Abstract: As readers have migrated to eBooks and similar digital forms, there has been a transformation in the manner in which they leave marks on books. With this shift, there has been a movement from the widespread ability to find evidence of individual readers towards an aggregation of this information as a monolithic entity often described as “big data,” offering little discrimination between various people. The Kindle contains a database of several million individual highlights that cannot be analysed in great detail on the individual level, but rather on a global, where all nuances and reasonable analysis become vaguer.
One way in which we can trace the reader in these new forms is by instead looking at the collective markings of a single book. Surprisingly, the most popular book to annotate is the The New Oxford American Dictionary, which has been pre-installed on all the devices sold on the US. Over 1000 people have left annotations on the dictionary, but in an atypical way, characterizing many of Leah Price’s arguments about non-responsive readers. These notes are not close reading dictionary but rather something more complex and social, as a group has formed, primarily of pseudonymous teenagers, who use the dictionary in order to chat in a space that has been unrestricted by their parents or educators.
This paper examines this pocket of activity to the degree that it is representative of all digital reading practices and evidences. As the text is transformed into a social network and data to be mined for a variety of companies, to what degree can we see these readers as representative of the new forms of reading on top of the book rather through or in to it.
“Reading the Kindle’s (Non-)Readers.” Real, ideal or implied…? The Reader in Stylistics. June 2014. University of Nottingham.
In lieu of an abstract, in this presentation I looked at the methodological barriers to studying eBooks and how we can reconcile the ability to distant read millions of shared highlights and the lack of access of user’s personal used eBooks.
“Used eBooks.”Digital Reading Network Symposium, June 2014. University of Bournemouth.
One of Twitter’s unique selling points as a social network is its unerring focus on text. Even posting a picture or video independently generates a textual anchor for the media in the form of an URL. As a textual media, Twitter’s primary currency is reading. The politics of reading, and more typically, not reading, characterizes a user’s relationship with their audience of followers and beyond.
It turns out that some of Twitter’s most voracious readers are not human at all, but rather the range of artisan bots that have emerged in the last couple of years. While they vary in type dramatically (Tully Hansen’s taxonomy covers this territory well), at the most fundamental level these bots are reading machines. On a basic level, the tweets emerge from the bots reading, and enacting, their source algorithms, but their literacy extends beyond that. These bots do not generate material out of nothing but rather than read a variety of sources—Twitter searches, the dictionary, novels, ROM texts, headlines, and other assorted materials—and present their readings as new writings.
This sleight of hand, based upon the process of reading to write, is reminiscent of automata that have intrigued countless historical audiences. Through use of clockwork and other mechanisms, automata maintain an illusion of autonomy. Once the underlying mechanics have been figured out, the automata become either trivial or joyful for appreciating the underlying mechanics. Twitterbots garner similar reactions once the processes have been understood, although many make use of dynamic and timely reading sources to ensure proceedings do not become stale. Nonetheless, Adam Parrish’s @everyword, a project that undertook its name in alphabetic order, is probably the most popular bot, despite its stable reading material and its relative predictability ending (that was eventually subverted due to collating words starting with é after z).
@horse_ebooks, the most contentious, and previously beloved, of all Twitterbots has a special place in this analogy: the machine that was disappointingly all too human in the end. The grand reveal that the account which generated bizarrely poetic and uncommercial spam was in fact a human performance mirrors the trajectory of a human-run automata affectionately known as the mechanical turk (picked as a name for Amazon’s crowdsourcing service due to its namesake’s “artificial artificial intelligence”). The Turk appeared to be a brilliantly gifted automatic chess-player that was actually entirely controlled by a human hidden in a secret compartment behind the false clockwork. The performance that fueled @horse_ebooks’s final years equally represented a kind of reverse Turing Test, whereby a human attempted to appropriate the linguistic tics of a bot.
These reading automata become much more interesting when considering the ways in which they challenge our notions of reading. Take, for example, Mark Sample’s Station 51000 (@_LostBuoy_), which plays with the tensions of reading on various levels currently being teased out in humanities research. The bot mixes a reading of sections of Moby Dick with live data from the unmoored buoy classified as station 51000. Despite the specificity of location, the buoy still transmits a range of maritime data. The mash-up of a single, fixed, canonical work of literature with an erratic stream of nautical data goes beyond a comical clash of high culture, low culture and data—it reflects upon digital methodologies of reading that have emerged in recent decades including the use of “big data.” Of course, I’m not the first one to notice this, and the trend in Twitterbots more generally:
As automata, it is not up to these bots to make aesthetic decisions, as evidenced by Station 51000‘s mixture of the literary and real-time feed. Instead they can be used to push the limits of what reading means, and occasionally make us smile or laugh.