I-could-have-written-that: Difference between revisions

Revision as of 12:39, 10 June 2016

I-could-have-written-that
Creator	Manetta Berends
Year	2016
Bio	Manetta Berends (NL) has been educated as a graphic designer at ArtEZ, Arnhem before starting her master-education at the Piet Zwart Institute (PZI), Rotterdam. From an interest in linguistics, code and a research based design practice, she currently works on the systemization of language in the field of natural language processing and text mining.
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Website	http://www.manettaberends.nl

i-could-have-written-that is a two hour workshop, that critically examines the reading power of text mining software. The workshop dismantels how large sets of written documents are transformed into useful/meaningful/truthful information. A process that is presented by text mining companies as "the power to know", "the absolute truth", "with an accuracy that rivals and surpasses humans". This workshop challenges the image of this modern algorithmic oracle, by making our own. more info: i-could-have-written-that.info

i-could-have-written-that is a two hour workshop, in which we will write between the lines, raw data will be cooked, and personal oracles will be trained. The workshop contains three parts. We start with a writing exercise in which we challenge the strictness of numbers with words. Then we will train our personal oracles by following the 1989 model Knowledge Discovery in Databases, and do manual pattern recognition with notecards and pens. After this hard work, we will ask three algorithms to do a bit of work as well. They will built your personal oracle, which you can consult at any time in the future!

Many text mining services offer pre-trained classifiers through API's without specifying how the system is created. They promise to read meaning out of a text correctly. This unconditional trust is the basis of automated essay scoring systems (AES), psychology studies, job vacancy platforms and terrorism prevention. Automated systems are trained to detect sentiment, education level, mental illness, or suspicious behaviour. The techniques are used online in i.e. social media platforms, in the academic field and by the government. However, framing text mining techniques as readerly systems makes them immune for questions and discussion. When text mining results are considered as a product of writing, the presence of an author is emphasized. This makes it possible to discuss text mining results in relation to predefined goals and breaks the idea that text mining results represent an absolute truth.

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-|Description=''i-could-have-written-that'' is a workshop on the subject of text mining, a technology that uses statistics and computational linguistics to extract knowledge from the overwhelming amount of digital texts that are published online. Under mottos like ''"the power to know"'', text mining services offer pre-trained classifiers through APIs without specifying how the system is created. The workshop challenges the image of this modern oracle, by discussing how the software, work process and vocabulary together construct text mining outcomes. more info: [http://i-could-have-written-that.info i-could-have-written-that.info]
+|Description=''i-could-have-written-that'' is a two hour workshop, that critically examines the reading power of text mining software. The workshop dismantels how large sets of written documents are transformed into useful/meaningful/truthful information. A process that is presented by text mining companies as ''"the power to know"'', ''"the absolute truth"'', ''"with an accuracy that rivals and surpasses humans"''. This workshop challenges the image of this modern algorithmic oracle, by making our own. more info: [http://i-could-have-written-that.info i-could-have-written-that.info]
 }}
-[[File:Perceiving-a-wordcloud.png|400px]]
+[[File:I-could-have-written-that categories-modality.gif]]
-The overwhelming amount of digital text available these days, is presented as the problem that can be solved by algorithmic processes: text mining. Text classification is one of its subsections, in which statistics are combined with techniques that process natural language. The technology is used widely to detect i.e. sentiment, depression, education level, pedophilia or to prevent terrorism. Text mining is presented with untenable enthusiasm and a strong belief in its reading abilities. However, an actual training process is messy and chaotic. Although the eventual results are presented as being read or mined from the text, they show more similarities to something that is written. This research project shows how text mining results are never an absolute truth, but are constructed through a collaboration between the software, the metaphors that are used, the text miner and the authoritative power of language.
+''i-could-have-written-that'' is a two hour workshop, in which we will write between the lines, raw data will be cooked, and personal oracles will be trained. The workshop contains three parts. We start with a writing exercise in which we challenge the strictness of numbers with words. Then we will train our personal oracles by following the 1989 model Knowledge Discovery in Databases, and do manual pattern recognition with notecards and pens. After this hard work, we will ask three algorithms to do a bit of work as well. They will built your personal oracle, which you can consult at any time in the future!
+Many text mining services offer pre-trained classifiers through API's without specifying how the system is created. They promise to read meaning out of a text correctly. This unconditional trust is the basis of automated essay scoring systems (AES), psychology studies, job vacancy platforms and terrorism prevention. Automated systems are trained to detect sentiment, education level, mental illness, or suspicious behaviour. The techniques are used online in i.e. social media platforms, in the academic field and by the government. However, framing text mining techniques as readerly systems makes them immune for questions and discussion. When text mining results are considered as a product of writing, the presence of an author is emphasized. This makes it possible to discuss text mining results in relation to predefined goals and breaks the idea that text mining results represent an absolute truth.