{"id":253,"date":"2013-08-29T19:49:14","date_gmt":"2013-08-29T23:49:14","guid":{"rendered":"http:\/\/blogs.law.harvard.edu\/andresmh\/?p=253"},"modified":"2013-12-08T02:59:11","modified_gmt":"2013-12-08T07:59:11","slug":"the-3-things-you-can-learn-about-your-neighborhood-using-whooly","status":"publish","type":"post","link":"https:\/\/archive.blogs.harvard.edu\/andresmh\/2013\/08\/the-3-things-you-can-learn-about-your-neighborhood-using-whooly\/","title":{"rendered":"The 3 things you can learn about your neighborhood using Whooly"},"content":{"rendered":"

Along with my colleagues\u00a0Shelly Farnham<\/a>, and Michal Lahav\u2014and our interns\u00a0Yuheng Hu<\/a>,\u00a0Emma Spiro<\/a>, and\u00a0Nate Matias<\/a>\u2014we have been exploring ways of discovering and fostering latent neighborhood information to help people understand what\u2019s happening in their local communities.<\/p>\n

As part of this research, we have created\u00a0Whooly<\/a>\u00a0an experimental\u00a0mobile website that discovers and highlights neighborhood-specific information on Twitter in real-time. The system is focused, for now, on various neighborhoods of the Seattle metro area (King County<\/a>\u00a0to be specific).\u00a0Whooly<\/a>\u00a0automatically discovers, extracts and summarizes hyperlocal Twitter content from these communities based on mentions of local neighborhoods and relevant keywords from tweets and profiles. One can think of\u00a0Whooly<\/a>\u00a0as a neighborhood Twitter client.<\/p>\n

\"Screenshot<\/a><\/p>\n

<\/p>\n

\"\"While\u00a0Whooly<\/a>\u00a0is currently aggregating Twitter information for most of Seattle\u2019s neighborhoods, our research will be focused on studying social media interactions and the impact of this particular system for a select number set of them, including:Capitol Hill<\/a>,\u00a0South Lake Union<\/a>,\u00a0Wallingford<\/a>, and\u00a0West Seattle<\/a>.<\/p>\n

From our\u00a0original research<\/a>\u00a0on Whooly (which Yuheng presented at\u00a0CHI 2013<\/a>), we wanted the system to highlight three particular things people can learn about their neighborhoods:<\/p>\n

    \n
  1. Many of your neighbors are already on Twitter and they\u2019re talking to each other.<\/li>\n
  2. Some of those neighbors are community hubs who publish and curate timely and useful information.<\/li>\n
  3. Just like cities have \u201cTwitter Trending Topics,\u201d so do neighborhoods, and they often represent neighborhood events.<\/li>\n<\/ol>\n

    More broadly, neighborhoods shape so much of our life experiences, yet, many of us feel disconnected from the people who live around us, often feeling closer to someone 2,000 miles away than our next door neighbors.\u00a0Some<\/a>\u00a0have been blamed suburbanization, sprawl, and the privatization of leisure time, while\u00a0others<\/a>\u00a0have argued that network technologies are contributing to being \u201calone together.\u201d<\/p>\n

    While all this might be true, since the early 1970\u2019s, people have been exploring how network technologies can be used by and for hyperlocal communities. \u00a0For example,Community Memory<\/a>\u00a0was a bulletin board system created in 1973 specifically to strengthen Berkeley\u2019s local community with the idea that “strong, free, non-hierarchical channels of communication\u2014whether by computer and modem, pen and ink, telephone, or face-to-face\u2014are the front line of reclaiming and revitalizing our communities.”<\/p>\n

    \"A<\/a><\/p>\n

    We have seen these ideas evolve and transform into systems like\u00a0Craigslist<\/a>,\u00a0Meetup<\/a>,Yelp<\/a>, and\u00a0Foursquare<\/a>, among others. More recently, systems specifically targeting neighborhoods, like\u00a0Nextdoor<\/a>, have gained some of attention. However, some of the main challenges with these and any new social computing systems is that\u00a0adopting a new communication channel is never an easy feat<\/em>. This is what inspired\u00a0Whooly<\/a>‘s approach to build\u00a0on\u00a0existing<\/em>\u00a0communication channels to\u00a0discover and highlight latent neighborhood communities<\/em>. As we mentioned, it turns out \u00a0neighbors are already talking to each other, it\u2019s just hard to find this right now. Here is, for example, three network diagram of the conversations in three of the neighborhoods we examined.\u00a0Whooly<\/a>\u00a0uses data like these to make its recommendations.<\/p>\n

    \"Intra-neighborhood<\/a><\/p>\n

    If you live in Seattle, we invite you to start experimenting with\u00a0Whooly\u00a0<\/a>and tell us what you think. As with any research project, there are plenty of kinks to iron out, but early feedback is incredibly helpful. Feel free to reach us at\u00a0@whoolyit<\/a>.<\/strong><\/p>\n

    Cross posted at the\u00a0FUSE Labs blog<\/a>.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

    Along with my colleagues\u00a0Shelly Farnham, and Michal Lahav\u2014and our interns\u00a0Yuheng Hu,\u00a0Emma Spiro, and\u00a0Nate Matias\u2014we have been exploring ways of discovering and fostering latent neighborhood information to help people understand what\u2019s happening in their local communities. As part of this research, we have created\u00a0Whooly\u00a0an experimental\u00a0mobile website that discovers and highlights neighborhood-specific information on Twitter in real-time. […]<\/p>\n","protected":false},"author":3887,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[53290],"tags":[],"class_list":["post-253","post","type-post","status-publish","format-standard","hentry","category-civic-computing"],"jetpack_featured_media_url":"","_links":{"self":[{"href":"https:\/\/archive.blogs.harvard.edu\/andresmh\/wp-json\/wp\/v2\/posts\/253","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/archive.blogs.harvard.edu\/andresmh\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/archive.blogs.harvard.edu\/andresmh\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/archive.blogs.harvard.edu\/andresmh\/wp-json\/wp\/v2\/users\/3887"}],"replies":[{"embeddable":true,"href":"https:\/\/archive.blogs.harvard.edu\/andresmh\/wp-json\/wp\/v2\/comments?post=253"}],"version-history":[{"count":3,"href":"https:\/\/archive.blogs.harvard.edu\/andresmh\/wp-json\/wp\/v2\/posts\/253\/revisions"}],"predecessor-version":[{"id":255,"href":"https:\/\/archive.blogs.harvard.edu\/andresmh\/wp-json\/wp\/v2\/posts\/253\/revisions\/255"}],"wp:attachment":[{"href":"https:\/\/archive.blogs.harvard.edu\/andresmh\/wp-json\/wp\/v2\/media?parent=253"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/archive.blogs.harvard.edu\/andresmh\/wp-json\/wp\/v2\/categories?post=253"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/archive.blogs.harvard.edu\/andresmh\/wp-json\/wp\/v2\/tags?post=253"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}