Digital Intermediation

A study of automated media influencers
Jonathon_Hutchinson_Digital_Intermediation

Digital Intermediation: A study of automated media influencers

Digital Intermediation: A study of automated media influencers 1024 683 jonathonhutchinson

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

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

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

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

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

Aims and Outcomes 

The aims of the project are:  

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

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

The primary research question is:  

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

The research subquestions are: 

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

Background 

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

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

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

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

Original by Ari He on Unsplash

jonathonhutchinson

Sydney based media academic. Researching internet technologies, social media and public service media.

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