This book offers a new framework for understanding content creation and distribution across automated media platforms – a new mediatisation process. The book draws on three years of empirical and theoretical research to carefully identify and describe a number of unseen digital infrastructures that contribute to predictive media (algorithmic platforms) within the media production process: digital intermediation. The empirical field data is drawn from several international sites, including Los Angeles, San Francisco, Portland, London, Amsterdam, Munich, Berlin, Hamburg, Sydney and Cartagena. By highlighting the automated content production and distribution process, the book responds to a number of regulatory debates emerging around the societal impact of platformisation. Digital Intermediation: Towards transparent digital infrastructure describes and highlights the importance of key developments that help shape the production and distribution of content, including micro-platformization and digital first personalities. The book explains how digital agencies and multichannel networks use platforms strategically to increase exposure for the talent they manage, while providing inside access to the processes and requirements of developers who create algorithms for platforms. The findings in this book provide key recommendations for policy makers working within digital media platforms based on the everyday operation of content production and consumption within automated media environments. Finally, this book highlights user agency as a strategy for consumers who seek information on automated social media content distribution platforms.
As with all new publications, Routledge have provided a 20% discount for all purchases – please use code AFL03.
Also, a series of book launches are underway from August through to October in Australia, so looking forward to seeing those who can travel to the following locations:
9 August – News and Media Research Centre, University of Canberra
20 September – Digital Media and Research Centre, Queensland University of Technology
27 September – AI Governance and Trust in Digital Societies, University of Sydney
My day was split into two key sessions: a workshop in the morning that explored embedded industry research, and the second half of the day which was for HDR mentoring. I’m here to bring my research, meet people and think through potential collaborative research projects with colleagues. I’d like to thank all the wonderful people at the News and Media Research Centre for hosting me over the five days.
Embedded Industry Research
First off, I forgot how much I love travelling and talking with people in a face-to-face mode! I haven’t presented research anywhere in person for about two years, so I was very excited to talk with people in a room that didn’t rhyme with Zoom.
This first session was designed as a two and a half hour workshop for HDRs and beyond to explore the contexts and nuances of embedded research within industry. Drawing on my last ten years of embedded research at various industry partners from around the world, it was refreshing to re-visit how to do this sort of really important work. From how to approach industry with an offer, to co-designing research questions, and then how to integrate the appropriate methods, particularly in a post-lock down world, was refreshing for me.
What was more exciting was the discussion that emerged after the presentation. We had about an even split of colleagues who had done industry research (and this includes Linkage projects, consultancy work, commissioned research, and longer form research), and those that hadn’t. As we broke into smaller groups (not break out rooms), the conversation was focussed on the lived experience of researching with industry partners. It was excellent.
Some of the key topics that emerged included:
Often there are different languages and perspectives at play between academics and industry – intermediaries are always useful, to broker between the different stakeholders
We (academics) can become annoying? How do we ensure we remain relevant to the project from the industry perspective, too?
Often the experience was disappointing – a great word to use here, where some of the finding shave been ignored or not acted upon
There can be an anti-intellectual/academic culture – is it common with media organisations/journalists or more broadly than this?
Is there something about the authority of academics that might not gel with industry folk?
How could we know about their world/environment?
What is your character that you take in with you? I’m a journalist. I’m a content creator. ‘Interloper’ was used.
Suspicion seems to be the reaction from those being researched – why are they here?
‘It’s all about trust’
The complications of trust
Pandemic and the loss of hanging out with our industry folk
Reflexivity – all data is skewed, “situation of data gathering’
If you are interested, you can access the slides from the day here:
The slides for the Embedded Industry Research workshop
The second half of the day was spent listening to HDRs talk about their projects and trying to guide them where I could. I very much look forward to connecting many of these amazing people with some fo the amazing humans from MECO – there are many cross over points that can be strengthened with a more national network of HDRs.
https://i0.wp.com/jonathonhutchinson.com.au/wp-content/uploads/2022/05/pexels-photo-7996492.jpeg?fit=1625%2C1300&ssl=113001625Jonathon Hutchinsonhttps://jonathonhutchinson.com.au/wp-content/uploads/2019/01/cropped-JH_Logo_icon-150x150.jpgJonathon Hutchinson2022-05-10 17:30:202022-05-10 17:30:26Distinguished Research Fellowship – Day 2
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.
https://i0.wp.com/jonathonhutchinson.com.au/wp-content/uploads/2019/03/Jonathon_Hutchinson_Internet_Research.jpg?fit=1920%2C1080&ssl=110801920Jonathon Hutchinsonhttps://jonathonhutchinson.com.au/wp-content/uploads/2019/01/cropped-JH_Logo_icon-150x150.jpgJonathon Hutchinson2019-03-11 13:33:402019-03-11 13:33:482019 Internet Research Overview
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:
What is your gender?
What is your age?
How often do you use YouTube in a week?
What is your favourite category of YouTube video?
Are you likely to watch the next video that YouTube suggests for you?
Do you ever watch the trending videos?
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:
Views
Likes
Dislikes
Published On
Comment Numbers
Reaction to those comments
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:
Empathise – with your users
Define – your user’s needs, their problem, and your insights
Ideate – by challenging assumptions and creating ideas for innovative solutions
Prototype – to start creating solutions
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:
Name
Age, gender
Marital status
Occupation
Hobbies
Technology familiarity
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:
