If you are a Bowie fan, you will definitely enjoy this. If you are curious why other people are so into Bowie you will enjoy this. If you’ve never read any Critchley and are interested in something quick and accessible by him you will enjoy this. I fell into the first and third categories so I guess I’m guessing about the second. But I suspect it’s true.
I finished the book feeling like I understand the why and how of my own fascination with Bowie’s work much better. I also want to revisit some of his albums like Diamond Dogs, Heathen and Outside which I didn’t quite connect with at first. I would’ve enjoyed a continued discussion of Bowie’s use of the cutup technique, but I guess that fell out of the scope of the book.
I also want to read some more Critchley too – so if you have any recommendations please let me know. The sketches at the beginning of each chapter are wonderful. OR Books continues to impress.
There have been some interesting visualizations of languages in use on Twitter, like this one done by Gnip and published in the New York Times. Recently I’ve been involved in some research on particular a topical collection of tweets. One angle that’s been particularly relevant for this dataset is language. When perusing some of the tweet data we retrieved from Twitter’s API we noticed that there were two lang properties in the JSON. One was attached to the embedded user profile stanza, and the other was a top level property of the tweet itself.
We presumed that the user profile language was the language the user (who submitted the tweet) had selected, and that the second language on the tweet was the language of the tweet itself. The first is what Gnip used in its visualization. Interestingly, Twitter’s own documentation for the /get/statuses/:id API call only shows the user profile language.
When you send a tweet you don’t indicate what language it is in. For example you might indicate in your profile that you speak primarily English, but send some tweets in French. I can only imagine that detecting language for each tweet isn’t a cheap operation for the scale that Twitter operates at. Milliseconds count when you are sending 500 million tweets a day, in real time. So at the time I was skeptical that we were right…but I added a mental note to do a little experiment.
This morning I noticed my friend Dan had posted a tweet in Hebrew, and figured now was as a good a time as any.
I downloaded the JSON for the Tweet from the Twitter API and sure enough, the user profile had language en and the tweet itself had language iw which is the deprecated ISO 639-1 code for Hebrew (current is he. Here’s the raw JSON for the tweet, search for lang:
This one came across with lang fr. So having the text be a bit longer helped in this case. Admittedly this isn’t a very sound experiment, but it seems interesting and useful to see that Twitter is detecting language in tweets. It isn’t perfect, but that shouldn’t be surprising at all given the nature of human language. It might be useful to try a more exhaustive test using a more complete list of languages to see how it fairs. I’m adding another mental note…
These are some brief remarks I prepared for a 5 minute lightning talk at the Ferguson Town Hall meeting at UMD on December 3, 2014.
Thank you for the opportunity to speak here this evening. It is a real privilege. I’d like to tell you a little bit about an archive of 13 million Ferguson related tweets we’ve assembled here at the University of Maryland. You can see a random sampling of some of them up on the screen here. The 140 characters of text in a tweet only makes up about 2% of the data for each tweet. The other 98% includes metadata such as who sent it, their profile, how many followers they have, what tweet they are replying to, who they are retweeting, when the tweet was sent, (sometimes) where the tweet was sent from, embedded images and video. I’m hoping that I can interest some of you in studying this data.
I intentionally used the word privilege in my opening sentence to recognize that my ethnicity and my gender enabled me to be here speaking to you this evening. I’d actually like to talk very quickly about about a different set of privileges I have: those of my profession, and as a member of the UMD academic community that we are all a part of:
I remember a world before the Internet. And I remember what it really takes to have movement on the ground. Someone tweeted me back and said, “Well, you know, back in the day they didn’t have Twitter, but they had letters, and they wrote letters to each other, so…” I said, “Yeah, but ain’t nobody saying that the letters started the revolution.”
When I look at the Green Revolution, when I look what happened to Egypt, when I look at what happened to Occupy Wall Street, yeah, the tweets helped—they helped a lot—but without those bodies in the street, without the people actually being there, ain’t nothing to tweet about. If Twitter worked like that, Joseph Kony would be locked down in a jail right now.
Of course, Talib is right. It’s why we are all here this evening. In some sense it doesn’t matter what happens on Twitter. What matters is what happened in Ferguson, what is happening in Ferguson, and in meetings and demonstrations like this one all around the country.
Talib’s comparison of a tweet to a letter struck me as particularly insightful. I work as an archivist and software developer in the Maryland Institute for Technology in the Humanities here at UMD. Humanities scholars have traditionally studied a particular set of historical materials, of which letters are one. These materials form the heart of what we call the archive. What gets collected in archives and studied is inevitably what forms our cultural canon. It is a site of controversy, for as George Orwell wrote in 1984:
Who controls the past controls the future. Who controls the present controls the past.
Would we be here tonight if it wasn’t for Twitter? Would the President be talking about Ferguson if it wasn’t for the groundswell of activity on Twitter? Without Twitter what would the main stream media have reported about Mike Brown, John Crawford, Eric Garner, Renisha McBride, Trayvon Martin, and Tamir Rice? As you know this list of injustices is long…it is vast and overwhelming. It extends back to the beginnings of this state and this country. But what trace do we have of these injustices and these struggles in our archives?
The famed historian and social activist Howard Zinn said this when addressing a group of archivists in 1970:
the existence, preservation and availability of archives, documents, records in our society are very much determined by the distribution of wealth and power. That is, the most powerful, the richest elements in society have the greatest capacity to find documents, preserve them, and decide what is or is not available to the public. This means government, business and the military are dominant.
This is where social media and the Web present such a profoundly new opportunity for us, as we struggle to understand what happened in Ferguson…as we struggle to understand how best to act in the present. We need to work to make sure the voices of Ferguson are available for study–and not just in the future, but study now. Let’s put our privilege as members of this academic community to work. Is there something to learn in these 13 million tweets, these letters from ordinary people. The thousands of videos and photographs, and links to stories? I think there is. I’m hopeful that these digital traces provide us with a new insight into an old problem…insights that can guide our actions here in the present.
If you have questions you’d like to ask of the data please get in touch with either me or Neil Fraistat (Director of MITH) here tonight, or via email or Twitter.
Every document, every moment in every document, conceals (or reveals) an indeterminate set of interfaces that open into alternate spaces and temporal relations.
Traditional criticism will engage this kind of radiant textuality more as a problem of context than a problem of text, and we have no reason to fault that way of seeing the matter. But as the word itself suggests, “context” is a cognate of text, and not in any abstract Barthesian sense. We construct the poem’s context, for example, by searching out the meanings marked in the physical witnesses that bring the poem to us. We read those witnesses with scrupulous attention, that is to say, we make our detailed way through the looking glass of the book and thence to the endless reaches of the Library of Babel, where every text is catalogued and multiple cross-referenced. In making the journey we are driven far out into the deep space, as we say these days, occupied by our orbiting texts. There objects pivot about many different points and poles, the objects themselves shapeshift continually and the pivots move, drift, shiver, and even dissolve away. Those transformations occur because “the text” is always a negotiated text, half perceived and half created by those who engage with it.
After writing about the Ferguson Twitter archive a few months ago three people have emailed me out of the blue asking for access to the data. One was a principal at a small, scaryish defense contracting company, and the other two were from a prestigious university. I’ve also had a handful of people interested where I work at the University of Maryland.
I ignored the defense contractor. Maybe that was mean, but I don’t want to be part of that. I’m sure they can go buy the data if they really need it. My response to the external academic researchers wasn’t much more helpful since I mostly pointed them to Twitter’s Terms of Service which says:
If you provide Content to third parties, including downloadable datasets of Content or an API that returns Content, you will only distribute or allow download of Tweet IDs and/or User IDs.
You may, however, provide export via non-automated means (e.g., download of spreadsheets or PDF files, or use of a “save as” button) of up to 50,000 public Tweets and/or User Objects per user of your Service, per day.
Any Content provided to third parties via non-automated file download remains subject to this Policy.
It’s my understanding that I can share the data with others at the University of Maryland, but I am not able to give it to the external parties. What I can do is give them the Tweet IDs. But there are 13,480,000 of them.
So that’s what I’m doing today: publishing the tweet ids. You can download them from the Internet Archive:
On the one hand, it seems unfair that this portion of the public record is unshareable in its most information rich form. The barrier to entry to using the data seems set artificially high in order to protect Twitter’s business interests. These messages were posted to the public Web, where I was able to collect them. Why are we prevented from re-publishing them since they are already on the Web? Why can’t we have lots of copies to keep stuff safe? More on this in a moment.
Twitter limits users to 180 requests every 15 minutes. A user is effectively a unique access token. Each request can hydrate up to 100 Tweet IDs using the statuses/lookup REST API call.
So to hydrate all of the 13,480,000 tweets will take about 7.8 days. This is a bit of a pain, but realistically it’s not so bad. I’m sure people doing research have plenty of work to do before running any kind of analysis on the full data set. And they can use a portion of it for testing as it is downloading. But how do you download it?
Gnip, who were recently acquired by Twitter, offer a rehydration API. Their API is limited to tweets from the last 30 days, and similar to Twitter’s API you can fetch up to 100 tweets at a time. Unlike the Twitter API you can issue a request every second. So this means you could download the results in about 1.5 days. But these Ferguson tweets are more than 30 days old. And a Gnip account costs some indeterminate amount of money, starting at $500…
I suspect there are other hydration services out there. But I adapted <a href="http://github.com/edsu/twarc’>twarc the tool I used to collect the data, which already handled rate-limiting, to also do hydration. Once you have the tweet IDs in a file you just need to install twarc, and run it. Here’s how you would do that on an Ubuntu instance:
After a week or so, you’ll have the full JSON for each of the tweets.
Well, not really. You will have most of them. But you won’t have the ones that have been deleted. If a user decided to remove a Tweet they made, or decided to remove their account entirely you won’t be able to get their Tweets back from Twitter using their API. I think it’s interesting to consider Twitter’s Terms of Service as what Katie Shilton would call a value lever.
The metadata rich JSON data (which often includes geolocation and other behavioral data) wasn’t exactly posted to the Web in the typical way. It was made available through a Web API designed to be used directly by automated agents, not people. Sure, a tweet appears on the Web but it’s in with the other half a trillion Tweets out on the Web, all the way back to the first one. Requiring researchers to go back to the Twitter API to get this data and not allowing it circulate freely in bulk means that users have an opportunity to remove their content. Sure it has already been collected by other people, and it’s pretty unlikely that the NSA are deleting their tweets. But in a way Twitter is taking an ethical position for their publishers to be able to remove their data. To exercise their right to be forgotten. Removing a teensy bit of informational toxic waste.
As any archivist will tell you, forgetting is an essential and unavoidable part of the archive. Forgetting is the why of an archive. Negotiating what is to be remembered and by whom is the principal concern of the archive. Ironically it seems it’s the people who deserve it the least, those in positions of power, who are often most able to exercise their right to be forgotten. Maybe putting a value lever back in the hands of the people isn’t such a bad thing. If I were Twitter I’d highlight this in the API documentation. I think we are still learning how the contours of the Web fit into the archive. I know I am.
It seems like an important move for MIT to formally recognize that social media is a new medium that deserves its own research focus, and investment in infrastructure. The language on the homepage gives a nice flavor for the type of work they plan to be doing. I was particularly struck by their frank assessment of how our governance systems are failing us, and social media’s potential role in understanding and helping solve the problems we face:
In a time of growing political polarization and institutional distrust, social networks have the potential to remake the public sphere as a realm where institutions and individuals can come together to understand, debate and act on societal problems. To date, large-scale, decentralized digital networks have been better at disrupting old hierarchies than constructing new, sustainable systems to replace them. Existing tools and practices for understanding and harnessing this emerging media ecosystem are being outstripped by its rapid evolution and complexity.
Their notion of “social machines” as “networked human-machine collaboratives” reminds me a lot of my somewhat stumbling work on (???) and archiving Ferguson Twitter data. As Nick Diakopoulos has pointed out we really need a theoretical framework for thinking about what sorts of interactions these automated social media agents can participate in, formulating their objectives, and for measuring their effects. Full disclosure: I work with Nick at the University of Maryland, but he wrote that post mentioning me before we met here, which was kind of awesome to discover after the fact.
Some of the news stories about the Twitter/MIT announcement have included this quote from Deb Roy from MIT who will lead the LSM:
The Laboratory for Social Machines will experiment in areas of public communication and social organization where humans and machines collaborate on problems that can’t be solved manually or through automation alone.
What a lovely encapsulation of the situation we find ourselves in today, where the problems we face are localized and yet global. Where algorithms and automation are indispensable for analysis and data gathering, but people and collaborative processes are all the more important. The ethical dimensions to algorithms and our understanding of them is also of growing importance, as the stories we read are mediated more and more by automated agents. It is super that Twitter has decided to help build this space at MIT where people can answer these questions, and have the infrastructure to support asking them.
When I read the quote I was immediately reminded of the problem that some of us were discussing at the last Society of American Archivists meeting in DC: how do we document the protests going on in Ferguson?
Much of the primary source material was being distributed through Twitter. Internet Archive were looking for nominations of URLs to use in their web crawl. But weren’t all the people tweeting about Ferguson including URLs for stories, audio and video that were of value? If people are talking about something can we infer its value in an archive? Or rather, is it a valuable place to start inferring from?
I ended up archiving 13 million of the tweets that mention “ferguson” for the 2 week period after the killing of Michael Brown. I then went through the URLs in these tweets, and unshortened them and came up with a list of 417,972 unshortened URLs. You can see the top 50 of them here, and the top 50 for August 10th (the day after Michael Brown was killed) here.
I did a lot of this work in prototyping mode, writing quick one off scripts to do this and that. One nice unintended side effect was unshrtn which is a microservice for unshortening URLs, which John Kunze gave me the idea for years ago. It gets a bit harder when you are unshortening millions of URLs.
But what would a tool look like that let us analyze events in social media, and helped us (archivists) collect information that needs to be preserved for future use? These tools are no doubt being created by those in positions of power, but we need them for the archive as well. We also desperately need to explore what it means to explore these archives: how do we provide access to them, and share them? It feels like there could be a project here along the lines of what George Washington University University are doing with their Social Feed Manager. Full disclosure again: I’ve done some contracting work with the fine folks at GW on a new interface to their library catalog.
The 5 million dollars aside, an important contribution that Twitter is making here (that’s probably worth a whole lot more) is firehose access to the Tweets that are happening now, as well as the historic data. I suspect Deb Roy’s role at MIT as a professor and as Chief Media Scientist at Twitter helped make that happen. Since MIT has such strong history of supporting open research, it will be interesting to see how the LSM chooses to share data that supports its research.