Category Archives: Academic Work

Scraping Twitter using Outwit Hub

Students in my graduate unit Philosophies of Communication Technologies and Change (part of our Graduate Certificate in Social Media and Public Engagement) are producing simple lists of tweets.

Some students are using Outwit Hub to generate these lists as this is what I have used since 2012. I have created a guide “Scraping Twitter using Outwit Hub worksheet” for my students but others may also find it useful.

Scraping the results from a Twitter ‘advanced search’ allows you create an archive of tweets without the limitations of the API. It is only useful for relatively small sets that have less than 3,200 tweets per day as you can query Twitter for all tweets for a given hashtag per day.

The lists of tweets shall be used for the purpose of carrying out sophisticated analyses of the ‘circulation of discourse':

Writing to a public helps to make a world, insofar as the object of address is brought into being partly by postulating and characterizing it. This performative ability depends, however, on that object’s being not entirely fictitious–not postulated merely, but recognized as a real path for the circulation of discourse. That path is then treated as a social entity. (Warner 2002: 64)

The character of this discourse will depend on the stakeholder publics they (or their organisations) wish to engage with and so on.

 

Economy of Culture

Boris Groys’ On the New would’ve productively informed my essay on the how the media event of True Detective could be understood as part of the revaluation of cultural values.  We are reading it as part of our aesthetics reading group. Groys wants to present an understanding of innovation and by ‘innovation’ he does not mean the Silicon Valley destructive innovation sense. Innovative theories or innovative art are not described and justified on the basis of signification to reality or truth but whether they are culturally valuable. He is drawing on Nietzsche’s conception of the revaluation of value. Page 12 of On the New:

The economy of culture is, accordingly, not a description of culture as a representation of certain extra-cultural economic constraints. Rather, it is an attempt to grasp the logic of cultural development itself as an economic logic of the revaluation of values.

I am enjoying Groys’ non-market ‘economic’ interpretation of Nietzschean truth.  He develops an economic  conception of Nietzsche’s non-moral version of value without turning to Marxist conceptions of value that would position cultural value as a consequence of the social relation between capital and labour power.

In my True Detective essay I develop a notion of ‘meta’ so as to grapple with the epistemological displacement that occurs in the midst of a revaluation of values. I call this a ‘liminal epistemology’, which has been commodified as ‘discovery’ in contemporary ‘apps’ that assist users access various kinds of cultural texts (music, written texts, phatic/social media texts, etc). The media event of True Detective (as compared to the televisual text) is interesting as it dramatises the ‘detective work’ of this liminal epistemology itself. From the introduction of my True Detective essay:

If nothing else, True Detective clearly triggers meta-detective work by the audience. The show, its inter-textual references, and non-diegetic exegetical explanations of these references produced new edges of surprise and a new sense of expectation. For example, there is a folding of the crime fiction genre into existentialist horror and a topological transformation wrought upon both. Both genres frame a passage of discovery by the characters and audience. “Discovery” has become a buzzword in user-centred design to describe the design of platforms that assist users discover appropriate content, and this refers to the way users willingly embrace the delegated agency of “smart” interfaces. The liminal epistemology of discovery in meta-stable media assemblages pose answers to questions that haven’t yet been asked. The question isn’t simply asked of the characters of the show, but of the entire event itself as it repeated different elements of genres in different ways; in effect, the audience carries out meta-detective work.

The reason why I am excited about Groys’ work is that he has already isolated a similar problematic with regards to the revaluation of values. His focus so far is not animated by the same concerns as I am, but there is a similar problematic. I make it very clear that what I found the most interesting about the True Detective media event is that it is part of a broader constellation of cultural texts that are all, in different ways, working through this revaluation of values. From the introduction of my essay:

In the final section I develop meta in terms of what Sianne Ngai (2012) calls a minor aesthetic category, and in this case what characterises meta as a minor aesthetic category is the way any text, object or event that dramatises the suspension of cultural values. In Simondon’s terms, meta is an aesthetic category that refers to works that in some way repotentialise values that serve as the “preindividual norms” of value in a state of meta-stability ready to be potentialised in a multiplicity of ways (Combes 2013: 64). As I shall explore in detail, True Detective dramatises a conflict between systems of belief and cultural value through the figures of the two main characters, Rust and Marty. In this way, “meta” signals a threshold of value (or what Nietzsche (1968) calls “transvaluation”) more often associated with nihilism.

I look forward to reading the rest of On the New.

Still Forgetting OOO

I am presenting a workshop on assemblages today primarily for the PhD students in one of our research centres. I have set two readings, one of which is Ian Buchanan’s chapter “The ‘Clutter’ Assemblage” (here is another version of the essay) in The Schizoanalysis of Art.

A brief passage in the essay reminded me of my Forget OOO post from almost 5 years ago encouraging graduate students to not get caught up in the internet hype of OOO. The 2006 post was triggered by Levi Bryant’s reading of ‘desiring machines’ in terms of OOO’s ‘objects’. Buchanan’s chapter addresses the use of schizoanalysis to understand how desire is productive in the context of artistic work. The passage extracted below explains better than I did why reading ‘desiring machines’ in terms of ‘objects’ as a move to some how escape from Kantianism is profoundly ill-advised. (Of course, there is another dimension to the below that Buchanan does not emphasise, which I indicate in my Forget OOO post pertaining to the ‘machinic’ or what I think is best described as the ‘milieu of singularities’):

Desiring-production is the process and means the psyche deploys in producing connections and links between thoughts, feelings, ideas, sensa- tions, memories and so on that we call desiring-machines (assemblages). It only becomes visible to us in and through the machines it forms. While both these terms were abandoned by Deleuze and Guattari in subsequent writing on schizoanalysis, the thinking behind them remains germane throughout. This is by no means straightforward because Deleuze and Guattari cast their discussion of desiring-production in language drawn from Marx, which has the effect of making it seem as though they are talking about the production of physical things, which simply is not and cannot be the case. The truth of this can be seen by asking the very simple question: if desire produces, then what does it produce?

The answer isn’t physical things. The correct answer is ‘objects’ – but ‘objects’ in the form of intuitions, to use Kant’s term for the mind’s initial attempts to grasp the world (both internal and external to the psyche). That is what desire produces, objects, not physical things. Kant, Deleuze and Guattari argue, was one of the first to conceive of desire as production, but he botched things by failing to recognize that the object produced by desire is fully real. Deleuze and Guattari reject the idea that superstitions, hallucinations and fantasies belong to the alternate realm of ‘psychic reality’ as Kant would have it (Deleuze and Guattari 1983: 25). The schizophrenic has no awareness that the reality they are experiencing is not reality itself. They may be aware that they do not share the same reality as everyone else, but they see this as a failing in others rather than a flaw in themselves. If they see their long dead mother in the room with them they do not question whether this is possible or not; they aren’t troubled by any such doubts. That is the essential difference between a delusion and a halluci- nation. What delusionals see is what is, quite literally. If this Kantian turn by Deleuze and Guattari seems surprising, it is never- theless confirmed by their critique of Lacan, who in their view makes essentially the same mistake as Kant in that he conceives desire as lacking a real object (for which fantasy acts as both compensation and substitute). Deleuze and Guattari describe Lacan’s work as ‘complex’, which seems to be their code word for useful but flawed (they say the same thing about Badiou). On the one hand, they credit him with discovering desiring-machines in the form of the objet petit a, but on the other hand they accuse him of smothering them under the weight of the Big O (Deleuze and Guattari 1983: 310). As Zizek is fond of saying, in the Lacanian universe fantasy supports reality. This is because reality, as Lacan conceives it, is fundamentally deficient; it perpetually lacks a real object. If desire is conceived this way, as a support for reality, then, they argue, ‘its very nature as a real entity depends upon an “essence of lack” that produces the fantasized object. Desire thus conceived of as production, though merely the production of fantasies, has been explained perfectly by psychoanalysis’ (Deleuze and Guattari 1983: 25). But that is not how desire works. If it was, it would mean that all desire does is produce imaginary doubles of reality, creating dreamed-of objects to complement real objects. This subordinates desire to the objects it supposedly lacks, or needs, thus reducing it to an essentially secondary role. This is precisely what Deleuze was arguing against when he said that the task of philosophy is to overturn Platonism. Nothing is changed by correlating desire with need as psychoanalysis tends to do. ‘Desire is not bolstered by needs, but rather the contrary; needs are derived from desire: they are counterproducts within the real that desire produces. Lack is a countereffect of desire; it is deposited, distributed, vacuolized within a real that is natural and social’ (Deleuze and Guattari 1983: 27).

#thedress for journalism educators

Black and Blue? Gold and White? What does #thedress mean for journalism educators?

The Dress Buzzfeed
Original Buzzfeed post has now had 38 million views.

At the time of writing, the original Buzzfeed post has just under over 38m visitors and 3.4m people have voted in poll at the bottom of the post. Slate created a landing page, aggregating all their posts including a live blog. Cosmo copied Buzzfeed. Time produced a quick post that included a cool little audio slideshowWired published a story on the science of why people see the wrong colours (white and gold). How can we use this in our teaching?

Nearly every single student in my big Introduction to Journalism lecture knew what I was talking about when I mentioned #thedress. I used it as a simple example to illustrate some core concepts for operating in a multi-platform or convergent news-based media  environment.

Multi-Platform Media Event

Journalists used to be trained to develop professional expertise in one platform. Until very recently this included radio, television or print and there was a period from the early to mid-2000s when ‘online’ existed as a fourth category. Now ‘digital’-modes of communication are shaping almost all others. We’ve moved from a ‘platform only’ approach to a ‘platform first’ approach — so that TV journalists also produces text or audio, writers produce visuals, an so on — and what is called a ‘multi-platform’ (or ‘digital first’, ‘convergent’ or ‘platform free’) approach.

When with think ‘multi-platform’, we think about how the elements of a story will be delivered across media channels or platforms:

  • Live – presentations
  • Social – Facebook, Twitter, Youtube, etc.
  • Web – own publishing platform, podcast, video, etc.
  • Mobile – specific app or a mobile-optimised website
  • Television – broadcast, narrowcast stream, etc.
  • Radio – broadcast, digital, etc.
  • Print – ‘publication’

‘Platform’ is the word we use to describe the social and technological relation between a producer and a consumer of a certain piece of media content in the act of transmission or access. In a pre-digital world, transmission or delivery were distinct from what was transmitted.

Thinking in terms of platforms also incorporates how we ‘operate’ or ‘engage’ with content via an ‘interface’ and so on. Most Australians get their daily news from the evening broadcast television news bulletin. Recent figures indicate that most people aged 18-24 actually get their news about politics and elections from online and SNS sources, compared to broadcast TV.

#thedress is a multi-platform media event. It began on Tumblr and then quickly spread via the Buzzfeed post to Twitter and across various websites belonging to news-based media enterprises.  It only makes sense if the viral, mediated character of the event is taken into account.  #thedress media event did not simply propagate, it spread at different rates and at different ways. The amplification effect of celebrities meant #thedress propagated across networks that are different orders of magnitude in scale. Viral is a mode of distribution, but it also produces relations of visibility/exposure.

New News and Old News Conventions

Consumers of news on any platform expect the conventions of established news journalism. What are the conventions of established news journalism?

  • The inverted pyramid
  • The lead/angle
  • Sourcing/attribution
  • Grammar: Active Voice, Tense
  • Punctuation
  • Sentence structure
  • Word use
  • Fairness

When we look at #thedress multi-platform media event we see different media outlets covered the story in different ways. Time magazine wrote the most conventional lead out of any that I have seen; the media event is the story:

Everyone on the Internet Wants to Know What Color This Dress Is
The Internet took a weird turn Thursday when all of a sudden everyone started buzzing about the color of a dress. A woman had taken to Tumblr the day before to ask a seemingly normal question: what color is this dress?

Cosmopolitan largely mediated between the two, both framing the story as an investigation into colour, but also reporting on the virality of the multi-platform media event:

Help Solve the Internet’s Most Baffling Mystery: What Colors Are This Dress?
Blue and black? Or white and gold?
If you think you know what colors are in this dress, you are probably wrong. If you think you’re right, someone on the Internet is about to vehemently disagree with you, because no one can seem to agree on what colors these are.

I’ve only include the head, intro and first par for Time and Cosmo and you can see already they are far more verbose compared to Buzzfeed’s original post. The original Buzzfeed post rearticulated a Tumblr post, but with one important variation:

What Colors Are This Dress?
There’s a lot of debate on Tumblr about this right now, and we need to settle it.
This is important because I think I’m going insane.
Tumblr user swiked uploaded this image.
[Image]
There’s a lot of debate about the color of the dress.
[Examples]
So let’s settle this: what colors are this dress?
68% White and Gold
32% Blue and Black

The Buzzfeed post added an ‘action': the poll at the bottom of the post. Why is this important?

Buzzfeed, Tumblr and the Relative Value of a Page View

Buzzfeed COO Jon Steinberg addressed the question of the Buzzfeed business model by posting a link to this article back in 2010:

Some of its sponsored “story unit” ad units have clickthrough rates as high as 4% to 5%, with an average around 1.5% to 2%, BuzzFeed President Jon Steinberg says. (That’s better than the roughly 1% clickthrough rate Steinberg says he thought was good for search ads when he worked at Google.) BuzzFeed’s smaller, thumbnail ad units have clickthrough rates around 0.25%.

The main difference now is the importance of mobile. In a 2013 post to LinkedIN Steinberg wrote:

At BuzzFeed our mobile traffic has grown from 20% of monthly unique visitors to 40% in under a year. I see no reason why this won’t go to 70% or even 80% in couple years.

Importantly, Buzzfeed’s business model is still organised around displaying what used to be called ‘custom content’ and what is now commonly referred to as ‘native advertising’ or even ‘content marketing’ when it is a longer piece (like these Westpac sponsored posts at Junkee).

Buzzfeed
Image via Jon Steinberg, LinkedIN

On the other hand, Tumblr is a visual platform; users are encouraged to post, favourite and reblog all kinds of content, but mostly images. For example, .gif-based pop-culture subcultures thrive on tumblr and tumblr icons are those that perform gestures that are easily turned into gifs (Taylor Swift) or static images (#thedress).The new owners of Tumblr, Yahoo, are struggling to commercialise Tumblr’s booming popularity.

I had a discussion with the Matt Liddy and Rosanna Ryan on Twitter this morning about the relative value of the 73 million views of the original Tumblr post versus the value of the 38 million views of the Buzzfeed post. Trying to make sense of what is of value in all this is tricky. At first glance the 73 million views of the original Tumblr post trumps the almost 38 million views of the Buzzfeed post, but how has Tumblr commercialised the relationship between users of the site and content? There is no clear commercialised relationship.

Buzzfeed’s business model is premised on a high click-through rate for their ‘native advertising’. Of key importance in all this is the often overlooked poll at the bottom of the Buzzfeed post. Almost 38 million or even 73 million views pales in comparison to the 3.4 million votes in the poll. Around 8.6% of the millions of people who visited the Buzzfeed article performed an action when they got there. This may not seem as impressive an action as those 483.2 thousand Tumblr uses that reblogged #thedress post, but the difference is that Buzzfeed has a business model that has commercialised performing an action (click-through), while Tumblr has not.

Nieman Lab 2015 Predictions for Journalism

Last week I delivered the first lecture in our Introduction to Journalism unit. I am building on the material that my colleague, Caroline Fisher, developed in 2014. One of the things about teaching journalism is that every example has to be ‘up to date’. One of the things that Caroline discussed in the 2014 lecture were the predictions for 2014 as presented by the Nieman Lab.

The Nieman Lab is a kind of journalism think tank, clearing house and site of experimentation. At the end of each year they ask professionals and journalism experts to suggest what they think is going to happen in journalism the next year.

Incorporating these predictions into a lecture is a good way to indicate to students what some professionals and experts think are going to be the big trends, changes and events in journalism for that year. (The anticipatory logic of predictions about near-future events has become a genre of journalism/media content that I briefly discuss in a forthcoming journal article. See what I did there.)

To analyse the the 65 predictions for 2015 in a lecture that only goes for an hour would be almost impossible. What I did instead was to carry out a little exercise in data journalism to introduce students to the practical concepts of ‘analytics’, ‘website scraping’, and the capacity to ‘tell a story through data’.

Nieman Lab
Nieman Lab 2015 Predictions

I created a spreadsheet using Outwit Hub Pro that scraped the author’s name, the title of the piece, the brief one or two line intro and the number of Twitter and Facebook shares. I wanted to know how many times each prediction had been shared on social media. This could then serve as a possible indicator of whether readers though the prediction was worth sharing through at least one or two of their social media networks. By combining the number of shares I could then have a very approximate way to measure which predictions readers of the site had the most value.

Spreadsheet shares
Here is the spreadsheet created through Outwit Hub Pro,

I have uploaded the table of the Nieman Lab Journalism Predictions 2015 to Google Drive. The table has some very quick and simple coding of each of the predictions so as to capture some sense of what area of journalism the prediction is discussing.

The graph resulting from this table indicates that there were four predictions that were shared more than twice the number of times compared to the other 61 predictions. The top three stories had almost three times the number of shares.

combined social shares
The four predictions with the highest number of shares clearly standout from the rest.

Here are the four stories with the total number of combined shares:

  1. Diversity: Don’t talk about it, be about it                              1652
  2. The beginning of the end of Facebook’s traffic engine 1617
  3. The year we get creeped out by algorithms                        1529
  4. A wave of P.R. data                                                                             1339

I was able to then present these four links to my students and suggest that it was worth investigating why these four predictions were shared so many more times than the other 61 predictions.

In the most shared prediction, Aaron Edwards forgoes the tech-based predictions that largely shape the other pieces and instead argues that media organizations need to take diversity seriously:

I guess I could pivot here to talk about the future of news in 2015 being about mobile and personalization. (I would geek out about both immensely.) I suppose I could opine on how the reinvention of the article structure to better accommodate complex stories like Ferguson will be on every smart media manager’s mind, just as it should have been in 2014, 2013, and 2003.
But let’s have a different kind of real talk, shall we?
My prediction for the future of news in 2015 is less of a prediction and more of a call of necessity. Next year, if organizations don’t start taking diversity of race, gender, background, and thought in newsrooms seriously, our industry once again will further alienate entire populations of people that aren’t white. And this time, the damage will be worse than ever.

It was a different kind of prediction compared to the others on offer. Most people who work in the news-based media industry have been tasked with demonstrating a permanent process of professional innovation. Edwards piece strips back the tech-based rhetoric and gets at the heart of what media organizations need to be doing so as to properly address all audiences.  “The excuse that it’s ‘too hard’ to find good journalists of diverse backgrounds is complete crap.”

The second most shared piece, on the limitations of over-relying on Facebook as a driver of traffic, fits perfectly with the kind of near-future prediction that we have come to expect. Gnomic industry forecasting flips the causal model with which we are  familiar — we are driven by ‘history’ and it is the ‘past’ (past traumas, past successes, etc) that define our current character — so that it draws on the future as a kind of tech-mediated collective subconscious. Rather than being haunted by the past, we are haunted by possible futures of technological and organisational change.

My favourite piece among all the predictions is Zeynep Tufekci who suggests that things are going to get weird when our devices start to operate as if animated by a human intelligence. She suggests that “algorithmic judgment is the uncanny valley of computing“:

Algorithms are increasingly being deployed to make decisions where there is no right answer, only a judgment call. Google says it’s showing us the most relevant results, and Facebook aims to show us what’s most important. But what’s relevant? What’s important? Unlike other forms of automation or algorithms where there’s a definable right answer, we’re seeing the birth of a new era, the era of judging machines: machines that calculate not just how to quickly sort a database, or perform a mathematical calculation, but to decide what is “best,” “relevant,” “appropriate,” or “harmful.”