Danke für die lustigen Zeiten, meine neuen Freunde!

You may notice I call it a sabbatical, even though technically it is called Special Studies Program (SSP) here at the University of Sydney. But, so a broader audience gets what’s going on here, let’s go with sabbatical for now…

Good times…

During my sabbatical, which I undertook during December 2018 to July 2019, I was embedded as a Visiting Research Fellow at the Hans Bredow Institute (now called the Leibnitz Institute for Media Research) in Hamburg Germany. I was working on the Algorithmed Public Sphere project alongside my two amazing colleagues Dr Cornelius Puschmann and Dr Felix Münch.

It was also a unique opportunity to meet a diverse group of like-minded researchers from around the world as we all converged on Hamburg to get going with some ground-breaking research. The powerhouse of researchers include Arjen van Dalen (Denmark), Christiano Ferri (Brazil), and Maris Männiste (Estonia).

The Algorithmed Public Sphere Fellows, 2019, L to R: Christiano Ferri, Jonathon Hutchinson, Cornelius Puschmann, Felix Münch, Maris Männiste (Arjen was missing that day).

Beyond having an amazing experience with these folk and learning about the bizarre similarities and differences of our countries, we shared insights into our research on automation, algorithms, media policy, and social cohesion. We also moved forward with some innovative digital methods, and have hatched a number of new research projects, including Bot visibility and authenticity: Automated social media conversation detection:

Bots are increasingly simple to produce and used as key communication protocols for individuals and institutions across social media platforms as one form of automated media production. Simultaneously, however, bot use is emerging as a relationship creator (Ford & Hutchinson, 2019) between consumers across platforms, skewing content visibility. Recent work by Münch et al. (forthcoming) identify bots within the German Twittersphere, resulting in a high probability of bots within Marketing and public relations (PR) conversations. Conversely, there is a low probability of bots communicating within the YouTuber Creator conversations across the same Twittersphere. This observation supports the argument that YouTubers may have a better strategy at visibility than bots, yet their content production is determined by their cultural, economic and political backgrounds. This project seeks to test bot-detection methods, for example the Botometer. It will design a ‘human’ baseline for bot detection within the German and Australian Twittersphere that can be compared against the automated bot-detection processes currently utilised. It will produce a bot-detection classifier that will be able to categorise accounts across a scale of malign, benign, or not likely automated.

Keynotes and Public Lectures

I also spent a small amount of time travelling to other European Universities to strengthen networks and develop future research projects. I was invited to deliver a Keynote Lecture to the Baltic Film, Media, Arts and Communication Institute of Tallin University in Estonia. My exceptional host was Dr Katrin Tiidenberg, who made me feel very much at home, but also exposed me to the life in the few countries within post-Soviet Union (like, I saw a real KGB interrogation room!!). Thanks to everyone who came along and asked engaging questions to help me continue to think through my new research area.

The view of Old Town in Estonia.

I also had the pleasure of visiting a number of other universities, to catch up with friends, colleagues and hatch new ideas and projects, including:

  • The University of Amsterdam, Netherlands;
  • London School of Economics, United Kingdom;
  • City University, London, United Kingdom;
  • Alexander von Humboldt Institute, Berlin, Germany.

The result:

  • Hutchinson J. (2019). Towards transparent public automated media: Digital intermediation. Keynote Lecture. University of Tallin, Estonia. 16 May.
  • Hutchinson J. (2019). Towards transparent public automated media: Digital intermediation. Keynote Lecture. Leibnitz Institute for Media and Communication, Hamburg, Germany. 15 May.

Methodology Masterclass

While I was in Tallin, I also delivered a masterclass on Data Ethnography with a group a diverse folk from Lecturers across the Arts, through to Masters Students from Information Technology. While the participants genuinely enjoyed the class, I think I always take more from this workshop as I keep developing the method. Thanks everyone for coming along the ride with me:

  • Hutchinson J. (2019). Data ethnography: How do we research what we can’t see? Postgraduate Masterclass. University of Tallin, Estonia. 17 May

Publications

I mean, this is what it all comes down to, right? Of which I am most delighted to have some time to finish those articles that were stuck on my hard drive, complete some new pieces, and then start work on the next few areas.

My time away during my SSP was spent for the most part writing and researching on my new emerging area of research, digital intermediation which I argue highlights the new media ecology that incorporates the agency of digital agencies, automation and algorithms.

I bought some books, they were heavy to carry home.

I was also able to have three articles published as a precursor to this work, and commenced work on new research and writing in this space. As I return to work, I have two articles under review and one book proposal in with the editors of Media Series for MIT Press.

Published Journal Articles:

  • Hutchinson J. (2019). Micro-platformization for digital activism on social media. Information, Communication & Society. DOI: 10.1080/1369118X.2019.1629612
  • Ford H & Hutchinson J. (2019). Newsbots That Mediate Journalist and Audience Relationships. Digital Journalism. DOI: 10.1080/21670811.2019.1626752
  • Hutchinson J. (2019). Digital first personality: Automation and influence within evolving media ecologies. Convergence. DOI: 10.1177/1354856519858921

Journal Articles Under Review:

  • Hutchinson J. (2019). Data ethnography for digital intermediation: How do we research what we can’t see? Big Data & Society.
  • Hutchinson J. (2019). Theorizing digital intermediation: Automating our media. Media, Culture & Society.

Book (almost with commissioning editors):

  • Hutchinson J. Revealing digital intermediation: Towards transparent infrastructure. Distribution Matters book series, MIT Press.

Grants

I was also awarded a few smaller grants to assist in developing my research towards an external grant application. At this stage, I have focussed on an ARC Discovery Grant to be submitted in March 2020 for funding in 2021. I am also looking at other funding opportunities such as the Australian Communications Consumer Action Network (ACCAN) Grants Program for funding in July 2020.

SLAM Research Support Scheme: $2966.44.

This grant is being used for the research project Bot visibility and authenticity: Automated social media conversation detection, which is underway with colleagues from the Leibnitz Institute for Media and Communication, Hamburg. This project seeks to understand the reliability of the Botometer project in comparison with the bot detection methods we have already developed, to understand how perceived real communication occurs around events within the Australian and German Twitterspheres. The immediate output is a paper for the 2020 ICA Conference, with a view to continue working on this project in the near several years.

Faculty Research Support Scheme: $5000.00

This grant is being used to bring several colleagues together on a project with a view to advance the research project to a competitive ARC Discovery Grant application. The project’s title is Promoting digital equality through better platform algorithmic policy, and brings expertise from Political Science, Computational Science, Design and Media Studies. While in Europe, I have received support for the project from Dr Jan Schmidt at the Leibnitz Institute for Media and Communication, Hamburg, and Associate Professor Thomas Poell from the University of Amsterdam – both leading academics in this field who are interested in becoming international advisors in the project.

Current Thinking…

I continue working on my book, which will create the field of digital intermediation. I describe the book in the following way:

Our media consumption is increasingly curated and designed by digital infrastructures that are informed by economic and infrastructural environments that determine the creation of content and how that content is distributed. Often, this is represented through algorithmically calculated decisions: recommendation systems on media applications and platforms. While this can be seen as a useful mechanism to sort, curate and present a digestible media diet within a saturated media market, automation is also an unseen digital infrastructure that contributes to the decrease in diversification of our exposure to information. Social media platforms increasingly promote what they see to be important content, which is often aligned with their commercial interests. Smart TVs are purchased with a bundle of pre-installed applications that are often unable to be uninstalled. Connected devices and interoperable systems are developed on information efficiency calculations with little concern for user and information equality. It is the commercial operators such as Netflix, Prime, YouTube and Apple who are succeeding in the content exposure battle, crowding out other key content creators, media organisations and cultural institutions. This is a digital distribution problem: the mismanagement of automated infrastructures.

This book constructs a theoretical model of digital intermediation within increasingly automated media systems. Digital intermediation can be applied to the process of digital media communication across the majority of social media platforms, which now drive the news and media cycle, highlighting the agency of users that becomes restricted and refined by the digital intermediaries that create, publish and distribute content. Through digital intermediation, it is also possible to understand the strategies of its most successful social media users, the platforms that privilege this content production process, and explain how some media is more visible than others. The book answers this question: How is media content produced nowadays, in what context(s), and within which structural pressures? Digital intermediation is a content production process that incorporates the culture and political economy that surrounds the technologies, online content producers, digital agencies and automation. The book describes these four unseen infrastructures of digital intermediation in detail by highlighting the production and distribution of content within our contemporary media ecology. The book then moves on to describe the cultural dimensions that surround how particular types of content is created as a means to represent our current societal understandings. The use of political economy is incorporated to then frame the regulation and economical practices that surround the production and consumption of content that is produced and distributed across digital spaces through the digital intermediation process. Finally, the book provides a series of recommendations that includes improved interface design that incorporates the dimensions of digital intermediation for content production and distribution to encourage the education and involvement of user agency within these media ecologies.

I’m super focussed, enjoying teaching again, and ready to develop my skills in the research service roles (HDR Coordinator). I am also managing the three International Executive Roles and learning so much from being on these Boards. Until the next three years!

Danke für die lustigen Zeiten, meine neuen Freunde!

That day I cooked for the Institute, with Philip, and it rocked!
Hans Bredow Cast

A few weeks ago, I sat down with Johanna Sebaurer, the producer of the Hans Bredow Bredowcast and social media expert of the Institute, to discuss some of my work while I have been a Visiting Research Fellow here. Just to remind everyone following along at home, I am undertaking research on the Algorithmed Public Sphere project here Hans Bredow Institute (which recently became the Leibniz Institute for Media Research) with a number of leading global researchers, where my key area of interest is in automated media, influencers and public service media.

Johanna pressed record on her DAT during our conversation and then produced our discussion into Episode 44 of the Bredowcast Podcast for the Institute! Apparently, this is the first podcast in English, so I am honoured to be the guest (although slightly sheepish my Deutsch is not strong enough at this point).

It was an amazing discussion that flowed really well, where we spoke about the things I have been doing here, my background and most importantly, how I have been working on a new methodology, data ethnography.

It’s always interesting to speak about developing work, and yes talking live is also very helpful, to spot holes in the work and think through areas that you have not given much time to previously. I found it especially helpful to explain the research process and the preliminary results to someone else, especially considering Johanna has not been that close to the development of this work.

The article that introduces Data Ethnography is almost complete and just about to be submitted to a journal, but until then you can get an idea of what the methodology is in this Bredowcast (along with some other fun stuff, too).

[advanced_iframe src=”https://podcast.hans-bredow-institut.de/2019/brc044-recommender-systems-igor-gabriela-and-their-youtube-journey/”]

Jonathon_Hutchinson_Transparent_Infrastructures

I have just completed a world-wind European tour, giving lectures at some of the best media institutes this side of the planet. Thanks to all the folk who made this possible, and took the time to promote my work. I’d like to reflect on that work and the discussions I’ve had with many great people within this post as I prepare this thinking for my next book – namely who should be facilitating and innovating transparent automated media systems? I argue public Service Media (PSM).

The thrust of this latest research was to problematise the concept of the ‘black-box’ as has been argued by so many scholars as something that we have no control over and are almost helpless to its control.

I think some of the most important work in this space was undertaken by Frank Pasquale and his Black Box Society book, which highlights the role algorithms play in society from a finance, legal and economic’s perspective. His argument of how algorithms control not only finance, but our digital lives, is a call for increased transparency and accountability on those who facilitate these technologies.

I also appreciate the work of many scholars who contribute and develop this arena of scholarship. Safiya Noble has done amazing work here and here book Algorithms of Oppression is a landmark piece of scholarship that brings to bear the real world implications of how algorithms are not only bias, but racist and oppressive.

Noble’s book leads well into Tania Bucher’s also groundbreaking book If…Then, that further develops the politics of algorithms and automated systems, to offer media academics a framework to help think through some of the implications of these socio-technical constructs.

I also find Christian Sandvig’s work incredibly inspiring here. While Sandvig’s work on algorithms and discrimination is super interesting, this particular piece on Auditing Algorithms sparked a particular interest in me on how to research algorithms.

But what I have found through most of this literature are two things, and this is perhaps where my ‘application brain’ is most curious. Firstly, most scholars tend to ignore user agency in these relationships, as if we are helplessly at the mercy of mathematical equations that are determining our society. Most (some) people are aware of the algorithm, and how to work alongside it these days, if our interface with platforms like Netflix, Spotify, YouTube etc. is anything to go by. Secondly, no-one talks of who should be responsible for facilitating a better system. Should we simply make more policy that tries to calm the overlord digital tech companies of now, or should we be thinking five to ten years ahead on how that technology can be used for society’s benefit (and not in a Chineses Social Credit System sense, either)?

So that is what I have been talking about in the last few weeks, and I think it is really important to include in the automated media conversation. I have been developing a digital intermediation framework that incorporates a number of these actors, and trying to understand how the intermediation process occurs. Check this out:

Digital intermediation actors, as part of the intermediation process

This is a first parse at what will become an important tool for a facilitating organisation who should be leading and innovating in this space: public service media.

Work has already commenced in this space, and we can draw on the thoughts of Bodó et al. (2018):

Public service media have charters that oblige them to educate, inform, and sustain social cohesion, and an ongoing challenge for public service media is interpreting their mission in the light of the contemporary societal and technological context. The performance metrics by which these organizations measure the success of their algorithmic recommendations will reflect these particular goals, namely profitability, loyalty, trust, or social cohesion.

Bodó, B., Helberger, N., Eskens, S., & Möller, J. (2019). Interested in Diversity. Digital Journalism, 7(2), 206–229.

So then, how does PSM do this? One way is to embed it in editorial policies to ensure PSM employees are operating as such. Another is to undertake PSM innovation remit and start teaching its users on how to work with algorithms effectively.

I don’t think ‘cracking open the black-box’ is all that useful to operationalise. They are often complex algorithmic formulas that require specialist expertise to design and interpret. But affording a control mechanism that enables users to ‘tweak’ how the algorithm performs may be not only possible, but crucial.

This is my focus for my last few weeks while I am working as a Research Fellow here in Hamburg.

hutchinson_predictive_media

The following passage is a thought moment, and by no means exhaustive of placing the idea within existing theories/fields. It would be interesting, and probably the published version of this will do so, to align it with media and cultural studies, queer theory or perhaps discrimination studies. That said, here is a thought process…

I have been undertaking substantial research into artificial intelligence (AI) and automation since arriving here at Hans Bredow. I am beginning to think that perhaps automation/AI isn’t the best or most appropriate way to frame our contemporary media lives. Those concepts certainly are a part of our media lives, but there may be a better way to describe the entire environment or ecosystem as I have previously written.

What I do understand at this point is that media curation/recommendation is responding to us as humans, but we are also responding to how that technology is responding and adapting to us. This is a human/technology relationship, and one that is constantly being refined, modified, adapted and changed – not by either agent alone, but collectively as any two agents would negotiate a relationship.

This type of framing, then, suggests we should no longer be thinking about algorithmic media, or automated media alone. Perhaps what we should be thinking about is the relationship of processed and calculated digital media with its consumers – for this I will use the term predictive media.

I will attempt to explain how I have arrived at predictive media.

Artificial Intelligence (AI) Media

Media certainly isn’t in an AI moment – I’m not entirely sure I align with AI to be honest (or at least I am still working through the science/concept and implications). Beyond its actual meaning, it feels as thought it is the new business catch phrase – “and put some AI in there with our big data and machine learning things”. If artificial intelligence is based on machine learning, the machine requires three phases of data to process: to interpret external data, to learn from those data, and to achieve a specific goal from those learnings. This implies that the machine has the capacity for cognitive processing, much like a human brain.

AI is completely reliant on data processing to produce a baseline, incorporate constant feedback data after the decisions have been made, and the recalculation of information to continue to improve its understanding of the data. Often, there is a human touch during many of these points placing a cloud of doubt over the entire machine learning capacity. While this iterative process is very impressive when done well, there will always be data points that are indistinguishable to a computer.

We should instead be thinking about these processes as a series of decision points, of which we also have input data.

Say for example, you are making a decision to board a bus to travel into town. AI would process data like distance, timetable, the number of people on the bus, for example and recommend which bus you should catch. What it can’t tell is if the bus driver is drunk and is driving erratically, or that the bus has advertising that you fundamentally disagree with, or that you have 10 students travelling with you. In that scenario, it will be the combination of AI processes along with your human decision making that prove to be the best interpretation of which will be the best bus to catch in to town.

As I see it, we are not in a pure Algorithmic Media moment – and this will be a long way away, if it manifests at all.

Algorithmic Media

We have also seen the rise of algorithmic media, which often presents itself as recommender systems or the like, which essentially suggests you should consume a particular type of media based on your past viewing habits or because of your demographic data.

Algorithmic media can be very useful, given our media saturated lives that have Netflix, Spotify, blogs, journalism, Medium, TikTok, and whatever else makes up our daily consumption habits. We need some help to sort, organise and curate our media lives to make the process possible (efficient).

Think of a Google search. It is often the case we search for specific information based on our needs. Google knows the sorts of information we are interested in and will attempt to return information that is relevant and useful. Of course this information result has a number of levers in operation behind the mechanics of results, for example commercial priorities, legislation, trends, etc., Further, we have also seen how algorithms can be incredibly racist, selective, indeed chauvinistic.

In some areas, developers have started addressing these areas, given the algorithms are developed by humans. But there is still a long way to go with this work.

So in that sense, I’m not algorithmic media makes a whole lot of sense due to the problems associated with it. It could be that by the time the algorithmic issues are entirely addressed, we will have moved on to our next media distribution and consumption phenomena.

Predictive Media

So if this is our background (and I understand I have raced through media and technology history, and critical studies here – I will flesh this out in an upcoming article), humans have altered their relationship with technology.

Heather Ford and I are about to (hopefully!!) have an article published that explores the human/technology relationship in detail through newsbots, but I think it is broader than bot conversations alone.

Indeed, content producers adapt and shift their relationship with algorithms daily to ensure their content remains visible. But I think consumers are now beginning to shift their relationship with how technology displays information. If not shift, we are definitely recognising these digital intermediary artefacts that impact, suspend, redirect, or omit our access to information.

Last week, Jessa Lingel published this cracking article on Culture Digitally, The gentrification of the internet. She likened our current internet to urbanisation, and made the argument that the process of gentrification is clearly in operation:

an economic and social process whereby private capital (real estate firms, developers) and individual homeowners and renters reinvest in fiscally neglected neighborhoods through housing rehabilitation, loft conversions, and the construction of new housing stock. Unlike urban renewal, gentrification is a gradual process, occurring one building or block at a time, slowly reconfiguring the neighborhood landscape of consumption and residence by displacing poor and working-class residents unable to afford to live in ‘revitalized’ neighborhoods with rising rents, property taxes, and new businesses catering to an upscale clientele

Perez, 2004, p.139

In her closing paragraphs, Jessa made a recommendation that is so obvious and excellent, why haven’t we done this before?

Be your own algorithm. Rather than passively accepting the networks and content that platforms feed us, we need to take more ownership over what our networks look like so that we can diversify that content that comes our way. 

Lingel, 2019, n.p.

It made me think about food and supermarkets – certainly in Sydney we have two (maybe three) major supermarkets. But there is a growing trend to avoid them and shop local, shop in food co-ops, join food co-ops, and change our food consumption habits entirely. If those major chains want to push inferior products and punish their suppliers to increase the bottom line, as consumers we (in the privileged Australian context) have the option to purchase our food elsewhere.

Why wouldn’t we do the same with our digital internet services? Is this a solution to bias, mismatched, commercially oriented media algorithms and the so-called AI?

Is Predictive Media the Solution?

I think we can apply the same approach towards predictive media.

We cannot consume the amount of media that is produced, suggesting we may be missing crucial information. We cannot trust automated media because it has proven to be incredibly bias. But perhaps it is in changing our relationship with technology and understanding how they work a little better, we might find a satisfactory medium.

It is not only greater transparency that is required to address our problems of automated and algorithmic media, but it is a proactive engagement with those machines to train the programs to understand us better. But changing that relationship is difficult if you don’t know that is an option. So perhaps the real call here is to establish alternative and transparent digital communication protocols that are easily accessible and decipherable for users. In education, change is possible, and this may be a defence against the current trajectory for digital media.

The combination of both increased understanding/transparency and more active engagement with training ‘our’ algorithms could be the basis for predictive media, where predictive media helps us beyond a commercial platform’s profit lines, and exposes us to more important and critical public affairs.

Original Image by Hello I’m Nik.

Jonathon_Hutchinson_Digital_First-Personality

EDIT: It is worth noting that News UK has teamed up with The Fifth to undertake exactly the point of this article. Read the Digiday article here.

In around 2017, Mike Williams and I had a few beers (can I say that?) in one of the studios at the ABC with a view to thinking through what was happening in the media at that time.

Instagram was ‘blowing up’, YouTube was going nuts, and a swag of micro platforms such as Vine, Musical.,ly, and others were fuelling the rise of branded content producers – otherwise known as solo content producers, otherwise known as influencers.

Both Mike and I had, and still do, have our favourite content producers, as I’m sure many readers do, and we often refer to their channels to see what they are doing, how they are reacting to certain global events, of what the latest trends might be.

But what we were interested in that night was understanding how this exploding creative industry was running alongside the existing media organisations, or was it all – where we both had a keen interest in how the ABC was shaping up in comparison.

One of the concepts we started throwing around was this idea that social media content producers now make their celebrity-ness online, build these massive audiences (or highly engaged audiences), and then often make the jump to traditional media. At the time, #7dayslater had just finished season 1 and I thought it was going to be a new production model the ABC would indeed pursue (but then, funding cuts).

What #7dayslater did represent however, was the praxis between online content producers and media organisations such as the ABC. And so was born the first concept of the Digital First Personality.

Of course this concept only raised more questions that night, like:

  • Why would a content producer become popular with their own style, and then switch over to somewhere like the ABC (with a remit for public service)?
  • How could they maintain their platform salary if they were to go off brand with their audience (suddenly start talking about the ABC as part of their suite of everyday-ness)
  • Should online content producers be trained by media organisations, and if so does that mean traditional celebrities should ‘learn’ social media?
  • Does the digital first personality become the new cultural intermediary?
  • Now that we have finished several beers, shall we go and have dumplings?

I’ve been thinking, researching and developing these questions for the last few years (beyond the call for dumplings), and have developed the concept of the digital first personality significantly. I first took it for a test drive with my MECO3602 Online Media students who bought into it and then also pulled the idea apart. I have presented the idea at a few conferences and have received some great feedback from colleagues along the way. Recently, I have resubmitted an article with major revisions to an A ranked journal, and am hopeful it will be published soon.

The last round of revisions with that journal really pushed me to think through some of the fundamental and theoretical concepts of the digital first personality. More broadly, I am beginning to draw connections between the digital first personality and microplatformization as part of the Digital Intermediation research project – how online content producers craft their skills as cultural intermediaries that are both experts at social influence and understanding platform automation, i.e. recommender systems. This is now starting to feed into the infrastructure work I am undertaking within the automated media space.

Here’s a basic introduction to how I am approaching the framework of the digital first personality:

Intermediation has traditionally been undertaken by a number of stakeholders including institutions, humans and non-human actors, to transfer information from one group of individuals to another. Recently, two new actors have emerged within the digital media ecology through cultural intermediation: social media influencers and automated media systems engaging algorithms. Cultural intermediation as a framework is a useful way to understand emerging social and cultural forms as a result of new media technologies. Cultural intermediation (Bourdieu, 1984) that describes how social capital can be exchanged between different stakeholder groups also incorporates market economics (Smith Maguire and Matthews, 2014) and expertise exchange. The latest iteration of cultural intermediation includes the agency of platforms, social media influencers and increasingly algorithms. Understanding this new form of cultural intermediation is crucial to enable items of public importance to remain visible.

Social influencers, which have previously been referred to as microcelebrities (Marwick, 2013; Senft 2013) and digital influencers (Abidin, 2016), are a particular subset of cultural intermediaries. Through their developed expertise to identify ‘cool’ boundary objects, they are able to engage in multiple media production practices to demonstrate the value of those objects to their large audiences. Examples of this practice include Zoella who often engages her audience with the products from her latest shopping haul (revealing the contents of one’s shopping bag), Evan’s Tube who engages his younger audience with an ‘unboxing’ of the latest Lego kit, or Fun for Louis who is often travelling to exotic locations to reveal its most appealing side. In each instance of these social influencers producing content, they engage in high levels of media literacy to transfer the value of the chosen product or service to their large fan base: a trustworthy, word of mouth news sharing technique. They will typically do this across a number of social media platforms, including their TikTok channel for the behind-the-scenes content, the Instagram platform for the ‘hype’ photo or Insta-Story, and a YouTube video to engage their largest audience.

The second emerging aspect of cultural intermediation is the algorithmic arena, which to a large extent describes how automation is undertaken across digital media platforms. As Gillespie (2014: 167) notes, algorithms “are encoded procedures for transforming input data into a desired output, based on specified calculations”. Within a media ecology that sees significantly more content produced than can be consumed, algorithms, in one sense, are seen as mechanisms to assist users in finding and consuming content that is relevant to their interests. In most cases, this manifests as a recommender system, which is represented as ‘Recommended for you’, ‘Up Next’ or ‘You will Like’ types of automated mechanisms. However, there is an increasing body of literature, which is described in detail below, that challenges the bias, power and relationships with content, society and culture that are represented by automated media systems.

Cultural intermediation that combines both social influencers and algorithms, then, acts as a process for media visibility across emerging networked platforms. What has become the process of blending private with public media (Meikle, 2016) has, as Turner (2010) highlights through the demotic turn, enabled ordinary folk to become key influential media producers. However, these key actors within cultural intermediation are typically engaging with the content production and distribution process for the social media entertainment (Cunningham and Craig, 2017) benefits such as increased social and economic capital. This cultural intermediation process is operationalised by what I argue is the digital first personality: those individuals that produce digital content for maximum visibility by engaging social influencer publication strategies that appease platform algorithms. In many cases, their media production focus is on commercial products and services to increase their social and economic capital. Within the social influencer genre that excludes fake news and disinformation, public issues, public affairs, news and current affairs, are often ignored in lieu of highly profitable alternatives.

So here is a beginning for a new area of research. I feel as though I have completed my fieldwork in digital agencies for now, but i can see a new space opening up that looks at the intersection of microplatformization and digital first personalities as the backbone of digital intermediation.

Original photo by Dean Rose on Unsplash

Jonathon_Hutchinson_Internet_Research

I’m lucky enough to be the Program Chair for the 2019 Association of Internet Researchers Conference, to be held in Brisbane in October. During the last week, I have engaged in the next task as Program Chair and gone through each individual submissions as I assign them to reviewers. This process involves reviewing the title, the abstract and then matching those papers to most suitable experts within the Association.

For those non-academic folk reading this, the conference process usually involves responding to a conference theme as designed by the conference and organisation committees, where potential delegates submit a proposal of anywhere between 500 and 1200 words addressing that theme. This proposal is then sent to a number of reviewers who conduct a blind review (blind meaning they do not know who the author(s) is/are), and then the paper is returned to the program chair with a review and overall score. The papers that receive a suitable score are invited to submit their paper to the conference, while the others are rejected.

We are just about to send the papers out to the reviewers after they have been assigned, which has provided me with some unique insights into the state of the field of internet research. Granted, the proposals are responding to the theme of Trust in the System, which will skew the submissions slightly, but typically academics will usually make their research align with any given conference theme as one’s field usually moves towards a common trajectory. The research that has been submitted can be read as a very strong overview and indicator of where the field is currently, and where it is heading.

Of course the items below are seen through my eyes, which is the first parse of the content coming through the submission portal – the final version of papers that will be accepted and presented will no doubt differ slightly from these initial observations.

What are the hot internet research topics?

As you would expect there is a growing number of research papers in the area of algorithms and platforms. The concept of automation and recommender systems has spread beyond Netflix and permeates in the areas of news and journalism, smart cities, politics, and healthcare.

Platform research continues to be incredibly important with work critically looking at YouTube, Instagram and Facebook as the most popular areas. It is interesting to see the rise of focus on emerging Chinese social media platforms – while I didn’t notice any on TikTok, there was a focus on WeChat and Weibo.

Other very popular areas of research interest include governance and regulation of internet and social media, news and journalism related to the internet, social media and politics, methodologies, labour and things/bots. There is also a group of researchers interested in Blockchain.

Who are internet researchers?

One of the core roles of the review assignment was aligning the papers that were submitted with relative experts in the field. To assist in this process, members of the Association nominate the topics and methodologies of which they are experts. This information provides a unique insight into how we see ourselves as internet researchers.

I have not crunched hard data on this, and would not publish any sensitive data from the Association, so this is a broad observation of my aggregated insights. That is, these are the methods fields that kept popping up when I was assigning papers to reviewers.

One of the most popular internet researcher categories that was available from the pool was ethnographers for social media – participant observation across social media practices. I directly fit into this category and needless to say much of the work undertaken by these researchers could easily align with my own research endeavours.

An emerging category that aligns with the growing field is social media algorithm analysts. As humanities and social scientists become increasingly involved in data science alongside media and communication, the rise of algorithmic analysis has become not only popular, but essential to understand our field.

News and journalism experts are often coupled with social media experts, and the other interesting (and popular) couplings included discourse analysis with social media, and social media and textual analysis/content analysis.

There is a significant gap however, in those researching identities and activism – from what I can see across most of the communication infrastructure formats. A number of researchers are presenting work in this area, yet we still don’t see ourselves as a large cohort of experts in identity research – which seems odd. Perhaps this is just how the methodological categories appear in the conference system, or perhaps this is true of how we (don’t) identify as researchers?

So what does all this mean?

Well, these insights certainly won’t change the field’s direction but it does offer some insights into the gaps of internet research. I think we have platform research covered, while social media and ethnography is very strong. Social media and politics also has a very strong presence.

But there are areas that lack representation in internet research, that would be useful for researchers to pick up on in the next 12 months.

These include:

  • Ethics – in both use of internet and how to research the internet;
  • Algorithm analysis – the growing field here requires more people to apply data science to their existing work on platforms, social media etc.;
  • Geography and geolocation – I didn’t notice any human geographers (I might have missed this) conducting work in internet research in this sample. There is a small group of researchers undertaking geolocation specific work, but there is room for more;
  • Internet histories;
  • Labour;
  • Public sphere;
  • Surveillance;
  • Apps;
  • Conflict; and
  • Commerce.

For me, a light bulb just went on with how to personally align my research after attending conferences. I guess I always thought of conferences as a chance to present my current work alongside the field. But after having undertaken this Program Chair role, I find it is better to also analyse the gaps in the field to position your work for the next 12 months.

Perhaps scholars have always worked like this and I am just catching up with the game, but having these insights has been incredibly useful to shape my thinking. Hopefully they are useful to others in some capacity.

Original photo by 85Fifteen on Unsplash.

Jonathon_Hutchinson_South_Korea

We have just returned from a week of interviews in Seoul, South Korea and Tokyo, Japan as part of our Australian Research Council funded Discovery Project, Media Pluralism and Online News. In this post I will focus on the South Korean case only, as we still require more work to understand the Japanese arena completely. During our time in South Korea, we interviewed key stakeholders from Daum, The Korea Herald, Yonhap news Agency, and the Korea Press Foundation.

The South Korean news media industry is unlike any other in the world, especially in terms of how the Koreans access their news. Unlike other parts of the world that typically use Facebook, Twitter and increasingly messaging apps (Kalogeropoulos, 2018), South Korea has the News Portals Naver and Daum. The statistics are around 70% of Koreans access news via Naver, 20% via Daum and the rest from directly accessing the news websites or messaging apps (Korea Press Foundation, 2018). This makes the market voice of Naver incredibly loud in the news media. But it is the news ecosystem in its entirety that is also of interest to understand how South Korean access their news online.

Who are the Key Players in South Korean News?

The media industry in South Korea is governed by the Broadcasting Act (2008), the telecom and ISP industry is dominated by KT, SK and LG, and a number of television networks, newspapers and outlets. Within the online news sector, there is also the News Assessment Council and the News Portals. While there is much work already done on the laws and incumbent stakeholders, there is little understanding on the news portals and the News Assessment Council – an area we focus on.

The role of the News Assessment Council includes allocating a Board of members from the news industry, media experts and appoints its own staff members. Sometimes they work as a proxy regulator for the news portals. Twice a year they accept applications from news sources to become part of the news portals, where portals will sponsor the Council to remain in operation. Essentially, the News Assessment Council acts as a self regulating body for the online news sector.

As news portals, Daum and Naver will pay a number of news providers to submit their news articles. News providers are required to accept the conditions of the portals to be published in that space. As the access data suggests, South Koreans consume most of their news via the portals (most significantly Naver) and the news organisation’s partnership with the news portals is crucial for those organisations to survive. The portal partnership enables the news organisation’s content to be searched on the portal and receives better visibility through search engines. If the news organisation level of partnership is high enough, the portals will pay increased money to the news provider (news fees). While the subscription money is not that much, the real money comes from search, which then leads to larger traffic.

What are the news portals?

Jonathon_Hutchinson_Naver
The homepage of the Naver portal

Both Naver and Daum are more than just news portals: they are a place where most Koreans undertake activities such as search, messaging, and they also include cash payment systems. They are an online destination for many users, making them an attractive space to also publish online news.

Users are presented with a series of categories on the news site including Breaking News, Society, Environment, and Lifestyle. The front page displays a selection of the top news articles and users are invited to either directly click on those articles or select from their categories of interest.

In talking with our interviewee at Daum, we established the following:

At Daum the breaking news priority is determined by their pre-determined categories on the main page. This is now based on how users access their information – this data is gleaned and based on browser behaviour and not a logged-in state (they say for user privacy). So algorithms, huh?

Users are given a random number but then the number can be reset, to avoid the privacy issues. There is no priority on sectors/genres, it is based on audience, and based on customer choice. The introduction of the algorithm is not to be political, it is to increase customer satisfaction.

Users can comment, share and vote up/down on each of those articles to determine where information will appear on the website.

News Aggregation

In talking with many interviewees, it became obvious that Yonhap News is the most consumed news service (the highest percentage at around 25% of all news consumed).

There might be a few reasons for this including the news agency is a 24/7 and can provide up-to-the minute journalism. Users also trust Yonhap more than other news agencies, increasing their consumption rate. Further, Yonhap are not subject to the constraints that stop other outlets publishing news simultaneously across news portals AND their own broadcast outlets.

So on the surface, it would appear that the self-regulatory body, the News Assessment Council, determine who can publish on the portals. Yonhap is the most consumed media source across those portals, and there is little to no intervention into community management of those conversations. Users determine, through popularity, where content will be displayed on the portals. This model was questioned by a number of stakeholders across the online news industry.

Media Diversity?

We will continue to analyse the preliminary data findings from this field work over the coming months to determine to what level there is media diversity. Other factors that need to be included in the analysis beyond the media environment are user behaviour, the impact of the portal algorithms, user experience, and the age of the news consumers (apparently news manipulation is over blown because young people don’t read the comments and hardly access journalism).

One interesting item to really think through is the arrival of YouTube and Instagram as a key news source for people. Anecdotally, YouTube is an easier interface for older people to access information, and users trust information if it is sent to them via Instagram. The role of other platforms is certainly changing the diversity of the media landscape in South Korea.

No doubt we will publish an article or book chapter from the findings and you can continue to follow the Media Pluralism blog for updates on this research.

And of course if you have any first-hand experiences with South Korean News, or have insights you can offer, please leave a comment or question below.

Original photo by Shawn Ang on Unsplash

Data_Ethnography

I’m cooking up some ideas while I’m away on Sabbatical at the Hans Bredow Institute. My core focus at this stage is the ‘how to’ research automation and algorithms. My current approach is integrating retro engineering through ethnography and design thinking. At this stage, I’m calling it Data Ethnography and below sets out a guideline for what I think that should be.

No doubt this is the skeleton for a journal article, but what is here is the early developing of a new method I am currently working on.

If you think this methodology could be useful, or you have any feedback or suggestions, please leave them below in the comments.

Why Data Ethnography?

Humanities and social science digital research methods have been interrupted due to the prominence of privacy and surveillance concerns of platform interoperability that produces large quantities of personified data. The Facebook Cambridge Analytica scandal, especially the revelation of its ability to construct predictive models of its user’s behaviors, brought to the public interest concerns over how platform user data is harvested, shared and manipulated by third party providers. The global pushback against the platform provider’s use of these data resulted in platforms closing down some access to application programming interfaces (APIs) to inhibit data manipulation. However, these restrictions also impact on how public benefit research is conducted, providing a useful prompt to rethink how humanities, social scientists and human computer interaction scholars research the digital.

While the datafication of our digital lives has provided us with new insights, the digital methods that enable us to research our digital selves have always been mixed to understand the field of enquiry, along with its surrounding political, cultural and economic constructs.Increased digital practices built on sophisticated calculations, for example the use of algorithmic recommendations, connected devices, internet of things, and the like, have impacted on our research environments, prompting the question, how do we research what we can’t see?This article provides evidence from investigating the visual cultures that surround YouTube that a new methodology is required to research the apparent ‘black boxes’ that operate alongside our digital selves through data ethnography. Data ethnography is the combination of stakeholder consultation, top level data analysis, persona construction, fine data analysis and finally topic or genre analysis. Data ethnography enables not only what we cannot see, but provides a useful way to understand government interoperability mandates and inform appropriate policy development.

Overview of Data Ethnography

The Five-Stage Process of Data Ethnography

Consultation

This methodology emerged from asking the question, what does the Australian YouTube visual culture look like? Building on the long-term participant observation that is synonymous with ethnography, a researcher is able to understand the norms, cultural affordances, communication practices, and so on. The researcher is required to both produce and consume videos on the platform to understand how users will create content to suit the platform constraints. Simultaneously, viewing the content provides insights into how viewing publics are constructed, how they communicate, what is considered important, norms and languages. In the context of YouTube, this included the platform, but also the intermediaries such as digital agencies, multichannel networks and other digital intermediaries such as influencers to highlight publication strategies. The combination of this ethnographic data provides a compelling starting point for the additional methods that emerge.

The video content was analysed using discourse analysis reflective of Jakobson (1960) to understand the video language function as referential, poetic, emotive, conative, phatic, and/or metalingual. As such the discourse in one of four ways: contact enunciation – looking into the camera & addressing the audience; emotive enunciation which is the expressive or affective relating to the style of the YouTuber; genre including thematic content, style and compositional structure; enunciative contract which is the reading contract (Véron, 1985) between the enunciator (addressor) and enunciatee (addressee). The discourse analysis enabled the vast amounts of YouTubers to be categorised into a smaller, more manageable group of users.

Building on the discourse analysis, I asked the users of the platform the following questions:

  1. What is your gender?
  2. What is your age?
  3. How often do you use YouTube in a week?
  4. What is your favourite category of YouTube video?
  5. Are you likely to watch the next video that YouTube suggests for you?
  6. Do you ever watch the trending videos?
  7. When you enter this address into your web browser, what is the URL of the “up next” video that it suggests for you: https://youtu.be/4_HssY_Y9Qs

The results of these several questions then guided the following snowballing process of the additional methods.

Top Level Data Analysis

Before undertaking comprehensive data scraping processes that rely on platform data availability, it is useful to observe how various incidental metrics are available. In the case of YouTube, this related to likes, comments, views, and the like that provide insights into what people are watching, how they engage with the content, and how they talk about the content. These top level metric data observations enable the researcher to direct the research or focus on areas of interest that are not otherwise obvious through the consultation phase of data ethnography. The top level metrics further support the user practices on how content is produced, published, shared, and consumed amongst a wide variety of users. Finally, the top level data analysis enables the researcher to ask questions such as what data are available, which processes might be automated, and how might these data be repurposed for other sorts of measurements.

For YouTube, the top level data analysis translated to the following areas of interest:

  1. Views
  2. Likes
  3. Dislikes
  4. Published On
  5. Comment Numbers
  6. Reaction to those comments
  7. Comments on comments

On the YouTube platform, these are the metrics that are available to the non-social science data scraping process. Researchers with no data programming skills are able to extract these data.

Persona Construction

Persona construction is a research approach that is based in human-computer interaction (HCI), user-centred design (UCD) and user-experience (UX). Emerging from the Design Thinking field which is human-centred to solve problems, persona construction is useful to understand how problems can be addressed between human and machine interaction. “Design Thinking is an iterative process in which knowledge is constantly being questioned and acquired so it can help us redefine a problem in an attempt to identify alternative strategies and solutions that might not be instantly apparent with our initial level of understanding” (Interaction Design, n.p.). It can have between 3 and seven stages, but these stages are not sequential or hierarchical, but rather iterative and the process typically does not abide to the dominant or common approaches of problem solving methods.

There are 5 phases in Design Thinking:

  1. Empathise – with your users
  2. Define – your user’s needs, their problem, and your insights
  3. Ideate – by challenging assumptions and creating ideas for innovative solutions
  4. Prototype – to start creating solutions
  5. Test – solutions

Persona Construction in Design Thinking is in the second phase of the process, which enables the researcher to define user needs and problems alongside one’s insights. There are four types of personas: Goal-directed, Role-based, Engaging, and Fictional personas. The data ethnography methodology uses Fictional Personas which “The personas in the fiction-based perspective are often used to explore design and generate discussion and insights in the field” (Nielsen, 2013, p.16). In this environment, a persona “is represented through a fictional individual, who in turn represents a group of real consumers with similar characteristics” (Miaskiewicz & Kozar, 2011, p. 419). Secondly, and similarly to ethnography, a persona is described in narrative form. This narrative has two goals: (1) to make the persona seem like a real person, and (2) to provide a vivid story concerning the needs of the persona in the context of the product being designed.

In the context of YouTube research, the key criteria for the fictional personas were:

  1. Name
  2. Age, gender
  3. Marital status
  4. Occupation
  5. Hobbies
  6. Technology familiarity
  7. Devices used

To ensure the accuracy of the process, the research was conducted behind the university wall which has a large range of IP addresses. The research was conducted using Firefox under a new username for each persona, the researcher was not in a signed in state for Google or YouTube, a new Google account was created for each persona and the location of user was set by suggesting a phone area code as per their country. Their interests (Hobbies) became the search terms and the algorithmically generated results were recorded in a pre-trained and post-trained state.

Fine Grained Data Scrape

By engaging the persona construction method which reveals insights into how an algorithm will treat its users, or within the context of this research the sorts of results it will recommend, it is then possible to engage in a fine-grained data scrape. A fine grained data scrape is defined as ….[ref]. In this research, it become possible to understand which were the top related videos, which channels were the most viewed, and sorts of networks that emerge around those videos. This process is most useful for not only identifying specific nodes or videos, but also clusters which can be translated into thematic areas, issue publics (Burgess and Matamoros-Fernández, 2016), and audience clusters. I have previously written about the specific social network analysis (SNA) method so I will not go into that detail here, but in order to find these thematic clusters there is a process of data extraction, cleaning and processing which can be followed. SNA is defined as a computational practice “which draws on computational technologies to help identify, aggregate, visualise and interpret people’s social networking and social media data traces” (p.1). In the first instance, I engaged the YouTube Network Analysis Tool (Ref) to extract the network data of related videos to those which returned as popular in the persona construction method – a post trained algorithm state. This digital method tool extracts the data as a Gephi file which can then be manipulated to provide a social network analysis (SNA) across the dataset.

Topic Modelling

The final method to understand how users congregate around popular content on YouTube, and how they communicate about the material, was to engage in topic modelling.

Topic Modelling is the final method which attempts to understand how users talk about certain things in particular ways. Specifically, I was trying to understand how certain topics emerged in relationship to other topics, which can be understood through the Latent Dirichlet Allocation topic modelling approach. Smith and Graham note, “Informally, LDA represents a set of text documents in terms of a mixture of topics that generate words with particular probabilities” through a predetermined number of topics. This provides the researchers with a “heuristic approach that aim[s] to maximise the interpretability and usefulness of the topics”.

For example, if we wanted to find out what are the popular topics that are discussed by a 14 year old Australian boy, we would construct the persona with interests, which in turn become search terms of, bike riding, Lego, Playstation, and Phil and Dan. The top YouTube Channel recommendations for this user before the algorithm training were:

  1. Family Guy
  2. Talk Shows
  3. Trailers
  4. Gordon Ramsey
  5. Joe Rogan
Jonathon_Hutchinson_Digital_Intermediation

Social media audiences consume approximately three percent of the entire amount of content published across platforms (Bärtl, 2018). Of this three percent, a small number of popular digital influencers create that content, for example Casey Neistat, Logan Paul, or Zoella that, arguably, leads to media homogenisation through the limited focus of popular themes and topics. Moreover, platform providers, such as YouTube and Instagram, operate on algorithmic recommender systems such as ‘trending’ and ‘up next’ mechanisms to ensure popular content remains highly visible. While platforms in the digital era exercise a social and political influence, they are largely free from the social, political and cultural constraints applied by regulators on the mass media. Beyond vague community guidelines, there remains very little media policy to ensure that the content produced by digital influencers and amplified by platforms is accurate, diverse to include public interest, or are indeed beneficial. 

This project will research the content production process of automated media systems that engage digital influencers, or leading social media users, who interact with extraordinarily large and commercially oriented audiences. The evidence base will assist in developing theory on contemporary digital media and society, which will consequently shape how communities access public information. Instead of harnessing this knowledge for commercial imperatives, this research project will examine the findings in the context of socially aware digital influencers who occupy similar roles to those found in traditional media organisations. Further, this project will examine how algorithms are making decisions for media consumers based on commercial executions, which are often void of the social awareness associated with public affairs and issues.  

At a time when mass media comes under scrutiny for its involvement in perpetuating misinformation around public issues, accurate media becomes increasingly crucial to the provision of educative material, journalistic independence, media pluralism, and universal access for citizens. At present, media organisations are attempting to repurpose traditional broadcast content on new media platforms, including social media, through automation built on somewhat experimental algorithms. In many cases, these organisations are failing in this new environment, with many automated media attempts appearing more as ‘experimental’. This should be an opportunity for media organisations to rethink how they produce content, and how new informed publics might be brought into being around that content. 

Instead of thinking of automation as a solution to their increasing media environmental pressures, media organisations should be looking toward algorithms to curate and publish informative media for its audiences. This moment provides a unique opportunity to research the contemporary social media environment as media organisations experiment with automated media processes. It also challenges our understanding of automated media through popular vanity metrics such as likes and shares, in what Cunningham and Craig (2017) are calling ‘social media entertainment’. Under this moniker, these scholars highlight the intersection point of social media platforms, content production, and entrepreneurial influencers who commercialise their presence to develop their own self-branded existence. Abidin (2016) refers to these users as digital influencers, to include YouTube and Instagram superstars who demonstrate an unprecedented capacity to manifest new commercially oriented publics. Digital influencers are typically young social media users who commercially create content across a host of social media platforms, which is liked, commented on and shared by millions of fans. It is estimated the top ten 18-24 year old YouTubers are worth $104.3 million collectively (Leather, 2016), indicating a burgeoning new media market. This model of exercising digital influence within automated media systems has potential to translate into the support of an informed public sphere amid a chorus of social media communication noise.  

The research is innovative in a number of ways. Firstly, it is groundbreaking through its approach of collecting and comparing datasets of contemporary social media practice from within the commercial and non-commercial media sectors. Secondly, it theoretically combines media studies, science and technology studies, sociology and internet studies to bolster the emerging field of contemporary life online: an interdisciplinary approach to everyday social media. Thirdly, methodologically it combines traditional qualitative methods such as interviews and focus groups, and blends these with contemporary digital ethnography techniques and emerging social network analysis. Fourth, this research contributes to the emerging field of automation and algorithmic culture, by providing a groundbreaking exploration of data science with traditional audience research: a field of particular importance for media organisations. Finally, the outcomes will provide innovative insights for digital agencies and leading media organisations. 

Aims and Outcomes 

The aims of the project are:  

  1. to understand how digital influencers operate across social media, in both commercial and non-commercial media environments;  
  2. to document how digital media agencies enable digital influencers to create large consumer based publics; 
  3. to examine and understand how algorithms are operating within large-scale media content production; 
  4. to identify how global media is incorporating digital influencer roles and automation (if at all) into their production methodologies; and 
  5. to provide a new theoretical framework, recommendations and a policy tool that enables media organisations to manifest and engage with its audiences on critical public issues.  

The aims will be met by engaging in digital ethnography methods that documents how digital influencers produce content and actively engage with their audiences in an online community. These users are responsible for creating discussion around a number of issues they deem to be important, yet are typically driven by commercial imperatives. These conversations inspired through influencer content production is then compounded by the digital agencies who operate as amplifying agents for those messages, by especially ‘gaming’ the exposure mechanisms of YouTube and Instagram. However, this research will seek to prove that if this model can work in the commercial media environment, can socially aware digital influencers adopt the same techniques. 

The primary research question is:  

  1. how do digital influencers operate to create large consumer based publics?  

The research subquestions are: 

  1. how does automation operate in media content production and distribution? 
  2. how do automated media systems make content distribution decisions based on behavioural assumptions? 
  3. how can media organisations incorporate the successful methods of automation and digital influencers in their publishing practice? 

Background 

Digital influencers are social media users, typically ‘vloggers’ or video bloggers, who create content about products or lifestyles on popular themes including toys, makeup, travel, food and health amongst other subject areas. Increasingly, digital influencers are using a number of social media platforms to build their brand and publish content to their niche and considerably large audiences. This process of content production and distribution is emblematic of digital intermediation through social media platforms that afford individuals to operate in a media ecology, while determined through algorithmic processes. As Gillespie (2014, p.167) notes, algorithms “provide a means to know what there is to know and how to know it, to participate in social and political discourse, and to familiarize ourselves with the publics in which we participate”. At the heart of these algorithmic platforms distributing trending and popular content are the digital influencers who are creating popular, entertaining media and represent the flow of traffic and information between increasingly large audiences. 

Media organisations have been experimenting with both digital influencers and automation to create and distribute its branded content. In many cases, commercial media have employed the services of digital influencers to boost their traditionally produced media content, while deploying, in many ways, crude experiments in automation. Media brands consistently send digital influencers products and services to integrate into their ‘lifestyle’ videos and images. Recommender systems (Striphas, 2015), such as those used for distribution platforms such as Netflix have proved most popular, where content is suggested based on an audience member’s past viewing habits. Recommendation systems have been adopted across a number of media services including Spotify, Apple iTunes, and most news and media websites. The integration of chatbots is also rising, where the most interesting experiment has emerged from the public media sector through the ABC News Chatbot. Ford and Hutchinson (forthcoming) note that the ABC News Chatbot is not only an experiment in automated media systems, but also a process of educating media consumers on how to access crucial information from within a cornucopia of media. 

The key theoretical problem demonstrated in these examples is an asymmetric distribution of agency when automated systems make ‘decisions’ that can be based on flawed normative or behavioural assumptions (Vedder 1999). At worst, there is no possibility to override the automated decision. That is why algorithmic recommendations are sensitive matters and should be explained to users (Tintarev & Masthoff 2015). But explaining and understanding recommendation systems requires deep technical knowledge as the results are produced by a series of complex and often counter-intuitive calculations (Koren et al 2009). Furthermore, recommendations are often the result of more than one algorithm applied in the online and offline processing of consumer behaviour data (Amatriain & Basilico 2015). The asymmetrical relationship this creates between users and media content providers is especially problematic due to the public complexion and social responsibility obligations that should be demonstrated by media organisations. 

Digital influencers as cultural intermediaries are tastemakers that operate across traditional media platforms such as television and radio, and have become more effective at their translation ability across social media platforms such as Instagram, Twitter and Vine for example. Digital intermediation is the next phase of this research, which builds on cultural intermediation, yet focuses on its relationship with automated media systems. 

Original by Ari He on Unsplash

Jonathon_Hutchinson_New_Blog

After a hiatus in publishing, I now have a brand new research blog!

For those of you who remember, I had my old blog at jonathonhutchinson.com. But after accidentally letting that domain name expire, some internet soul purchased that space immediately and then wanted to sell it back to me for money that it just wasn’t worth (maybe). So unfortunately, I lost about ten years worth of public research.

Some of that work including my early steps in blogging in about 2009, some foundational work with Adrian Miles on Quicktime multi video work and hypertextual media (now called vlogging), all of my Honours Research, all of my PhD research, and some of my post PhD work.

An expensive lesson, but one that I have now justified.

For a while, I thought I might switch to a YouTube Research Channel instead of a research blog, but the work in putting out research vlogs is huge (big shout outs to Zoe Glatt for her ongoing work on that format).

So…

This blog is a space is where I will publish research updates, methodology insights, conference undertakings, and some teaching and learning activity.

Most importantly, I am currently working on the research of the project, Digital Intermediation: A study of automated media influencers. Rising from the ashes of a failed DECRA application, I am now on a Research Fellowship at the Hans Bredow Institute in Hamburg Germany. I will work on my research as part of the Algorithmed Public Sphere, alongside Cornelius Puschmann and Jan Schmidt. I will recored all of the work from that research here.

Of course, if you have questions or would like to get in contact, please drop me a line.

Looking forward to many conversations in this space!

Feature photo by Danielle MacInnes on Unsplash.