This bot data mines a 1% sample of the public Twitter stream to identify tweets that could be considered haiku. It then republishes the result, formatting it as can be seen above, and retweets the original in its Twitter account. The page the haikus are published in uses random background images of nature, a nod towards the seasonal reference so valued in this poetic tradition.
This project is very similar to John Burger’s HaikuD2: they’re both bots that mine, filter, reformat and publish found poetry conceptually repackaged in the Haiku tradition. Their proximity invites comparison of the choices made in selecting and shaping the processed output. A noteworthy difference is the division into lines (in HaikuD2) versus using div tags to separate each 5 or 7 syllable cluster, particularly with how they focus attention on ideas, phrases, images, emphasizing or deemphasizing enjambment. Another is in how they handle attribution in their publication. Wood displays the original tweet in the “Tweet Haikus” page and linking back to it, but the Twitter manifestation is simply a retweet from @tweethaikuscom, without even a link back to the page generated from it or a hashtag (as Burger’s bot does) for the reader to realize that they have inadvertently written something that could be read as a haiku. More comparisons could be made, and even evaluations of their relative success, but suffice it to say that both bots are effective at what they do, and both have room to develop in order to refine their output (which both programmers have publicly expressed).My favorite aspect about Wood’s creation is that its source code is published online, along with tutorials that lead up to its creation. Reading these excellent entries on Twitter data mining and natural language detection, one can see this project as an example and proof-of-concept rather than a completed work. In this sense, “Tweet Haikus” is a wonderful invitation to remix, develop, and build upon this published framework.
And what else might we be able to find, remix, and repackage with similar methods?
Featured in Genre: Bot
Link to ELMCIP Knowledge Base entry.
