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Storify demo from Burt Herman on Vimeo.
It's easy to see immediate applications, especially when trying to filter content from noise on a specific hash tag.
But let's step away from intended use for a moment and think about new narrative possibilities. Imagine this as a tool for fiction. You could extract a story told through tweets and easily share it with those who aren't on Twitter, so this becomes a new method of publication or distribution for a short work. You could assemble a digital dérive, combining tweets with images and video. Even more radical, you could put together an entire story without writing a single line of it by piecing together items found on the web and then publishing the compilation. The interface is simple enough to permit a gap between intention and openness, so there's ample room for appropriation and play.
If you try out Storify for fiction, let me know. I'd love to see what you create.
Wordle is a toy for generating “word clouds” from text that you provide. The clouds give greater prominence to words that appear more frequently in the source text. You can tweak your clouds with different fonts, layouts, and color schemes. The images you create with Wordle are yours to use however you like. You can print them out, or save them to the Wordle gallery to share with your friends.Jonathan Feinberg, creator of Wordle (full disclosure: he and my husband worked in the same group at IBM research for two years), makes explicit that Wordle is a toy. It is not a tool for analysis. The reason relates to design: Wordle doesn't only count word frequencies and generate a visualization. It lets users make these word clouds pretty. Users can play with fonts, choose colors, even select whether Wordle should take capitalization into consideration. All of these decisions affect what the viewer judges to be important.
In 2003, a team of researchers from the Illinois Institute of Technology and Bar-Ilan University in Israel (Shlomo Argamon, Moshe Koppel, Jonathan Fine, and Anat Rachel Shimoni) developed a method to estimate gender from word usage. Their paper described a Bayesian network where weighted word frequencies and parts of speech could be used to estimate the gender of an author. Their approach made a distinction between fiction and non-fiction writing styles.The claim is that a small subset of words can skew the "gender" of a writing sample, and these sets vary according to formal versus informal writing styles. The source code clearly shows how words are ranked as masculine or feminine. In the category of informal writing, the top five feminine words are: him, something, because, actually, and everything. The top five masculine words are: some, this, as, now, and good.A simplified version of this work was implemented as the Gender Genie. They showed that fewer words were needed and that writing styles varied based on the forum. For example, fiction and non-fiction differs from blogs (informal writing). Even though the genres differ, there are still gender-specific word frequencies.