Association of Internet Researchers AoIR 2016: Day Two

Today I am again at the Association of Internet Researchers AoIR 2016 Conference in Berlin. Yesterday we had workshops, today the conference kicks off properly. Follow the tweets at: #aoir2016.

As usual this is a liveblog so all comments and corrections are very much welcomed. 

Platform Studies: The Rules of Engagement (Chair: Jean Burgess, QUT)

How affordances arise through relations between platforms, their different types of users, and what they do to the technology – Taina Bucher (University of Copenhagen) and Anne Helmond (University of Amsterdam)

Taina: Hearts on Twitter: In 2015 Twitter moved from stars to hearts, changing the affordances of the platform. They stated that they wanted to make the platform more accessible to new users, but that impacted on existing users.

Today we are going to talk about conceptualising affordances. In it’s original meaning an affordance is conceived of as a relational property (Gibson). For Norman perceived affordances were more the concern – thinking about how objects can exhibit or constrain particular actions. Affordances are not just the visual clues or possibilities, but can be felt. Gaver talks about these technology affordances. There are also social affordances – talked about my many – mainly about how poor technological affordances have impact on societies. It is mainly about impact of technology and how it can contain and constrain sociality. And finally we have communicative affordances (Hutchby), how technological affordances impact on communities and communications of practices.

So, what about platform changes? If we think about design affordances, we can see that there are different ways to understand this. The official reason for the design was given as about the audience, affording sociality of community and practices.

Affordances continues to play an important role in media and social media research. They tend to be conceptualised as either high-level or low-level affordances, with ontological and epistemological differences:

  • High: affordance in the relation – actions enabled or constrained
  • Low: affordance in the technical features of the user interface – reference to Gibson but they vary in where and when affordances are seen, and what features are supposed to enable or constrain.

Anne: We want to now turn to platform-sensitive approach, expanding the notion of the user –> different types of platform users, end-users, developers, researchers and advertisers – there is a real diversity of users and user needs and experiences here (see Gillespie on platforms. So, in the case of Twitter there are many users and many agendas – and multiple interfaces. Platforms are dynamic environments – and that differentiates social media platforms from Gibson’s environmental platforms. Computational systems driving media platforms are different, social media platforms adjust interfaces to their users through personalisation, A/B testing, algorithmically organised (e.g. Twitter recommending people to follow based on interests and actions).

In order to take a relational view of affordances, and do that justice, we also need to understand what users afford to the platforms – as they contribute, create content, provide data that enables to use and development and income (through advertisers) for the platform. Returning to Twitter… The platform affords different things for different people

Taking medium-specificity of platforms into account we can revisit earlier conceptions of affordance and critically analyse how they may be employed or translated to platform environments. Platform users are diverse and multiple, and relationships are multidirectional, with users contributing back to the platform. And those different users have different agendas around affordances – and in our Twitter case study, for instance, that includes developers and advertisers, users who are interested in affordances to measure user engagement.

How the social media APIs that scholars so often use for research are—for commercial reasons—skewed positively toward ‘connection’ and thus make it difficult to understand practices of ‘disconnection’ – Nicolas John (Hebrew University of Israel) and Asaf Nissenbaum (Hebrew University of Israel)

Consider this… On Facebook…If you add someone as a friend they are notified. If you unfriend them, they do not. If you post something you see it in your feed, if you delete it it is not broadcast. They have a page called World of Friends – they don’t have one called World of Enemies. And Facebook does not take kindly to app creators who seek to surface unfriending and removal of content. And Facebook is, like other social media platforms, therefore significantly biased towards positive friending and sharing actions. And that has implications for norms and for our research in these spaces.

One of our key questions here is what can’t we know about

Agnotology is defined as the study of ignorance. Robert Proctor talks about this in three terms: native state – childhood for instance; strategic ploy – e.g. the tobacco industry on health for years; lost realm – the knowledge that we cease to hold, that we loose.

I won’t go into detail on critiques of APIs for social science research, but as an overview the main critiques are:

  1. APIs are restrictive – they can cost money, we are limited to a percentage of the whole – Burgess and Bruns 2015; Bucher 2013; Bruns 2013; Driscoll and Walker
  2. APIs are opaque
  3. APIs can change with little notice (and do)
  4. Omitted data – Baym 2013 – now our point is that these platforms collect this data but do not share it.
  5. Bias to present – boyd and Crawford 2012

Asaf: Our methodology was to look at some of the most popular social media spaces and their APIs. We were were looking at connectivity in these spaces – liking, sharing, etc. And we also looked for the opposite traits – unliking, deletion, etc. We found that social media had very little data, if any, on “negative” traits – and we’ll look at this across three areas: other people and their content; me and my content; commercial users and their crowds.

Other people and their content – APIs tend to supply basic connectivity – friends/following, grouping, likes. Almost no historical content – except Facebook which shares when a user has liked a page. Current state only – disconnections are not accounted for. There is a reason to not know this data – privacy concerns perhaps – but that doesn’t explain my not being able to find this sort of information about my own profile.

Me and my content – negative traits and actions are hidden even from ourselves. Success is measured – likes and sharin, of you or by you. Decline is not – disconnections are lost connections… except on Twitter where you can see analytics of followers – but no names there, and not in the API. So we are losing who we once were but are not anymore. Social network sites do not see fit to share information over time… Lacking disconnection data is an idealogical and commercial issue.

Commercial users and their crowds – these users can see much more of their histories, and the negative actions online. They have a different regime of access in many cases, with the ups and downs revealed – though you may need to pay for access. Negative feedback receives special attention. Facebook offers the most detailed information on usage – including blocking and unliking information. Customers know more than users, or Pages vs. Groups.

Nicholas: So, implications. From what Asaf has shared shows the risk for API-based research… Where researchers’ work may be shaped by the affordances of the API being used. Any attempt to capture negative actions – unlikes, choices to leave or unfriend. If we can’t use APIs to measure social media phenomena, we have to use other means. So, unfriending is understood through surveys – time consuming and problematic. And that can put you off exploring these spaces – it limits research. The advertiser-friends user experience distorts the space – it’s like the stock market only reporting the rises except for a few super wealthy users who get the full picture.

A biography of Twitter (a story told through the intertwined stories of its key features and the social norms that give them meaning, drawing on archival material and oral history interviews with users) – Jean Burgess (Queensland University of Technology) and Nancy Baym (Microsoft Research)

I want to start by talking about what I mean by platforms, and what I mean by biographies. Here platforms are these social media platforms that afford particular possibilities, they enable and shape society – we heard about the platformisation of society last night – but their governance, affordances, are shaped by their own economic existance. They are shaping and mediating socio-cultural experience and we need to better to understand the values and socio-cultural concerns of the platforms. By platform studies we mean treating social media platforms as spaces to study in their own rights: as institutions, as mediating forces in the environment.

So, why “biography” here? First we argue that whilst biographical forms tend to be reserved for individuals (occasionally companies and race horses), they are about putting the subject in context of relationships, place in time, and that the context shapes the subject. Biographies are always partial though – based on unreliable interviews and information, they quickly go out of date, and just as we cannot get inside the heads of those who are subjects of biographies, we cannot get inside many of the companies at the heart of social media platforms. But (after Richard Rogers) understanding changes helps us to understand the platform.

So, in our forthcoming book, Twitter: A Biography (NYU 2017), we will look at competing and converging desires around e.g the @, RT, #. Twitter’s key feature set are key characters in it’s biography. Each has been a rich site of competing cultures and norms. We drew extensively on the Internet Archives, bloggers, and interviews with a range of users of the platform.

Nancy: When we interviewed people we downloaded their archive with them and talked through their behaviour and how it had changed – and many of those features and changes emerged from that. What came out strongly is that noone knows what Twitter is for – not just amongst users but also amongst the creators – you see that today with Jack Dorsey and Anne Richards. The heart of this issue is about whether Twitter is about sociality and fun, or is it a very important site for sharing important news and events. Users try to negotiate why they need this space, what is it for… They start squabling saying “Twitter, you are doing it wrong!”… Changes come with backlash and response, changed decisions from Twitter… But that is also accompanied by the media coverage of Twitter, but also the third party platforms build on Twitter.

So the “@” is at the heart of Twitter for sociality and Twitter for information distribution. It was imported from other spaces – IRC most obviously – as with other features. One of the earliest things Twitter incorporated was the @ and the links back.. You have things like originally you could see everyone’s @ replies and that led to feed clutter – although some liked seeing unexpected messages like this. So, Twitter made a change so you could choose. And then they changed again to automatically not see replies from those you don’t follow. So people worked around that with “.@” – which created conflict between the needs of the users, the ways they make it usable, and the way the platform wants to make the space less confusing to new users.

The “RT” gave credit to people for their words, and preserved integrity of words. At first this wasn’t there and so you had huge variance – the RT, the manually spelled out retweet, the hat tip (HT). Technical changes were made, then you saw the number of retweets emerging as a measure of success and changing cultures and practices.

The “#” is hugely disputed – it emerged through hashtag.org: you couldn’t follow them in Twitter at first but they incorporated it to fend off third party tools. They are beloved by techies, and hated by user experience designers. And they are useful but they are also easily coopted by trolls – as we’ve seen on our own hashtag.

Insights into the actual uses to which audience data analytics are put by content creators in the new screen ecology (and the limitations of these analytics) – Stuart Cunningham (QUT) and David Craig (USC Annenberg School for Communication and Journalism)

The algorithmic culture is well understood as a part of our culture. There are around 150 items on Tarleton Gillespie and Nick Seaver’s recent reading list and the literature is growing rapidly. We want to bring back a bounded sense of agency in the context of online creatives.

What do I mean by “online creatives”? Well we are looking at social media entertainment – a “new screen ecology” (Cunningham and Silver 2013; 2015) shaped by new online creatives who are professionalising and monetising on platforms like YouTube, as opposed to professional spaces, e.g. Netflix. YouTube has more than 1 billion users, with revenue in 2015 estimated at $4 billion per year. And there are a large number of online creatives earning significant incomes from their content in these spaces.

Previously online creatives were bound up with ideas of democratic participative cultures but we want to offer an immanent critique of the limits of data analytics/algorithmic culture in shaping SME from with the industry on both the creator (bottom up) and platform (top down) side. This is an approach to social criticism exposes the way reality conflicts not with some “transcendent” concept of rationality but with its own avowed norms, drawing on Foucault’s work on power and domination.

We undertook a large number of interviews and from that I’m going to throw some quotes at you… There is talk of information overload – of what one might do as an online creative presented with a wealth of data. Creatives talk about the “non-scalable practices” – the importance and time required to engage with fans and subscribers. Creatives talk about at least half of a working week being spent on high touch work like responding to comments, managing trolls, and dealing with challenging responses (especially with creators whose kids are engaged in their content).

We also see cross-platform engagement – and an associated major scaling in workload. There is a volume issue on Facebook, and the use of Twitter to manage that. There is also a sense of unintended consequences – scale has destroyed value. Income might be $1 or $2 for 100,000s or millions of views. There are inherent limits to algorithmic culture… But people enjoy being part of it and reflect a real entrepreneurial culture.

In one or tow sentences, the history of YouTube can be seen as a sort of clash of NoCal and SoCal cultures. Again, no-one knows what it is for. And that conflict has been there for ten years. And you also have the MCNs (Multi-Contact Networks) who are caught like the meat in the sandwich here.

Panel Q&A

Q1) I was wondering about user needs and how that factors in. You all drew upon it to an extent… And the dissatisfaction of users around whether needs are listened to or not was evident in some of the case studies here. I wanted to ask about that.

A1 – Nancy) There are lots of users, and users have different needs. When platforms change and users are angry, others are happy. We have different users with very different needs… Both of those perspectives are user needs, they both call for responses to make their needs possible… The conflict and challenges, how platforms respond to those tensions and how efforts to respond raise new tensions… that’s really at the heart here.

A1 – Jean) In our historical work we’ve also seen that some users voices can really overpower others – there are influential users and they sometimes drown out other voices, and I don’t want to stereotype here but often technical voices drown out those more concerned with relationships and intimacy.

Q2) You talked about platforms and how they developed (and I’m afraid I didn’t catch the rest of this question…)

A2 – David) There are multilateral conflicts about what features to include and exclude… And what is interesting is thinking about what ideas fail… With creators you see economic dependence on platforms and affordances – e.g. versus PGC (Professionally Generated Content).

A2 – Nicholas) I don’t know what user needs are in a broader sense, but everyone wants to know who unfriended them, who deleted them… And a dislike button, or an unlike button… The response was strong but “this post makes me sad” doesn’t answer that and there is no “you bastard for posting that!” button.

Q3) Would it be beneficial to expose unfriending/negative traits?

A3 – Nicholas) I can think of a use case for why unfriending would be useful – for instance wouldn’t it be useful to understand unfriending around the US elections. That data is captured – Facebook know – but we cannot access it to research it.

A3 – Stuart) It might be good for researchers, but is it in the public good? In Europe and with the Right to be Forgotten should we limit further the data availability…

A3 – Nancy) I think the challenge is that mismatch of only sharing good things, not sharing and allowing exploration of negative contact and activity.

A3 – Jean) There are business reasons for positivity versus negativity, but it is also about how the platforms imagine their customers and audiences.

Q4) I was intrigued by the idea of the “Medium specificity of platforms” – what would that be? I’ve been thinking about devices and interfaces and how they are accessed… We have what we think of as a range but actually we are used to using really one or two platforms – e.g. Apple iPhone – in terms of design, icons, etc. and the possibilities of interface is, and what happens when something is made impossible by the interface.

A4 – Anne) When the “medium specificity” we are talking about the platform itself as medium. Moving beyond end user and user experience. We wanted to take into account the role of the user – the platform also has interfaces for developers, for advertisers, etc. and we wanted to think about those multiple interfaces, where they connect, how they connect, etc.

A4 – Taina) It’s a great point about medium specitivity but for me it’s more about platform specifity.

A4 – Jean) The integration of mobile web means the phone iOS has a major role here…

A4 – Nancy) We did some work with couples who brought in their phones, and when one had an Apple and one had an Android phone we actually found that they often weren’t aware of what was possible in the social media apps as the interfaces are so different between the different mobile operating systems and interfaces.

Q5) Can you talk about algorithmic content and content innovation?

A5 – David) In our work with YouTube we see forms of innovation that are very platform specific around things like Vine and Instagram. And we also see counter-industrial forms and practices. So, in the US, we see blogging and first person accounts of lives… beauty, unboxing, etc. But if you map content innovation you see (similarly) this taking the form of gaps in mainstream culture – in India that’s stand up comedy for instance. Algorithms are then looking for qualities and connections based on what else is being accessed – creating a virtual circle…

Q6) Can we think of platforms as instable, about platforms having not quite such a uniform sense of purpose and direction…

A6 – Stuart) Most platforms are very big in terms of their finance… If you compare that to 20 years ago the big companies knew what they were doing! Things are much more volatile…

A6 – Jean) That’s very common in the sector, except maybe on Facebook… Maybe.

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