The first point made in the key findings of the report is about how young people receive news from family and friends, including teachers (from the infographic). Trust is extremely high.
My problem with the reporting in the Conversation is focuses on ‘fake news’. ‘Fake news’ has tabloid ‘outrage’ news value among an educated audience, but it is not actually that interesting from a research perspective.
After being part of three Digital News Reports (2015, 2016 and 2017) the key critical question for me is, how do children and young people develop news literacy and their own sources of news as they mature? If they are accessing news via their family and friends, does this mean this is how they also develop news literacy? By imitating the critical relationships based on cultural values and social norms of their parents? In our research low levels of trust in mainstream news have been interpreted as relatively high levels of critical news literacy. How does this work in the context of young people developing their own news literacy if they have extremely high levels of trust in their primary sources of news?
Critical News Literacies?
What is the relationship between perceptions of bias (key finding 3) and the capacity to spot ‘fake news’ (key finding 4)? Arguably ‘fake news’ is irrelevant compared to the ideological framing of most of the mainstream news. The key development of 8-12 to 13-16 year olds seems to be the radical reduction in the percentage share of those responding to the survey who don’t know about various measures of bias (Figures 18-20). That is, there is roughly half the number of young people who responded ‘I don’t know’ to questions 13-16 year olds compared to 8-12 year olds. Rightly or wrongly having a view on the bias of news representations demonstrates critical or discerning engagement and this increases.
I scraped Breitbart’s all posts from Facebook page. This is a representation of all ‘engagement’ (likes, comments and shares) for each month. The first six months of 2015 saw tremendous growth in engagement and it would be worth exploring what actually happened in that period, so I did a search of the Nexis service for ‘Breitbart’ across January – June 2015 to see if mainstream news services mentioned the site. Nexis is not comprehensive but it does track most major news publications and services. I did not include ‘press releases’ or ‘newswires’. Plus I collated all the articles that mentioned ‘Breitbart’ without any data cleaning so likely multiple entries for same article published in slightly different ways.
The table at the bottom of this post lists the publications with the most mentions of ‘Breitbart’. A few comments about this list. I had to search for ‘US Official News’ as I had not heard of it before. It is LexisNexis’s own news aggregation service. I think I can assume that only subscribers to LexisNexis can access this so it is not important for getting a sense of this period. MailOnline is next and as a click chasing operation it clearly went after ‘outrage’. There are multiple entries for WaPo blogs in the list so I think posts are being counted more than once. Interesting to see the Canberra Times down the bottom.
Reading the three pieces mentioned in these articles requires a subtle attuned to the concerns of Breitbart. The review celebrates the movie and what is understood to be general sentiment behind it. It also couches the movie as a kind of repudiation (I think?) of ‘Big Hollywood’. ‘Big Hollywood’ is a meta-tag on the site and therefore can be understood to be one of the major concerns. I think it refers to the conservative belief that the ‘cultural left’ rules Hollywood and that there is a kind of conspiracy to de-valuing ‘right wing’ culture. The other pieces are similar and even more explicitly framed in terms of broader concerns. The second WaPo blog piece is about ‘mainstream media’ reporting on ‘hoaxes’ as if they were true. The third piece interprets a tweet by Seth Rogen in such a way as to suggest that the movie is akin to Nazi propaganda. These are also tagged Big Hollywood. In this context then ‘Big Hollywood’ is not only about the movie industry but popular culture more broadly.
Writer Laura Bennet points out the positive social and political shifts of the rise of first person journalism. That there is “more of a market for underrepresented viewpoints than ever”. They seem to dramatize at the level of genre the relationship between the personal and the political. These are fantastic developments in the contemporary character of mass and niche media. Bennet also indicates the strong negatives:
The “first-person economy […] incentivizes knee-jerk, ideally topical self-exposure, the hot take’s more intimate sibling.”
Works of first person journalism “seem to be professional dead ends, journalistically speaking […] [r]ather than feats of self-branding”.
Pitches all end up sounding like they “were all written in the same voice: ‘immature, sort of boastful.'”
They’re predominately popular in a highly gendered part of the market: “many of the outlets that are most hungry for quick freelancer copy, and have the lowest barriers to entry for publication, are still women’s interest sites”. This is of course not ‘bad’. The implication is that first person journalism is a genre that has a very limited market.
But these do not explain why first person journalism has emerged as one of the popular genres of content online. Bennet draws a connection to the personal disclosure mode of Web 1.0’s practices of blogging. That might be true of very early examples of first person journalism online (2005-2009) but seems less true for subsequent generations of writers who simply bypassed the ‘blogging’ era of the internet.
Although they may be using the rhetorical forms of early blog-based first person journalism, the discursive function of the genre I suggest has more in common with celebrity discourse. As David Marshall argues, “celebrities have become the discursive talking points for the political dimensions of a host of formerly private and personal concern” (2009: 27). For example, an analysis of the representation of Slovenian political celebrities taking part in weekly interviews published in mass-market women’s magazine Jana, Luthar (2010) describes a process of personalisation which “involves the construction and representation of famous people and celebrities as individualized human types as the major component of popular discourse” (2010: 696). Luthar is concerned with the discursive articulation of a national Slovenian identity through personal identity characteristics, primarily gender. But we can see how first person journalism is a more general personalisation of what media and communications scholars call ‘public discourse’.
Celebrity discourse is one way to personalise public discourse and the genre of first person journalism is another. (To get more technical, the personalisation of public discourse around social issues through traumatic experience is one way to anchor audiences to affectively resonant ‘issue publics’ and produce click-based audiences as a commodity in the post-broadcast attention economy.) It in part explains why young writers think they are promoting themselves as ‘writers’ when they write and seek publication for works of first person journalism. They think that if their story allows them to become the center of an issue-based public organised around their experience, then this reflects well on their aspirations for being journalists or media personalities. In effect they become minor issue-based celebrities because of their experience. Instead, I’d emphasise Bennet’s point about the way the ‘click economy’ consumes such aspirants is very useful advice.
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
Grammar: Active Voice, Tense
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:
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.
There’s a lot of debate about the color of the dress.
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
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%.
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).
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.
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.
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’.
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.
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.
Here are the four stories with the total number of combined shares:
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.
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.”