are ordered left to right (and color coded) by effect size (Cohen’s d): the most Share on the left to the most No Share on the right. Only word types with effect sizes of .4 or greater are shown.

The Share posts generated comments that were more angry, social, “other” oriented (he/she/they), and more about health. Typical posts are more about leisure and about space and time (likely describing events and happenings in the neighborhood). It’s worth noting, though, that not all the other posts are fluffy noncontroversial posts – there are plenty of discussion-worthy topics.

Overall, this was a big event in the neighborhood and it was really interesting to see just how critical the blog was. This issue literally blew away records for number of comments on Wallyhood posts. When you look at the data, it appears that while the tone of the “discussion” was angry and very other oriented, there appears to be a general tenor (as evidenced by the large number of posts and posters) but also a clear effect of a smallish number of people. I dont’ think this pattern is readily apparent when you are just reading the posts.

* Yes, in Twitter-time, last fall is like the Mesozoic. However, in terms of major events in a neighborhood, last fall isn’t all that distant. One interesting feature of hyperlocal blogs is that they reflect a time scale that’s very relevant to people’s lives, but gets overlooked in other forms of social media.

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