Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
Susannah Kate Devitt1, Monique Mann2, Angela Daly3
1Defence Science and Technology Group, Australia; 2Queensland University of Technology; 3Chinese University of Hong Kong
In recent years, there has been an exponential increase in the collection, aggregation and automated analysis of information by government and private actors that disproportionately disadvantages the underrepresented, marginalized and unheard. In response to this there has been significant critique regarding what could be termed ‘bad’ data practices in the globalised digital economy. Considerations of ‘bad data’ practices often only provide critiques rather than engaging constructively with a new vision of how digital technologies and data can be used productively and justly to promote social, economic, cultural and politically progressive goals. In this paper we consider the fundamentals of Good Data to increase trust. We begin by conceptual considerations of the nature of ‘data’ and ‘goodness’. We align our principles with the Data Information Knowledge Wisdom (DIKW) model and use the term ‘data’ as a proxy for the whole DIKW model. Given the limits of our knowledge of moral facts (should they exist) and in light of colonial and post-colonial data practices we assume a hybrid moral theory—where we allow that some moral facts may be objective (e.g. ‘tolerance’ or ‘openness’) and others relative. We advocate an ethic of active seeking, openness and tolerance to diverse views on ‘the good’ particularly consultation with the underrepresented, marginalised and unheard. We go on to defend fifteen principles of good data under four banners: Community, Rights, Usability & Politics in order to ultimately progress a more just digital economy and society.
11:20am - 11:40am
Critical Simulation: Investigating the work of machine vision in visual social media culture
Nicholas Carah1, Daniel Angus2, Adam Smith1
1The University of Queensland, Australia; 2Queensland University of Technology, Australia
Social media are in the midst of an emphatic visual turn (Highfield & Leaver 2016), characterised by the convergence of everyday visual expression with professional creative practice and advertising. The advertising-driven business models of social media platforms increasingly depend on automation. Platforms’ use of machine vision is a key frontier in the algorithmic classification of culture. Machine vision algorithms automatically classify and misclassify faces, expressions, objects, and brand logos in the images users create and share. Images shared by platform users form vast databases used to train these same algorithms. Despite widespread use by social platforms, machine vision is poorly understood and accounted for in the study of everyday visual cultures. In this paper we detail a critical response to the use of automation in visual social media, called critical simulation. We outline a critical simulation framework, the ‘Image Machine’, focussed firstly on Instagram. The Image Machine comprises an Instagram data harvester, and open-access machine vision toolbox that allows digital humanities researchers to interrogate the inner workings of these algorithms and analyse their visual (mis)classifications. In this paper we showcase results from the Image Machine applied to images emanating from a major Australian music festival, Splendour in the Grass. This case examines not only how machine vision classifies and operates on culture, but also how these techniques are being operationalised within the advertising model and promotional culture of platforms like Instagram. We argue that the commercial application of machine vision is interdependent with the participatory culture of platforms like Instagram.
11:40am - 12:00pm
Household Digital Media Ecologies - Methodological Innovations for Fostering Researcher-Participant Trust
Jenny Kennedy1, Rowan Wilken1, Bjorn Nansen2, Michael Arnold2, Martin Gibbs2
1RMIT University, Australia; 2The University of Melbourne, Australia
In this paper, we describe a research methodology we have developed, based upon digital ethnography approaches, and which used mobile devices, digital ethnographic software and creative data collection activities. Our approach, refined over the course of a number of interconnected research projects, addressed these difficulties through a staged process – utilising traditional ethnographic techniques, but augmenting them with something more novel: the “domestic probe”. In essence, the domestic probe comprised a box of equipment given to the household to use in order to record and interpret their use of domestic technologies. In more recent work, we extended our participatory approach through the use of digital media, such as by using iPad minis pre-loaded with a data collection software tool, Ethnocorder. As we argue in this paper, these approaches carry three specific trust-related methodological benefits (and challenges): the foster trust in us as researchers; trust in our participants as co-researchers; and, as a result of this mutual researcher-participant trust, insight and a productive point of entry into discussing participant "domestication" of, and trust in, various household technologies.
12:00pm - 12:20pm
The QUT Digital Observatory Project: Building a Trusted Data Infrastructure for Social Media Research
Marissa Takahashi, Sam Hames, Elizabeth Alpert, Axel Bruns
Queensland University of Technology, Australia
Trust is fragile. The 2018 Facebook and Cambridge Analytica debacles highlighted how data harvested from social media platforms can be used not only for commercial purposes but also for political manipulation. This incident and the widespread discussion around it further demonstrated the following issues: unethical data collection enabled by a platform; unethical use of data for corporate and political interest; and unethical data sharing by an academic.
Research needs to be credible to maintain social license. Data is the lifeblood of research. For research to remain credible, research needs to remain fundamentally ethical and research methods comprising data collection and data analysis need to be robust, transparent, repeatable, and auditable. Such methods alone cannot create credibility, but research data infrastructure design and implementation can provide a foundation for credibility by addressing these fundamental processes.
Social science research has traditionally relied on data collection methods such as surveys, interviews, and ethnographic observations. However, an increasing proportion of human life is being mediated by online platforms, with approximately 2.3 billion active users on Facebook and 326 million active users on Twitter (Statista 2019). Social media data collection and analysis have become imperative for researchers interested in various phenomena playing out in these new media. This paper discusses the current state and issues of social media data collection and describes the Digital Observatory’s approach to establishing a credible and trusted research data infrastructure.