This year we had busy CSDH and CGSA meetings at Congress 2018 in Regina. My conference notes are here. Some of the papers I was involved in include:
- “Code Notebooks: New Tools for Digital Humanists” was presented by Kynan Ly and made the case for notebook-style programming in the digital humanities.
- “Absorbing DiRT: Tool Discovery in the Digital Age” was presented by Kaitlyn Grant. The paper made the case for tool discovery registries and explained the merger of DiRT and TAPoR.
- “Splendid Isolation: Big Data, Correspondence Analysis and Visualization in France” was presented by me. The paper talked about FRANTEXT and correspondence analysis in France in the 1970s and 1980s. I made the case that the French were doing big data and text mining long before we were in the Anglophone world.
- “TATR: Using Content Analysis to Study Twitter Data” was a poster presented by Kynan Ly, Robert Budac, Jason Bradshaw and Anthony Owino. It showed IPython notebooks for analyzing Twitter data.
- “Climate Change and Academia – Joint Panel with ESAC” was a panel I was on that focused on alternatives to flying for academics.
- “Archiving an Untold History” was presented by Greg Whistance-Smith. He talked about our project to archive John Szczepaniak’s collection of interviews with Japanese game designers.
- “Using Salience to Study Twitter Corpora” was presented by Robert Budac who talked about different algorithms for finding salient words in a Twitter corpus.
- “Political Mobilization in the GG Community” was presented by ZP who talked about a study of a Twitter corpus that looked at the politics of the community.
Also, a PhD student I’m supervising, Sonja Sapach, won the CSDH-SCHN (Canadian Society for Digital Humanities) Ian Lancashire Award for Graduate Student Promise at CSDHSCHN18 at Congress. The Award “recognizes an outstanding presentation at our annual conference of original research in DH by a graduate student.” She won the award for a paper on “Tagging my Tears and Fears: Text-Mining the Autoethnography.” She is completing an interdisciplinary PhD in Sociology and Digital Humanities. Bravo Sonja!
On May 25th I had a chance to attend a gem of a conference organized the Philosophy of Education (POE) committee at George Brown. They organized a conference with different modalities from conversations to formal talks to group work. The topic was Re-Imagining Education in An Automating World (see my conference notes here) and this conference is a seed for a larger one next year.
I gave a talk on Digital Citizenship at the end of the day where I tried to convince people that:
- Data analytics are now a matter of citizenship (we all need to understand how we are being manipulated).
- We therefore need to teach data literacy in the arts and humanities, so that
- Students are prepared to contribute to and critique the ways analytics are used deployed.
- This can be done by integrating data and analytical components in any course using field-appropriate data.
A paper that Stéfan Sinclair and wrote about Peter Luhn and the Keyword-in-Context (KWIC) has just been published by the Fudan Journal of the Humanities and Social Sciences, Too Much Information and the KWIC | SpringerLink. The paper is part of a series that replicates important innovations in text technology, in this case, the development of the KWIC by Peter Luhn at IBM. We use that as a moment to reflect on the datafication of knowledge after WW II, drawing on Lyotard.
Google has announced some cool text projects. See Google AI experiment has you talking to books. One of them, Talk to Books, lets you ask questions or type statements and get answers that are passages from books. This strikes me as a useful research tool as it allows you to see some (book) references that might be useful for defining an issue. The project is somewhat similar to the Veliza tool that we built into Voyant. Veliza is given a particular text and then uses an Eliza-like algorithm to answer you with passages from the text. Needless to say, Talking to Books is far more sophisticated and is not based simply on word searches. Veliza, on the other hand can be reprogrammed and you can specify the text to converse with.
Continue reading Google AI experiment has you talking to books
Cambridge Analytica harvested personal information from a huge swath of the electorate to develop techniques that were later used in the Trump campaign.
The New York Times has just published a story about How Trump Consultants Exploited the Facebook Data of Millions. The story is about how Cambridge Analytica, the US arm of SCL, a UK company, gathered a massive dataset from Facebook with which to do “psychometric modelling” in order to benefit Trump.
The Guardian has been reporting on Cambridge Analytica for some time – see their Cambridge Analytica Files. The service they are supposed to have provided with this massive dataset was to model types of people and their needs/desires/politics and then help political campaigns, like Trump’s, through microtargeting to influence voters. Using the models a campaign can create content tailored to these psychometrically modelled micro-groups to shift their opinions. (See articles by Paul-Olivier Dehaye about what Cambridge Analytica does and has.)
What is new is that there is a (Canadian) whistleblower from Cambridge Analytica, Christopher Wylie who was willing to talk to the Guardian and others. He is “the data nerd who came in from the cold” and he has a trove of documents that contradict what other said.
The Intercept has a earlier and related story about how Facebook Failed to Protect 30 Million Users From Having Their Data Harvested By Trump Campaign Affiliate. This tells how people were convinced to download a Facebook app that then took your data and that of their friends.
It is difficult to tell how effective the psychometric profiling with data is and if can really be used to sway voters. What is clear, however, is that Facebook is not really protecting their users’ data. To some extent their set up to monetize such psychometric data by convincing those who buy access to the data that you can use it to sway people. The problem is not that it can be done, but that Facebook didn’t get paid for this and are now getting bad press.
The question I want to explore today is this: what do we do about distant reading, now that we know that Franco Moretti, the man who coined the phrase “distant reading,” and who remains its most famous exemplar, is among the men named as a result of the #MeToo movement.
Lauren Klein has posted an important blog entry on Distant Reading after Moretti. This essay is based on a talk delivered at the 2018 MLA convention for a panel on Varieties of Digital Humanities. Klein asks about distant reading and whether it shelters sexual harassment in some way. She asks us to put not just the persons, but the structures of distant reading and the digital humanities under investigation. She suggests that it is “not a coincidence that distant reading does not deal well with gender, or with sexuality, or with race.” One might go further and ask if the same isn’t true of the digital humanities in general or the humanities, for that matter. Klein then suggests some thing we can do about it:
- We need more accessible corpora that better represent the varieties of human experience.
- We need to question our models and ask about what is assumed or hidden.
Last week I presented a paper based on work that Stéfan Sinclair and I are doing at the University of South Florida. The talk, titled, “Cooking Up Literature: Theorizing Statistical Approaches to Texts” looked at a neglected period of French innovation in the 1970s and 1980s. During this period the French were developing a national corpus, FRANTEXT, while there was also a developing school of exploratory statistics around Jean-Paul Benzécri. While Anglophone humanities computing was concerned with hypertext, the French were looking at using statistical methods like correspondence analysis to explore large corpora. This is long before Moretti and “distant reading.”
The talk was organized by Steven Jones who holds the DeBartolo Chair in Liberal Arts and is a Professor of Digital Humanities. Steven Jones leads a NEH funded project called RECALL that Stéfan and I are consulting on. Jones and colleagues at USF are creating a 3D model of Father Busa’s original factory/laboratory.
Last week I presented a keynote at the Digital Cultures, Big Data and Society conference. (You can seem my conference notes at Digital Cultures Big Data And Society.) The talk I gave was titled “Thinking-Through Big Data in the Humanities” in which I argued that the humanities have the history, skills and responsibility to engage with the topic of big data:
- First, I outlined how the humanities have a history of dealing with big data. As we all know, ideas have histories, and we in the humanities know how to learn from the genesis of these ideas.
- Second, I illustrated how we can contribute by learning to read the new genres of documents and tools that characterize big data discourse.
- And lastly, I turned to the ethics of big data research, especially as it concerns us as we are tempted by the treasures at hand.
Continue reading Digital Cultures Big Data And Society
Having just finished teaching a course on Big Data and Text Analysis where I taught students Python I can appreciate a well written tutorial on Python. Python Programming for the Humanities by Folgert Karsdorp is a great tutorial for humanists new to programming that takes the form of a series of Jupyter notebooks that students can download. As the tutorials are notebooks, if students have set up Python on their computers then they can use the tutorials interactively. Karsdorp has done a nice job of weaving in cells where the student has to code and Quizes which reinforce the materials which strikes me as an excellent use of the IPython notebook model.
I learned about this reading a more advanced set of tutorials from Allen Riddell for Dariah-DE, Text Analysis with Topic Models for the Humanities and Social Sciences. The title doesn’t do this collection of tutorials justice because they include a lot more than just Topic Models. There are advanced tutorials on all sorts of topics like machine learning and classification. See the index for the range of tutorials.
Text Analysis with Topic Models for the Humanities and Social Sciences (TAToM) consists of a series of tutorials covering basic procedures in quantitative text analysis. The tutorials cover the preparation of a text corpus for analysis and the exploration of a collection of texts using topic models and machine learning.
Stéfan Sinclair and I (mostly Stéfan) have also produced a textbook for teaching programming to humanists called The Art of Literary Text Analysis. These tutorials are also written as Jupyter notebooks so you can download them and play with them.
We are now reimplementing them with our own Voyant-based notebook environment called Spyral. See The Art of Literary Text Analysis with Spyral Notebooks. More on this in another blog entry.
This directory contains 450 novels that appeared between 1770 and 1930 in German, French and English. It is designed for us in teaching and research.
Andrew Piper mentioned a corpus that he put together, txtlab Multilingual Novels. This corpus is of some 450 novels from the late 18th century to the early 20th (1920s). It has a gender mix and is not only English novels. This corpus was supported by SSHRC through the Text Mining the Novel project.