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The Intimate Encyclopedia

The Intimate Encyclopedia is an experiment that makes explicit the subjectivities of encyclopedic knowledge. Using Wikipedia as inspiration, it offers three core principles guiding the writing of articles. It asks authors to present the 1. Subjective Point of View (IE:SPOV), warns readers that content is 2. Unverifiable and encourages 3. All Original Research (AOR). Although the Intimate Encyclopedia is no longer, this record reminds us of the alternative ways of representing knowledge, distinct from the logics that guide our current truthmaking practices.

Revision 245 of the Intimate Encyclopedia as at 11 December 2020

The following is from a talk I gave at the recent Digital Intimacies symposium organised by Paul Byron, Suneel Jethani, Amelia Johns and Natalie Krikowa, from my discipline group (Digital and Social Media) at the University of Technology Sydney.

I spend most of my time these days trying to understand what it means to know, whose knowledge is recognised and how knowledge should be governed. I do this in a world materially constituted by data and epistemologically by a moment in which truth seems to be located either as a result of machinic (as opposed to human) processes, or in the humans and crowds who seem to epitomise the rejection of a kind of politics that seems to muddy the truth. Seems, because even the algorithms that drive our truth machines are, we know, a very human craft and very much political artefacts. Seems, because the politicians who rise on the back of an idea that politics is corrupt, we learn are themselves often politically corrupt. Seems, because crowds are not – as Surowieki claimed – all wise. They do not always produce more truthful representations than individuals or groups, even if accuracy were the only thing we were in need of right now.

I’m interested in the governance of knowledge and my primary site of study is Wikipedia. When I tried to think about how I’d contribute to a conference dedicated to “Digital Intimacies”, I couldn’t imagine how. Wikipedia seems the opposite of intimate knowledge. Its policies are conservative and representative of Western enlightenment traditions. It asks editors to leave their knowledge at the door in favour of what it considers “reliable sources”, not to do original research, to represent the Neutral Point of View (NPOV).

And yet, in the decade of my research about the 2011 Egyptian Revolution article, constructed as protests descended in ever increasing waves on Egypt’s streets, I learned that intimate knowledge was everywhere. It was in the decisions about what facts to exclude, about who to contact on the ground for verification, in the knowledge about how Wikipedia really works and who to engage in order to make it work for them. As Donna Haraway wrote: “All knowledges are situated. There can be no ‘infinite vision’ – it is a ‘god trick’ (Haraway, 1988, p. 581).

And so, I started to imagine what an encyclopedia that opened itself up to this idea would look like and how it would be governed. This experiment makes knowledge’s subjectivity explicit. With the help of my colleagues in the Digital and Social Media discipline at the UTS School of Communication, we wrote seven encyclopedic articles for the inaugural and only version of the Intimate Encyclopedia. My instructions to authors were to write encyclopedia articles from a personal rather than objective point of view. The other rules came later, as they did with Wikipedia.

The Intimate Encyclopedia begins with three core content principles:

1. Subjective Point of View (IE:SPOV)

All Intimate encyclopedia articles and other encyclopedic content must be written from a subjective point of view, representing the authors’ views truthfully, momentarily and with as much bias as possible.

IE:SPOV

In the example below, Tisha Dejmanee defines the suitcase not only as a “form of luggage” but as a companion (accompanying Tisha to “grad school and new jobs, new houses and growing networks”) that is too big to hide in her new home. For Dejmanee, the suitcase (her suitcase) is symbolic of “the ruptures of 2020 while also serving as a reminder of the continued longing that carries people and hope across the world”. This statement is highly subjective (since when are suitcases symbolic?!) and thus perfectly suitable for the Intimate Encyclopedia.

“Suitcase” by Tisha Dejmanee

In another example, Paul Byron defines his chosen object, the “Portable Webcam” as “a video camera that feeds or streams an image or video in real time” but also as an instrument of oppression that represents constant surveillance and that is reflective of “a sad story of somebody who spends a lot of time at a desk.” This perspective on the webcam is reflective of a very particular moment in time and contains opinions rather than knowledge. Its place in the Intimate Encyclopedia is guaranteed!

“Portable Webcam” by Paul Byron

The second core content principle of the Intimate Encyclopedia is that it is:

2. Unverifiable (IE:U)

References provided are an indication but not evidence of the source for authors’ inspiration. Readers of the Intimate Encyclopedia must accept that authors have produced an accurate representation of their thoughts and feelings. The Intimate Encyclopedia was at one time open for challenge but is no longer*.

IE:U

In the example below, I write about the “Teapot”, “a vessel for steeping black tea leaves in boiling water”. “Only BLACK TEA?” you cry! This is an unverifiable statement (along with the method of making Proper Way tea). The citations here are a ruse – they do not support the statements made. Thankfully there is no need for verifiable knowledge on the Intimate Encyclopedia. Teapots, for this author, are “fragile things” whose “fragility reminded Ford of the tenuousness of our existence and the importance of celebrating small joys – even if they consisted only in a sip of a properly made cup of tea in a real tea cup and from a pot of freshly brewed tea made, importantly, in a teapot.”

“Teapot” by Heather Ford

“Kangaroo Paw” by Amelia Johns is equally unverifiable. Little to Johns’ knowledge, the kangaroo paw was sourced from a warehouse in Melbourne, but we must rely on Johns’ account because no original receipt was included. Kangaroo Paw, according to Johns, is the companion and toilet to Ella and a reminder of “the delicate balance of nature-animal-human cohabitations that have thrived during the pandemic.”

“Kangaroo paw” by Amelia Johns

3. All Original Research (IE: AOR)

The Intimate Encyclopedia only publishes original, untarnished thought. Although some facts may be attributed to a reliable source, authors must intersperse these with definitions of their own design so that the rendering is completely original.

IE:AOR

Bhuva Narayan’s article on the X-Ray is a very personal account of the object. Instead of an image of a human hand, she reveals that this image is, indeed, of her own hands, her own feet. These reflections are interspersed with factual statements about the ways in which X-rays were preceded by “pre-historic hunting cultures depicted animals by drawing or painting the skeletal frame and internal organs (Chaloupka, 1993)”.

X-Ray by Bhuva Narayan

In the next article about the “Dummy”, Natalie Krikowa classifies dummies as both “nipple substitute(s)” and objects “located in the cracks between couch cushions”. This original rendering is of a very particular set of dummies belonging to a very particular human.

In the final article, about the “Book”, Alan McKee presents a truly original portrait of this common object, making it very strange in this original rendering. Books, according to McKee, are not only “primitive forms of computers” but also objects that enable anxious people to “avoid staring straight into the face of the terrifying world around them”. The image is not an image of “a book” one might regularly see in an encyclopedic article about books but “a book nibbled by a parrot”. Parrots featuring in articles about books! Original indeed.

“Book” by Alan McKee

Coda

This tiny experiment demonstrates, among other things, that there are multiple ways of representing knowledges and that the rules that govern the dominant representations (from Wikipedia, for example) are not natural or obvious but shaped by particular ways of understanding what it means to know.

Through the experiment, I learned few facts about books, plants, webcams, suitcases, teapots, x-rays and dummies. I also learned about what is possibly more important: about the hopes, longings, anxieties and dreams of the people I spend many of my days with. Intimate knowledges are, indeed, a worthy persuit… alongside the Other (objective) forms we are so obsessed with at this moment in time.

* The Intimate Encyclopedia was technically available to the public for only a few weeks, even though we didn’t let anyone other than the participants of the conference. This is the only record of its existence.

Thanks to Francesco Bailo for installing our Intimate Encyclopedia and helping its authors with their contributions.

Australian Media Literacy Research Symposium

Chris Cooper (Reset Australia), Deliana Iacoban (All Together Now), Arial Bogle (The Australian Strategic Policy Institute’s Cyber Center), myself and James Arvanitakis at the Australian Media Literacy Research Symposium, 13 April 2021, Western Sydney University Paramatta Campus

Last week, the Australian Media Literacy Research Symposium was held simultaneously in Sydney, Canberra and Brisbane. Organised by Tanya Notley, Michael Dezuanni and Sora Park, the symposium brought together representatives from civil society, government, the major platforms and research institutions interested in media literacy in Australia.

I spoke on a panel titled “Using media literacy to confront the impact of disinformation on our democracy” with Arial Bogle (The Australian Strategic Policy Institute’s Cyber Center), Deliana Iacoban (All Together Now) and Chris Cooper (Reset Australia). We had a great discussion about the problems of disinformation and what a national media literacy programme might look like in order to respond to those threats. I talked mostly about my work with Wikipedia and how I’ve been thinking not of systems detecting the truth or falsity of claims but rather their stability or instability in the wake of catalytic events. Below is the vide of the panel.

Fact Factories: Wikipedia and Writing History as it Happens

I will be speaking at the Digital Histories Research Seminar on Thursday 8 October 2020, 6.00pm (AEST).

On the 24th of January, 2011, an Egyptian born Wikipedia editor, “The Egyptian Liberal” published the first draft of an article titled “2011 Egyptian protests” on English Wikipedia. Working with hundreds of other editors over the next two weeks, “The Egyptian Liberal” documented the events that catalysed the downfall of Hosni Mubarak as hundreds of thousands of people descended on Tahrir Square and in cities through the country to demand change. In this talk, I’ll discuss my forthcoming book, Fact Factories. I’ll introduce the concept of traveling facts and the mirroring (and sometimes refracting) of material realities on Wikipedia and in the streets of Egypt in ways that framed and eventually helped determine the result of the protests. The talk is about the writing of history as it happens, about the role of automated technology in our collaborative narration of events and about how Wikipedia’s narration will always be a partial one.

Join via Zoom: https://utsmeet.zoom.us/j/99750414645 

Data analyst/visualisation expert needed

Tamson Pietsch, Head of the Centre for Public History at UTS and I are leading a small pilot project at UTS to analyse Wikipedia’s scope and progress over the past twenty years in Australia together with collaborators, Wikimedia Australia
<https://wikimedia.org.au/wiki/Wikimedia_Australia> (including Pru Mitchell
and 99of9|Toby Hudson). We are looking for someone to help us to
develop a series of visualisations for a pilot project. This will involve
extracting data about en.wp.org articles (either from Wikipedia or via Wikidata) and comparing it to another dataset (possibly the Australian Honours List),
cleaning and coding data and, importantly, visualising the data using
mapping and other visualisation tools. This is a pilot project with resources for a few days work which we would ideally like to happen over the next month. Experience with Wikimedia data analysis is a plus.

Please contact me for more info!

BBC Click on Wikipedia interventions

BBC Click interviewed me for a segment on possible manipulation of Wikipedia by the Chinese state (below). Manipulation of Wikipedia by states is not new. What does seem to be new here, though, is the way in which strategies for intervening in Wikipedia (both through the election of administrators and at individual articles) are so explicitly outlined.

Remember, though, that we can never know who is editing these articles. Even wikiedits bots only pick up edits within government IP address ranges. We have no way of knowing whether the person represented by that IP address in that sitting is employed by the government. The point is that there is a lot to be gained from influencing Wikipedia’s representation of people, places, events and things given Wikipedia’s prioritised role as data source for digital assistants and search engines.

It makes sense, then, that institutions (including governments, corporations and other organisations) will try to give weight to their version of the truth by taking advantage of the weak points of the peer produced encyclopedia. Guarding against that kind of manipulation is critical but not a problem that can be easily solved. More thoughts on that soon…

PhD Scholarships on “Data Justice” and “Living with Pervasive Media Technologies from Drones to Smart Homes”

I’m excited to announce that I will be co-supervising up to four very generous and well-supported PhD scholarships at the University of New South Wales (Sydney) on the themes of “Living with Pervasive Media Technologies from Drones to Smart Homes” and “Data Justice: Technology, policy and community impact”. Please contact me directly if you have any questions. Expressions of Interest are due before 20 July, 2017 via the links below. Please note that you have to be eligible for post-graduate study at UNSW in order to apply – those requirements are slightly different for the Scientia programme but require that you have a first class honours degree or a Master’s by research. There may be some flexibility here but that would be ideal.

Living with Pervasive Media Technologies from Drones to Smart Homes

Digital assistants, smart devices, drones and other autonomous and artificial intelligence technologies are rapidly changing work, culture, cities and even the intimate spaces of the home. They are 21st century media forms: recording, representing and acting, often in real-time. This project investigates the impact of living with autonomous and intelligent media technologies. It explores the changing situation of media and communication studies in this expanded field. How do these media technologies refigure relations between people and the world? What policy challenges do they present? How do they include and exclude marginalized peoples? How are they transforming media and communications themselves? (Supervisory team: Michael Richardson, Andrew Murphie, Heather Ford)

Data Justice: Technology, policy and community impact

With growing concerns that data mining, ubiquitous surveillance and automated decision making can unfairly disadvantage already marginalised groups, this research aims to identify policy areas where injustices are caused by data- or algorithm-driven decisions, examine the assumptions underlying these technologies, document the lived experiences of those who are affected, and explore innovative ways to prevent such injustices. Innovative qualitative and digital methods will be used to identify connections across community, policy and technology perspectives on ‘big data’. The project is expected to deepen social engagement with disadvantaged communities, and strengthen global impact in promoting social justice in a datafied world. (Supervisory team: Tanja Dreher, Heather Ford, Janet Chan)

Further details on the UNSW Scientia Scholarship scheme are available on the titles above and here:
https://www.2025.unsw.edu.au/apply/?interest=scholarships 

Wikipedia’s relationship to academia and academics

I was recently quoted in an article for Science News about the relationship between academia and Wikipedia by Bethany Brookshire. I was asked to comment on a recent paper by MIT Sloan‘s Neil Thompson and Douglas Hanley who investigated the relationship between Wikipedia articles and scientific papers using examples from chemistry and econometrics. There are a bunch of studies on a similar topic (if you’re interested, here is a good place to start) and I’ve been working on this topic – but from a very different angle – for a qualitative study to be published soon. I thought I would share my answers to the interview questions here since many of them are questions that friends and colleagues ask regularly about citing Wikipedia articles and about quality issues on Wikipedia.

Have you ever edited Wikipedia articles?  What do you think of the process?

Some, yes. Being a successful editor on English Wikipedia is a complicated process, particularly if you’re writing about topics that are either controversial or outside the purview of the majority of Western editors. Editing is complicated not only because it is technical (even with the excellent new tools that have been developed to support editing without having to learn wiki markup) – most of the complications come with knowing the norms, the rules and the power dynamics at play.

You’ve worked previously with Wikipedia on things like verification practices. What are the verification practices currently?

That’s a big question 🙂 Verification practices involve a complicated set of norms, rules and technologies. Editors may (or may not) verify their statements by checking sources, but the power of Wikipedia’s claim-making practice lies in the norms of questioning  unsourced claims using the “citation needed” tag and by any other editor being able to remove claims that they believe to be incorrect. This, of course, does not guarantee that every claim on Wikipedia is factually correct, but it does enable the dynamic labelling of unverified claims and the ability to set verification tasks in an iterative fashion.

Many people in academia view Wikipedia as an unreliable source and do not encourage students to use it. What do you think of this?

Academic use of sources is a very contextual practice. We refer to sources in our own papers and publications not only when we are supporting the claims they contain, but also when we dispute them. That’s the first point: even if Wikipedia was generally unreliable, that is not a good reason for denying its use. The second point is that Wikipedia can be a very reliable source for particular types of information. Affirming the claims made in a particular article, if that was our goal in using it, would require verifying the information that we are reinforcing through citation and in citing the particular version (the “oldid” in Wikipedia terms) that we are referring to. Wikipedia can be used very soundly by academics and students – we just need to do so carefully and with an understanding of the context of citation – something we should be doing generally, not only on Wikipedia.

You work in a highly social media savvy field, what is the general attitude of your colleagues toward Wikipedia as a research resource? Do you think it differs from the attitudes of other academics?

I would say that Wikipedia is widely recognized by academics, including those of my colleagues who don’t specifically conduct Wikipedia research, as a source that is fine to visit but not to cite.

What did you think of this particular paper overall?

I thought that it was a really good paper. Excellent research design and very solid analysis. The only weakness, I would argue, would be that there are quite different results for chemistry and econometrics and that those differences aren’t adequately accounted for. More on that below.

The authors were attempting a causational study by adding Wikipedia articles (while leaving some written but unadded) and looking at how the phrases translated to the scientific literature six months later. Is this a long enough period of time?

This seems to be an appropriate amount of time to study, but there are probably quite important differences between fields of study that might influence results. The volume of publication (social scientists and humanities scholars tend to produce much lower volumes of publications and publications thus tend to be extended over time than natural science and engineering subjects, for example), the volume of explanatory or definitional material in publications (requiring greater use of the literature), the extent to which academics in the particular field consult and contribute to Wikipedia – all might affect how different fields of study influence and are influenced by Wikipedia articles.

Do you think the authors achieved evidence of causation here?

Yes. But again, causation in a single field i.e. chemistry.

It is important to know whether Wikipedia is influencing the scientific literature? Why or why not?

Yes. It is important to know whether Wikipedia is influencing scientific literature – particularly because we need to know where power to influence knowledge is located (in order to ensure that it is being fairly governed and maintained for the development of accurate and unbiased public knowledge).

Do you think papers like this will impact how scientists view and use Wikipedia?

As far as I know, this is the first paper that attributes a strong link between what is on Wikipedia and the development of science. I am sure that it will influence how scientists and other academic view and use Wikipedia – particularly in driving initiatives where scientists contribute to Wikipedia either directly or via initiatives such as PLoS’s Topic Pages.

Is there anything especially important to emphasize?

The most important thing is to emphasize the differences between fields that I think needs to be better explained. I definitely think that certain types of academic research are more in line with Wikipedia’s way of working, forms and styles of publication and epistemology and that it will not have the same influence on other fields.

Towards software that supports interpretation rather than quantification

Towards software that supports interpretation rather than quantification

[Reblogged from the Software Sustainability Institute blog]

My research involves the study of the emerging relationships between data and society that is encapsulated by the fields of software studies, critical data studies and infrastructure studies, among others. These fields of research are primarily aimed at interpretive investigations into how software, algorithms and code have become embedded into everyday life, and how this has resulted in new power formations, new inequalities, new authorities of knowledge [1]. Some of the subjects of this research include the ways in which Facebook’s News Feed algorithm influences the visibility and power of different users and news sources (Bucher, 2012), how Wikipedia delegates editorial decision-making and moral agency to bots (Geiger and Ribes, 2010), or the effects of Google’s Knowledge Graph on people’s ability to control facts about the places in which they live (Ford and Graham, 2016).

As the only Software Sustainability Institute fellows working in this area, I set myself the goal of investigating what tools, methods and infrastructure researchers working in these fields were using to conduct their research. Although Big Data is a challenge for every field of research, I found that the challenge for social scientists and humanities scholars doing interpretive research in this area is unique and perhaps even more significant. Two key challenges stand out. The first is that data requiring interpretation tends to be much larger than traditionally analysed. This often requires at least some level of quantification in order to ‘zoom out’ to obtain a bigger picture of the phenomenon or issues under study. Researchers in this tradition often lack the skills to conduct such analyses – particularly at scale. The second challenge is that online data is subject to ethical and legal restrictions, particularly when research involves interpretive research (as opposed to the anonymized data collected for statistical research).

In many universities it seems that mathematics, engineering, physics and computer science departments have started to build internal infrastructure to deal with Big Data, and some universities have established good Digital Humanities programs that are largely about the quantitative study of large corpuses of images/films/videos or other cultural objects. But infrastructure and expertise is severely lacking for those wishing to do interpretive rather than quantitative research using mixed, experimental, ethnographic or qualitative research using online data. The software and infrastructure required for doing interpretive research is patchy, departments are typically ill-equipped to support researchers and students with the expertise required to conduct social media research, and significant ethical questions remain about doing social media research, particularly in the context of data protection laws.

Data Carpentry offers some promise here. I organized, with the support of the Software Sustainability Institute, a “Data Carpentry for the Social Sciences workshop” with Dr Brenda Moon (Queensland University of Technology) and Martin Callaghan (University of Leeds) in November 2016 at Leeds University. Data Carpentry workshops tend to be organized for quantitative work in the hard sciences and there were no lesson plans for dealing with social media data. Brenda stepped in to develop some of these materials based partly on the really good Library Carpentry resources and both Martin and Brenda (with additional help from Dr Andy Evans, Joanna Leng and Dr Viktoria Spaiser) made an excellent start towards seeding the lessons database with some social media specific exercises.

The two-day workshop centered on examples from Twitter data and participants worked with Python and other off-the-shelf tools to extract and analyze data. There were fourteen participants in the workshop ranging from PhD students to professors and from media and communications to sociology and social policy, music to law, earth and environment to translation studies. At the end of the workshop participants said that they felt they had received a strong grounding in Python and that the course was useful, interactive, open and not intimidating. There were suggestions, however, to make improvements to the Twitter lessons and to perhaps split up the group in the second day to move onto more advanced programming for some and to go over the foundations for beginners.

Also supported by the Institute was my participation in two conferences in Australia at the end of 2016. The first was a conference exploring the impact of automation on everyday life at the Queensland University of Technology in Brisbane, the second, the annual Crossroads in Cultural Studies conference in Sydney. Through my participation in these events (and via other information-gathering that I have been conducting in my travels) I have learned that many researchers in the social sciences and humanities suffer from a significant lack of local expertise and infrastructure. On multiple occasions I learned of PhD students and researchers running analyses of millions of tweets on their laptops, suffering from a lack of understanding when applying for ethical approval and conducting analyses that lack a consistent approach.

Centers of excellence in digital methods around the world share code and learnings where they can. One such program is the Digital Methods Initiative (DMI) at the University of Amsterdam. The DMI hosts regular summer and winter schools to train researchers in using digital methods tools and provides free access to some of the open source software tools that it has developed for collecting and analyzing digital data. Queensland University of Technology’s Social Media Group also hosts summer schools and has contributed to methodological scholarship employing interpretive approaches to social media and internet research. The common characteristic of such programmes are that they are collaborative (sharing resources across the university departments and between different universities) and innovative (breaking some of the traditional rules that govern traditional research in the university).

Many researchers who handle data in more interpretive studies tend to rely on these global hubs in the few universities where infrastructure is being developed. The UK could benefit from a similar hub for researchers locally, especially since software and code needs to be continually developed and maintained for a much wider variety of evolving methods. Alternatively, or alongside such hubs, Data Carpentry workshops could serve as an important virtual hub for sharing lesson plans and resources. Data Carpentry could, for example, host code that can be used to query APIs for doing social media research and workshops could also be used to collaboratively explore or experiment with methods for iterative, grounded investigation of social media practices.

Due to the rapid increase in the scale and velocity of social media data and because of the lack of technical expertise to manage such data, social scientists and humanities scholars have taken a backseat to the hard sciences in explaining new dimensions of social life online. This is disappointing because it means that much of the research coming out about social media, Big Data and the computation lacks a connection to important social questions about the world. Building from some of this momentum will be essential in the next few years if we are to see social scientists and humanities scholars adding their important insights into social phenomena online. Much more needs to be done to build flexible and agile resources for the rapidly advancing field of social media research if we are to benefit from the contributions of social science and humanities scholars in the field of digital cultures and politics.

[1] For an excellent introduction to the contribution of interpretive scholars to questions about data and the digital see ‘The Datafied Society’ just published by Amsterdam University Press http://en.aup.nl/books/9789462981362-the-datafied-society.html

Pic: Martin Callaghan displays the ‘Geeks and repetitive tasks’ model during the November 2016 Data Carpentry for the Social Sciences workshop at Leeds University.

Human-bot relations at ICA 2017 in San Diego

News this week that a panel I contributed to on political bots has been accepted for the annual International Communication Association (ICA) conference in San Diego with Amanda Clarke, Elizabeth Dubois, Jonas Kaiser and Cornelius Puschmann this May. Political bots are automated agents that are deployed on social media platforms like Twitter to perform a variety of functions that are having a significant impact on politics and public life. There is already some great work about the negative impact of bots that are used to “manipulate public opinion by megaphoning or repressing political content in various forms” (see politicalbots.org) but we were interested in the types of bots these bots are often compared to — the so-called “good” bots that expose the actions of particular types of actors (usually governments) and thereby bring about greater transparency of government activity.

Elizabeth, Cornelius and I worked on a paper about WikiEdits bots for ICA last year in the pre-conference: “Algorithms, Automation, Politics” (“Keeping Ottawa Honest — One Tweet at a Time?” Politicians, Journalists and their Twitter bots, PDF) where we found that the impact of these bots isn’t as simple as bringing about greater transparency. The new work that we will present in May is a deeper investigation of the types of relationships that are catalysed by the existence and ongoing development of transparency bots on Twitter. I’ll be working on the relationship between bots and their creators in both Canada and South Africa, attempting to investigate the relationship between the bots and the transparency that they promise. Cornelius is looking at the relationship between journalists and bots, Elizabeth and Amanda are looking at the relationship between bots and political staff/government employees, and Jonas will be looking more closely at bots and users. The awesome Stuart Geiger who has done some really great work on bots has kindly agreed to be a respondent to the paper.

You can read more about the panel and each of the papers below.

Do people make good bots bad?

Political bots are not necessarily good or bad. We argue the impact of transparency bots (a particular kind of political bot) rests largely on the relationships bots have with their creators, journalists, government and political staff, and the general public. In this panel each of these relationships is highlighted using empirical evidence and a respondent guides wider discussion about how these relationships interact in the wider political and media system.

This panel challenges the notion that political bots are necessarily good or bad by highlighting relationships between political actors and transparency bots. Transparency bots are automated social media accounts which report behaviour of political players/institutions and are normally viewed as a positive force for democracy. In contrast, bot activity such as astroturfing and the creation of fake followers or friends on social media has been examined and critiqued as nefarious in academic and popular literature. We assert that the impact of transparency bots rests largely on the relationships bots have with their creators, journalists, government and political staff, and the general public. Each panelist highlights one of these relationships (noting related interactions with additional actors) in order to answer the question “How do human-bot relationships shape bots’ political impact?”

Through comparative analysis of the Canadian and South African Wikiedits bots, Ford shows that transparency is not a potential affordance of the technology but rather of the conditions in place between actors. Puschmann considers the ways bots are framed and used by journalists in a content analysis of news articles. Dubois and Clarke articulate the ways public servants and political staff respond to the presence of Wikiedits bots revealing that internal institutional policies mediate the relationships these actors can have with bots. Finally, Kaiser asks how users who are not political elite actors frame transparency bots making use of a quantitative and qualitative analysis of Reddit content.

Geiger (respondent) then poses questions which cut across the relationships and themes brought out by panelists. This promotes a holistic view of the bot in their actual communicative system. Cross-cutting questions illustrate that the impact of bots is seen not simply in dyadic relationships but also in the ways various actors interact with each other as well as the bots in question.

This panel is a needed opportunity to critically consider the political role and impact of transparency bots considering the bot in context. Much current literature assumes political bots have significant agency, however, bots need to interact with other political actors in order to have an impact. A nuanced understanding of the different types of relationships among political actors and bots that exists is thus essential. The cohesive conversation presented by panelists allows for a comparison across the different kinds of bot-actor relationships, focusing in detail on particular types of actors and then zooming out to address the wider system inclusive of these relationships.

  1. Bots and their creators
    Heather Ford

Bots – particularly those with public functions such as government transparency – are often created and recreated collaboratively by communities of technologists who share a particular world view of democracy and of technology’s role in politics and social change. This paper will focus on the origins of bots in the motivations and practices of their creators focusing on a particular case of transparency bots. Wikipedia/Twitter bots are built to tweet every time an editor within a particular government IP range edits Wikipedia as a way of notifying others to check possible government attempts to manipulate facts on the platform. The outputs of Wikipedia/Twitter bots have been employed by journalists as sources in stories about governments manipulating information (Ford et al, 2016).

Investigating the relationship between bot creators and their bots in Canada and South Africa by following the bots and their networks using mixed methods, I ask: To what extent is transparency an affordance of the particular technology being employed? Or is transparency rather an affordance of the conditions in place between actors in the network? Building from theories of co-production (Jasanoff, 2004) and comparing the impact of Wikipedia/Twitter bots on the news media in Canada and South Africa, this paper begins to map out the relationships that seem to be required for bots to take on a particular function (such as government transparency). Findings indicate that bots can only become transparency bots through the enrolling of allies (Callon, 1986) and through particular local conditions that ensure success in achieving a particular outcome. This is a stark reminder of the connectedness of human-machine relations and the limitations on technologists to fully create the world they imagine when they build their bots.

 

2. Bots and Journalists
Cornelius Puschmann

Different social agents — human and non-human — compete for attention, spread information and contribute to political debates online. Journalism is impacted by digital automation in two distinct ways: Through its potentially manipulative influence on reporting and thus public opinion (Woolley & Howard, 2016, Woolley, 2016), and by providing journalists with a set of new tools for providing insight, disseminating information, and connecting with audiences (Graefe, 2016; Lokot & Diakopoulos, 2015). This contribution focuses primarily on the first aspect, but also takes the second into account, because we argue that fears of automation in journalism may fuel reservations among journalists regarding the role of bots more generally.

To address the first aspect, we present the results of a quantitative content analysis of English-language mainstream media discourse on bots. Building on prior research on the reception of Bots (Ford et al, 2016), we focus on the following aspects in particular:

– the context in which bots are discussed,

– the evaluation (“good” for furthering transparency, “bad” because they spread propaganda),

– the implications for public deliberation (if any).

Secondly, we discuss the usage of bots and automation for the news media, using a small set of examples from the context of automated journalism (Johri, Han & Mehta, 2016). Bots are increasingly used to automate particular aspects of journalism, such as the generation of news items and the dissemination of content. Building on these examples we point to the “myriad ways in which news bots are being employed for topical, niche, and local news, as well as for providing higher-order journalistic functions such as commentary, critique, or even accountability” (Lokot & Diakopoulos, 2015, p. 2).

 

3. Bots and Government/Political Staff
Elizabeth Dubois and Amanda Clarke

Wikiedits bots are thought to promote more transparent, accountable government because they expose the Wikipedia editing practices of public officials, especially important when those edits are part of partisan battles between political staff, or enable the spread of misinformation and propaganda by properly neutral public servants. However, far from bolstering democratic accountability, these bots may have a perverse effect on democratic governance. Early evidence suggests that the Canadian Wikiedits bot (@gccaedits) may be contributing to a chilling effect wherein public servants and political staff are editing Wikipedia less or editing in ways that are harder to track in order to avoid the scrutiny that these bots enable (Ford et al, 2016). The extent to which this chilling effect shapes public officials’ willingness to edit Wikipedia openly (or at all), and the role the bot plays in inducing this chilling effect, remain open questions ripe for investigation. Focusing on the bot tracking activity in the Government of Canada (@gccaedits), this paper reports on the findings of in-depth interviews with public and political officials responsible for Wikipedia edits as well as analysis of internal government documents related to the bot (retrieved through Access to Information requests).

We find that internal institutional policies, constraints of the Westminster system of democracy (which demands public servants remain anonymous, and that all communications be tightly managed in strict hierarchical chains of command), paired with primarily negative media reporting of the @gccaedits bot, have inhibited Wikipedia editing. This poses risks to the quality of democratic governance in Canada. First, many edits revealed by the bot are in fact useful contributions to knowledge, and reflect the elite and early insider insight of public officials. At a larger level, these edits represent novel and significant disruptions to a public sector communications culture that has not kept pace with the networked models of information production and dissemination that characterize the digital age. In this sense, the administrative and journalistic response to the bot’s reporting sets back important efforts to bolster Open Government and digital era public service renewal. Detailing these costs, and analysing the role of the bot and human responses to it, this paper suggests how wikiedit bots shape digital era governance.

4. Bots and Users
Jonas Kaiser

Users interact online with bots on a daily basis. They tweet, upvote or comment, in short: participate in many different communities and are involved in shaping the user’s perceptions. Based on this experience the users’ perspective on bots may differ significantly from journalists, bot creators or political actors. Yet it is being ignored in the literature up to now. As such we are missing an integral perspective on bots that may help us to understand how the societal discourse surrounding bots is structured. To analyze how and in which context users talk about transparency bots specifically a content analysis and topic analysis of Reddit comments from 86 posts in 48 subreddits on the issue of Wikiedits bots will be conducted. This proposal’s research focuses on two major aspects: how Reddit users 1) frame and with what other 2) topics they associate transparency bots.

Framing in this context is understood as “making sense of relevant events, suggesting what is at issue” (Gamson & Modigliani, 1989, p. 3). Even though some studies have shown, for example, how political actors frame bots (Ford, Dubois, & Puschmann, 2016) a closer look at the user’s side is missing. But this perspective is important as non-elite users may have a different view than the more elite political actors that can help us understand in how they interpret bots. This overlooked perspective, then, could have meaningful implications for political actors or bot creators. At the same time it is important to understand the broader context of the user discourse on transparency bots to properly connect the identified frames with overarching topics. Hence an automated topic modeling approach (Blei, Ng & Jordan, 2003) is chosen to identify the underlying themes within the comments. By combining frame analysis with topic modeling this project will highlight the way users talk about transparency bots and in which context they do so and thus emphasize the role of the users within the broader public discourse on bots.

Bibliography

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Ford, H., Dubois, E., & Puschmann, C. (2016). Automation, Algorithms, and Politics | Keeping Ottawa Honest—One Tweet at a Time? Politicians, Journalists, Wikipedians and Their Twitter Bots. International Journal of Communication, 10, 24.

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Johri et al. (2016). Domain specific newsbots. Live automated reporting systems involving natural language communication. Paper presented at 2016 Computation + Journalism Symposium.

Lokot, T. & Diakopoulos, N. (2015). News bots: Automating news and information dissemination on Twitter. Digital Journalism. doi: 10.1080/21670811.2015.1081822

Woolley, S. C. (2016). Automating power: Social bot interference in global politics. First Monday. doi: 10.5210/fm.v21i4.6161

Woolley, S. C., & Howard, P. (2016). Bots unite to automate the presidential election. Retrieved Jun. 5, 2016, from http://www.wired.com/2016/05/twitterbots-2/

How Wikipedia’s silent coup ousted our traditional sources of knowledge

[Reposted from The Conversation, 15 January 2016]

As Wikipedia turns 15, volunteer editors worldwide will be celebrating with themed cakes and edit-a-thons aimed at filling holes in poorly covered topics. It’s remarkable that a user-editable encyclopedia project that allows anyone to edit has got this far, especially as the website is kept afloat through donations and the efforts of thousands of volunteers. But Wikipedia hasn’t just become an important and heavily relied-upon source of facts: it has become an authority on those facts.

Through six years of studying Wikipedia I’ve learned that we are witnessing a largely silent coup, in which traditional sources of authority have been usurped. Rather than discovering what the capital of Israel is by consulting paper copies of Encyclopedia Britannica or geographical reference books, we source our information online. Instead of learning about thermonuclear warfare from university professors, we can now watch a YouTube video about it.

The ability to publish online cheaply has led to an explosion in the number and range of people putting across facts and opinion than was traditionally delivered through largely academic publishers. But rather than this leading to an increase in the diversity of knowledge and the democratisation of expertise, the result has actually been greater consolidation in the number of knowledge sources considered authoritative. Wikipedia, particularly in terms of its alliance with Google and other search engines, now plays a central role. Continue reading “How Wikipedia’s silent coup ousted our traditional sources of knowledge”