Digifesto

Tag: center for investigative reporting

notes on innovation in journalism

I’ve spent the better part of the past week thinking hard about journalism. This is due largely to two projects: further investigation into Weird Twitter, and consulting work I’ve been doing with the Center for Investigative Reporting. Journalism, the trope goes, is a presently disrupted industry. I’d say it’s fair to say it’s a growing research interest for me. So here’s the rundown on where things seem to be at.

Probably the most rewarding thing to come out of the fundamentally pointless task of studying Weird Twitter, besides hilarity, is getting a better sense of the digital journalism community. I’ve owed Ethnography Matters a part 2 for a while, and it seems like the meatiest bone to pick is still on the subject of attention economy. The @horse_ebooks/Buzzfeed connection drives that nail in deeper.

I find content farming pretty depressing and only got more depressed reading Dylan Love’s review of MobileWorks that he crowdsourced to crowdworkers using MobileWorks. I mean, can you think of a more dystopian world than one in which the press is dominated by mercenary crowdworkers pulling together plausible-sounding articles out of nowhere for the highest bidder.

I was feeling like the world was going to hell until somebody told me about Oximity, which is a citizen journalist platform, as opposed to a viral advertising platform. Naturally, this has a different flavor to it, though is less monetized/usable/populated. Hmm.

I spend too much time on the Internet. That was obvious when attending CIR’s Dissection:Impact events on Wednesday and Thursday. CIR is a foundation-funded non-profit that actually goes and investigates things like prisons, migrant farm workers, and rehab clinics. The people there really turned my view of things around, as I realized that there are still people out there dedicated to using journalism to do good in the world.

There were three interesting presentations with divergent themes.

One was a presentation of ConText, a natural language and network processing toolkit for analyzing the discussion around media. It was led by Jana Deisner at the I School at Urbana-Champaign. Her dissertation work was on covert network analysis to detect white collar criminals. They have a thoroughly researched impact model, and software is currently unusable by humans but combines best practices in text and network analysis. The intend to release it as an academic tool for researchers, open source.

Another was a presentation by Harmony Institute, which has high-profile clients like MTV. Their lead designer walked us through a series of compelling mockups of ImpactSpace, an impact analysis tool that shows the discussion around an issue as “constellations” through different “solar systems” of ideas. Their project promises to identify how one can frame a story to target swing viewers. But they were not specific about how they would get and process the data. They intend to make demos of thir service available on-line, and market it as a product.

The third presentation was by CIR itself, which has hired a political science post-doc to come up with an analysis framework. They focused on a story, “Rape in the Fields”, about sexual abuse of migrant farm workers. These people tend not to be on Twitter, but the story was a huge success on Univision. Drawing mainly on qualitative data, it considers “micro”, “mezo”, and “macro” impact. Micro interactions might be eager calls to the original journalist for more information, or powerful anectdotes of how somebody hurt had felt healed when they were able to tell their story to the world.

Each team has their disciplinary bias and their own strengths and weaknesses. But they are tackling the same problem: trying to evaluate the effectiveness of media. They know that data is powerful: CIR uses it all the time to find stories. They will sift through a large data set, look for anomalies, and then carefully investigate. But even when collaborative science, including “data science” components, is effectively used to do external facing research, the story gets more difficult, intellectually and politically, when it turns that kind of thinking reflexively on itself.

I think this story sounds a lot like the story of what’s happening in Berkeley. A disrupted research organization struggles to understand its role in a changing world under pressure to adapt to data that seems both ubiquitous and impoverished.
Does this make you buy into the connection between universities and journalism?

If it does, then I can tell you another story about how software ties in. If not, then I’ve got deeper problems.

There is an operational tie: D-Lab and CIR have been in conversation about how to join forces. With the dissolution of disciplines, investigative reporting is looking more and more like social science. But its the journalists who are masters of distribution and engagement. What can we learn about the imoact of social science research from journalists? And how might the two be better operationally linked?

The New School sent some folks to the Dissection event to talk about the Open Journalism program they are starting soon.

I asked somebody at CIR what he thought about Buzzfeed. He explained that it’s the same business model as HuffPo–funding real journalism with the revenue from the crappy clickbait. I hope that’s true. I wonder if they would suffer as a business if they only put out clickbait. Is good journalism anything other than clickbait for the narrow segment of the population that has expensive taste in news?

The most interesting conversation I had was with Mike Corey at CIR, who explained that there are always lots of great stories, but that the problem was that newspapers don’t have space to run all the stories, they are an information bottleneck. I found this striking because I don’t get my media from newspapers any more, and it revealed that the shifting of the journalism ecosystem is still underway. Thinking this through…

In the old model, a newspaper (or radio show, or TV show) had limited budget to distributed information, and so competed for prestige with creativity and curational prowess. Naturally they targeted different audiences, but there was more at stake in deciding what to and what not to report. (The unintentional past tense here just goes to show where I am in time, I guess.)

With web publishing, everybody can blog or tweet. What’s newsworthy is what gets sifted through and picked up. Moreover, this can be done experimentally on a larger scale than…ah, interesting. Ok, so individual reporters wind up building a social media presence that is effectively a mini-newspaper and…oh dear.

One of the interesting phrases that came out of the discussion at the Dissection event was “self-commodification”–the tendency of journalists to need to brand themselves as products, artists, performers. Watching journalists on Twitter is striking partly because of how these constraints affect their behavior.

Putting it another way: what if newspapers had unlimited paper on which to print things? How would they decide to sort and distribute information? This is effectively what the Gawker, Buzzfeed, Techcrunch, and all the rest of the web press is up to. Hell, it’s what the Wall Street Journal is up to, as older more prestigious brands are pressured to compete. This causes the much lamented decline in the quality of journalism.

Ok, ok, so what does any of this mean? For society, for business. What is the equilibrium state?

How to tell the story about why stories don’t matter

I’m thinking of taking this seminar because I’m running into the problem it addresses: how do you pick a theoretical lens for academic writing?

This is related to a conversation I’ve found myself in repeatedly over the past weeks. A friend who studied Rhetoric insists that the narrative and framing of history is more important than the events and facts. A philosopher friend minimizes the historical impact of increased volumes of “raw footage”, because ultimately it’s the framing that will matter.

Yesterday I had the privilege of attending Techraking III, a conference put on by the Center for Investigative Reporting with the generous support and presence of Google. It was a conference about data journalism. The popular sentiment within the conference was that data doesn’t matter unless it’s told with a story, a framing.

I find this troubling because while I pay attention to this world and the way it frames itself, I also read the tech biz press carefully, and it tells a very different narrative. Data is worth billions of dollars. Even data exhaust, the data fumes that come from your information processing factory, can be recycled into valuable insights. Data is there to be mined for value. And if you are particularly genius at it, you can build an expert system that acts on the data without needing interpretation. You build an information processing machine that acts according to mechanical principles that approximate statistical laws, and these machines are powerful.

As social scientists realize they need to be data scientists, and journalists realize they need to be data journalists, there seems to be in practice a tacit admission of the data-driven counter-narrative. This tacit approval is contradicted by the explicit rhetoric that glorifies interpretation and narrative over data.

This is an interesting kind of contradiction, as it takes place as much in the psyche of the data scientist as anywhere else. It’s like the mouth doesn’t know what the hand is doing. This is entirely possible since our minds aren’t actually that coherent to start with. But it does make the process of collaboratively interacting with others in the data science field super complicated.

All this comes to a head when the data we are talking about isn’t something simple like sensor data about the weather but rather is something like text, which is both data and narrative simulatenously. We intuitively see the potential of treating narrative as something to be treated mechanically, statistically. We certainly see the effects of this in our daily lives. This is what the most powerful organizations in the world do all the time.

The irony is that the interpretivists, who are so quick to deny technological determinism, are the ones who are most vulnerable to being blindsided by “what technology wants.” Humanities departments are being slowly phased out, their funding cut. Why? Do they have an explanation for this? If interpetation/framing were as efficacious as they claim, they would be philosopher kings. So their sociopolitical situation contradicts their own rhetoric and ideology. Meanwhile, journalists who would like to believe that it’s the story that matters are, for the sake of job security, being corralled into classes to learn CSS, the programming language that determines, mechanically, the logic of formatting and presentation.

Sadly, neither mechanists nor interpretivists have much of an interest in engaging this contradiction. This is because interpretivists chase funding by reinforcing the narrative that they are critically important, and the work of mechanists speaks for itself in corporate accounting (an uninterpretive field) without explanation. So this contradiction falls mainly into the laps of those coordinating interaction between tribes. Managers who need to communicate between engineering and marketing. University administrators who have to juggle the interests of humanities and sciences. The leadership of investigative reporting non-profits who need to justify themselves to savvy foundations and who are removed enough from particular skillsets to be flexible.

Mechnanized information processing is becoming the new epistemic center. (Forgive me:) the Google supercomputer approximating statistics has replaced Kantian trancendental reason as the grounds for bourgious understanding of the world. This is threatening, of course, to the plurality of perspectives that do not themselves internalize the logic of machine learning. Where machine intelligence has succeeded, then, it has been by juggling this multitude of perspectives (and frames) through automated, data-driven processes. Machine intelligence is not comprehensible to lay interpretivism. Interestingly, lay interpetivism isn’t comprehensible yet to machine intelligence–natural language processing has not yet advanced so far. It treats our communications like we treat ants in an ant farm: a blooming buzzing confusion of arbitrary quanta, fascinatingly complex for its patterns that we cannot see. And when it makes mistakes–and it does often–we feel its effects as a structural force beyond our control. A change in the user interface of Facebook that suddenly exposes drunken college photos to employers and abusive ex-lovers.

What theoretical frame is adequate to tell this story, the story that’s determining the shape of knowledge today? For Lyotard, the postmodern condition is one in which metanarratives about the organization of knowledge collapse and leave only politics, power, and language games. The postmodern condition has gotten us into our present condition: industrial machine intelligence presiding over interpretivists battling in paralogical language games. When the interpretivists strike back, it looks like hipsters or Weird Twitter–paralogy as a subculture of resistance that can’t even acknowledge its own role as resistance for fear of recuperation.

We need a new metanarrative to get out of this mess. But what kind of theory could possibly satisfy all these constituents?