Now that Microsoft and Google are going to search Twitter, how to make that useful? Social search is clearly part of the answer – filtering results based on social proximity, based on friend\/follow lists. There’s another piece that is missing – the context of the conversation. In Twitter, conversations are represented implicitly by a series of replies between users. Twitter itself does not show that explicitly, though there are clients that do so. <\/p>\n
The thing is, in Twitter, each message is very short, and often depends for context on a poster’s previous tweet, and on her replies to other correspondents. So in order to deliver meaningful results, it would be useful to algorithmically reconstitute the conversation. <\/p>\n
The border of a conversation is fuzzy. In the recent conversation<\/a> between Howard Rheingold and his Twitter followers on multi-tasking, there were a series of back and forth exchanges, that interspersed a bit with other topics. An algorithm would approximate the cutoff points where the topic changes, and the conversation ends. <\/p>\n Then, the search result could be shown in the context of the conversation, and make more sense. <\/p>\n The spark for this post is a conversation between me, Thomas Vander Wal<\/a> and Alan Lepofsky<\/a> on Twitter.<\/p>\n","protected":false},"excerpt":{"rendered":" Now that Microsoft and Google are going to search Twitter, how to make that useful? Social search is clearly part of the answer – filtering results based on social proximity, based on friend\/follow lists. There’s another piece that is missing – the context of the conversation. In Twitter, conversations are represented implicitly by a series … Continue reading “Search the conversation”<\/span><\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2},"jetpack_post_was_ever_published":false},"categories":[20,28],"tags":[],"class_list":["post-1838","post","type-post","status-publish","format-standard","hentry","category-social-software","category-sxd"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_shortlink":"https:\/\/wp.me\/prDRq-tE","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.alevin.com\/index.php?rest_route=\/wp\/v2\/posts\/1838","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.alevin.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.alevin.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.alevin.com\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.alevin.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1838"}],"version-history":[{"count":4,"href":"https:\/\/www.alevin.com\/index.php?rest_route=\/wp\/v2\/posts\/1838\/revisions"}],"predecessor-version":[{"id":1866,"href":"https:\/\/www.alevin.com\/index.php?rest_route=\/wp\/v2\/posts\/1838\/revisions\/1866"}],"wp:attachment":[{"href":"https:\/\/www.alevin.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1838"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.alevin.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1838"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.alevin.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1838"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}