Bridges Sing

This video was originally performed at the Conference on College Composition and Communication in 2017. It includes mobile phone footage from the conference location, Portland, Oregon. This footage was combined with videos and a partial audio track. For the academic presentation, the author spoke over this partial audio track. Afterwards, a new audio track with spoken narration was recorded for the version here.

The video includes screen recordings of student projects and web materials. These materials provide samples that demonstrate engagements with data. There is also a dialog between the narrator and a computer-generated voice. A musical track plays quietly in parts of the video. A motif related to trains also appears in the sonic and visual elements of the piece.


00:00 The video opens with a title: "Tweet Analysis, Ambiguity, and Storytelling." A cursor appears on screen and adds text to the title that reads "A Dialog." Below the title appears the text: "Daniel Anderson" The cursor adds "@iamdan." The cursor highlights and then deletes all of the text on the title screen, and then begins typing. Video footage of bridges plays in the top fifth of the screen. An image of Donald Trump flashes briefly on screen, and then a computer-generated voice reads aloud the text being typed: "See there is this thing called writing. And it's great."

00:30 The narrator's voice responds: "I love writing. But I'm supposed to be here to talk about data. Writing . . . ah, I mean data is so cool. Look at how it helps us make sense of something like an election." On screen, web articles discussing the 2016 US presidential election appear. The narrator continues: "Okay. Maybe I should rethink that. The 2016 election was about the death of data, and the rise of stories."

00:52 The computer-generated voice speaks: "It's not that politics don't matter, but writing, now that's a human activity." A quote from Peggy Noonan about Americans' relationship to data appears in the top fifth of the screen: "In America now only normal people can see the obvious. Everyone else is lost in a data-filled fog."

01:06 Several layers of video and images appear on screen, including video footage of a man looking out a train window facing a bridge, screenshots of articles about the election, and a zoomable map of Portland, Oregon taken from Google Maps. The narrator's voice responds: "I don't know. Death is so serious. At most, maybe data just had a bad day. And if the point is to tell stories and write our lives, data is doing that even as we speak. And it's kind of already political. And it's kind of taken on a life of its own." On screen, an image of a website with a class assignment appears. The window shows the title of the assignment: "Curating and Analyzing Tweets." The narrator continues: "Here it is built into an assignment." On screen, an image of a website with an article appears. The window shows the title of the article: "Baby We Were Born to Tweet: Springsteen Fans, the Writing Practices of In Situ Tweeting, and the Research Possibilities for Twitter." The narrator continues: "And here is someone using it for scholarship in the humanities, telling stories with it."

01:37. The narrator's voice continues: "And it's really kind of interesting to play around with. We used this spreadsheet. It was surprisingly easy to gather an archive of tweets. And then we could think about the terms that we might use to make sense of the many tweets. We learned to think about data and tell some stories—almost feels like a campfire."

01:54 On screen, a spreadsheet displays columns of tweets and related data. The narrator continues: "And look what the machine put together for us. It's so cool, all referenced with metadata and sortable. And watch this, we can run the tweets through a sentiment analysis algorithm. So cool." On screen, a website appears showing sentiment analysis figures for the Twitter hashtag #posttruth. The figure shows a pie chart and bar graph with positive sentiment in green (twenty-three responses, 85%) and negative sentiment in red (four responses, 15%).

02:09 On screen, a white layer appears and a cursor starts typing. The computer-generated voice speaks the text being typed: "I don't think we are talking about the same thing. Sometimes you still need to draw a line."

02:26 The narrator's voice responds: "I'm with you, probability 100. That computational analysis of posttruth is bogus."

02:32 On screen, tweets on the sentiment analysis website are scrolled through. The narrator continues: "Look at those tweets. Debates on the topic. Dystopia. A wake-up call. Posttruth is no positive development."

02:40 The sound of a train horn is heard at high volume. The computer-generated voice speaks: "What is that?"

02:45 As tweets continue to appear on screen, the narrator responds: "That's a train horn. They were amazing network makers in the analog days. Their horns sound lonesome."

02:50 The computer-generated voice speaks: "I know that. I have access to all the databases."

02:55 As video of a train in a station faintly appears on screen, overlaid with the spreadsheet from the class assignment, the narrator speaks: "It's cool that they run in Portland right through town, down by the river."

03:00 The computer-generated voice speaks: "Yeah. Their horns sound lonesome."

03:05 The assignment spreadsheet fills the screen. The narrator speaks: "Anyway, back to the tweets. We got about 1,000 of them cleaned up. And then we started assigning meaning to them—metadata. We all worked on the same spreadsheet, filling in data. And then our spreadsheet gave us these tools to computationally assist our coding."

03:25 On screen, a pie chart in a spreadsheet shows the results of human-generated coding for positive and negative sentiment, with positive sentiment in yellow (5.3%), neutral sentiment in red (32.7%), and negative sentiment in blue (62%). The narrator's voice continues: "The tweets weren't positive, obviously. We only found a positive sentiment 5.3 percent of the time."

03:33 The computer-generated voice speaks: "I still don't think we are on the same page. I'm talking shady characters and fuzzy operations, deep intrigue, the stuff of storytelling."

03:42 As a network visualization appears on screen, the narrator speaks: "Oh, this is fuzzy, and shady. Our 5.3 percent is just one study, but it feels right. Come on, just between me and you: it's a lot of counting, interpreting, and representing. Maybe we can focus on the stories coming out of that process. Let's look at those."

04:00. On screen, a student video containing another spreadsheet appears. It shows data for the hashtags #notmypresident and #blacklivesmatter. A student voice speaks: "I have chosen to mark as well as I can the emotions of each one within certain broad categories, the temporal focus, when is the tweet referring to. And does the tweet include a call to action no matter how big or how small."

04:20 The narrator speaks, over the sound of soft music and, at one point, the sound of a train: "So, I asked everyone to share their discoveries from working with the data. Really, it's pretty much like writing. It's framing things, asking questions, representing ideas. And trying to make change in the world. In fact, the best part of the experience is the way it helps us question our basic assumptions."

04:39 On screen, a student video appears, showing footage of people and places all over the world. A student voice narrates the video on screen: "Humans need to categorize everything. That's the only way we can navigate in this world. But putting everything in boxes is problematic. What happens when the object does not fit? In the action of defining lines for when a term is in a category and when it's outside lies a lot of judgment. Every word makes up for its own category: woman, sci-fi, horror, beautiful, nationality, clever, left-wing, truth, depression, art, etc. In the following I wish to investigate the three categories: 'human,' 'computer,' and, um, the category, 'category.'"

05:23 On screen, aerial footage shows two bridges over a river. The scene moves down the river as the camera flies over the bridges. In the background, sparse guitar sounds are heard as a music track begins to play softly. The narrator speaks: "I remember my dad told me a story about human development. He said, much boils down to accidents of eyes and ears. Somehow our eyes and ears evolved to either side of our heads, giving us binary brains, with switching and signals. He liked the long view. Just this week he told me things sometimes get worse before they get better. He said, what we've done follows from that change, from that binary brain. Our sight. Our smell. The way we walk. Our bridges. The streams of vapor we leave tracing the sky." The intensity of the song in the background begins to pick up.

06:05 On screen, the camera continues to move over bridges and the river. The image of PC and Mac characters from Apple's "Get a Mac" advertising campaign appear on screen, along with source code showing a poem. The narrator continues speaking: "I once wrote some poems about the organic mechanic. I layered one beneath an article about Mac and PC characters. The poems were about metaphors and the aesthetics and power of the materials and design of machines. The space shuttle. The telephone. I can tell, you bridges sing, my friend. Make no mistake about it, bridges sing."

06:36 The computer-generated voice speaks, as typing appears on screen: "Look, you can mix together all the people and machines you want; I still want to hear the sound of a human voice." The musical track continues to play.

06:46 The narrator speaks: "I hear you."

06:48 The computer-generated voice and the narrator speak in unison: "Amen to that, my friend, amen." On screen, the camera flies over a bridge on which two light-rail trains are crossing. The musical track fades out. The video ends.

This video includes samples from articles: a news item opens up the piece, and a screenshot of a scholarly article related to qualitative analysis of Twitter data is included. The piece also makes use of several images that depict spreadsheets that have been populated with tweet-related data using the TAGS (Twitter Archiving Google Spreadsheet) tool. These spreadsheets (developed as part of class activities) include categories for making sense of the collected tweets.

In the latter half of the video, excerpts from "Portland, Oregon—Bridges from a Drone" are layered among other materials while a performance of the song "Your Hand in Mine" by the band Explosions in the Sky is brought into the soundtrack. Opaque images depicting downtown Portland in Google Maps are also included. The piece also includes excerpts from two projects created in a class called Digital Literature. For the projects, students studied Twitter conversations, and then produced a video exploring culture, community, and identity.


"5x5 Portland Bridges." Vimeo, uploaded by Alex Itin, April 1, 2008,

Anderson, Daniel. “Digital Literature.” Department of English and Comparative Literature, UNC Chapel Hill, August 23, 2016,

Anderson, Daniel. "I'm a Map I'm a Green Tree." Kairos: A Journal of Rhetoric, Technology, and Pedagogy 15, no. 1 (2010):

Barthes, Roland. Image Music Text. Translated by Stephen Heath. New York: Hill and Wang, 1997,

Bogost, Ian. Alien Phenomenology, or What It's Like to Be a Thing. Minneapolis: University of Minnesota Press, 2012.

"BRIDGETOWN—Portland, Oregon—Bridges from a Drone." YouTube, uploaded by FlyWork Drone Services, May 19, 2015,

Brown, James J., Jr. Ethical Programs: Hospitality and the Rhetorics of Software. Ann Arbor: University of Michigan Press, 2015.

Crawford, Matthew B. Shop Class as Soulcraft: An Inquiry into the Value of Work. Harmondsworth, UK: Penguin, 2009.

Drucker, Johanna. "Humanities Approaches to Graphical Display." Digital Humanities Quarterly 5, no. 1 (2011).

Engel, Adam. "Visualizing Frustrated Desire and Its Political Consequences (draft)." Vimeo, uploaded by Adam Engel, December 3, 2016,

Explosions in the Sky. "Your Hand in Mine." YouTube, uploaded by FairyBrownie6, March 17, 2009,

"Final Research Draft 2." Vimeo, uploaded by Nete Bier, December 3, 2016,

Go, Alec, et al. “Sentiment140 [Sentiment analysis for #posttruth].” Google Sites. Accessed March 17, 2017.

Harman, Graham. Guerrilla Metaphysics. Chicago: Open Court, 2005.

Hawksey, Martin. TAGS (Twitter Archiving Google Spreadsheet). June 2010. Accessed March 17, 2017.

O'Neil, Cathy. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy . New York: Broadway, 2017.

"Portland, Oregon." Map. Google Maps. Accessed March 17, 2017.

Wolff, Michael. "Trump Win Exposes Media's Smug Failures." Hollywood Reporter, November 9, 2016.

Wolff, William I. "Baby We Were Born to Tweet: Springsteen Fans, the Writing Practices of In Situ Tweeting, and the Research Possibilities for Twitter." Kairos: A Journal of Rhetoric, Technology, and Pedagogy 19, no. 3 (2015).


This video was originally created as part of a panel presentation on data in composition studies. This was a particularly challenging presentation, as many were experiencing a sense of data shock after the 2016 US presidential election. At the time, questions about the role of data in political campaigns were pressing. And scholars were interrogating relationships among data, truth, and storytelling, wrestling with the sense that data and machines had gotten everything wrong. Bridges Sing explores these concerns through dialog between the narrator's voice and a voice created through text-to-speech processing. The computer's voice takes on the despondence associated with the "death of data" stories in the news after the election. The human narrator consoles the machine by pointing out their connections. James Brown Jr. explains that human-machine boundaries blur as algorithms act as rhetorical devices and as humans develop "machinic thinking" (139). This overlap extends to the relationships between data and narrative, since "the interpretation of data will always require narrative" (138) and "narrative and data are not separable" (136). Much like the processes and operating systems of computers, from Brown's perspective, the approaches that humans develop can be seen as "machines that enact ethical programs and solve problems" (139).

Though they work together in the dialog, the human and machine are not the same. At around 01:26 in the video, the chime of a text message alerts us to human engagements beyond the screen and the creative energies of serendipity. Roland Barthes tells us, as we create or interpret messages, "the language must include the 'surprises' of meaning" (47). But surprises of meaning run counter to typical approaches to computers and data. Cathy O'Neil warns of the efficiency that filters out any noisy surprises "as automated systems [chomp] away on our error-ridden dossiers" (155). When it comes to data and machines, the difficulties extend beyond the glitches that arise with surprise. "For all their advances in logic and language," O'Neil explains, computers "still struggle mightily with concepts. They 'understand' beauty only as a word associated with the Grand Canyon, ocean sunsets, and grooming tips in Vogue magazine" (95). Beautiful surprises belong to humans, though we long to extend them to our computational companions.

screenshot of Bridges Sing video

The text-to-speech function gives voice to posthuman agency associated with computers

The dialog between the narrator and data is linked to networks and machines. At 02:44, another unexpected sound, a train horn, disrupts the video. The conversation veers as the narrator describes the emotional tenor of the horn. The surprise of the machine indicates the experience of the sound is new. Here, we reach one conclusion of posthuman extensions of agency to nonhuman actors. These thing-agencies are always elusive. As Ian Bogost sums it up, "Things constantly machinate within themselves and mesh with one another, acting and reacting to properties and states while still keeping something secret" (27). Graham Harman describes these interactions as "interconnected, [but] always through indirect or vicarious means" (192). The "properties and states" of trains are linked with our own moments of apprehension and with human industry, and they recede even as they emerge in the interrupting sound of the horn, which is itself ambient and flowing with affect and ambiguity. It's no wonder the computer has trouble processing it.

A longing for connection plays out in the dialog between human and machine, particularly in the closing segments of the video. At 06:17, the visuals and narration take up a series of poems created by the author. The poems explore the beauty of machinic connections and the ways humans express creativity and capture stories through (and with) machines. Matthew Crawford suggests that the mechanic who sees beneath the surface "cares about the motor, in a personal way" (95). The poems celebrating this involvement were hidden in the code of an earlier publication, and one is brought to the surface here:

The organic mechanic
sets his ring tone,
choosing bird chirps:
the peep in the poplar or,
sometime later,
in the inking evening
the distant owl
yopping its wahoo!