Society of Scholarly Publishing: Fact vs Fake News

The Society for Scholarly Publishing held their regional event in late September at Springer Nature’s Macmillan Campus. With a panel of publishers, journalists, researchers and tech professionals discussing Fact vs Fake News: Who decides what’s true? Anna and Clare went along to hear more. Here is what Anna took away from the discussion:

The Market in Data

In the UK on average we spend 4 hours a day online on a desktop and 1 hour 47 minutes online on our phones (2017). Every moment of that we are producing data which becomes a product. The consumer becomes the product to enhance marketing campaigns and to pinpoint a susceptible audience to target. All of the Cambridge Analytica controversy, Facebook targeting political campaigns at specific users…

Fake news is about persuasion. What to agree with. Variations of an ad will appeal to different people because of their personal bias. Star vs Circle? You are more likely to listen to one than the other.

What makes a fact?

  • Access to the source
  • Expertise
    • Analysis + Interpretation of information -> Explanation to be understood

Challenges to “facts” today?

  • Readability for money
  • Comment and opinion pieces are rising in number
  • Appeal to emotion rather than figures/numbers
  • The incentive is to be read as with “click-bait” to generate income
  • The monopoly of ownership
  • False balance
    • Example: the BBC disproportionately giving voices in debate
    • Lack of diversity in voices writing/speaking
  • Disenfranchisement and lack of knowledge

Technological Solutions?

Annotation Software
  • This uses layers to allow multiple experts to make notes at different levels, separately from the editors but in real time.
  • Collaborators can see what is going on and there is transparency

This can happen before or after publication.

The Credibility Coalition want to create standards (a toolset of questions to give ratings) the appropriateness and accuracy of online content.

Credibility scoring articles are now being published for the public to view, for instance regarding climate change.

Content Assessment

Community-driven start-ups are looking at algorithms, aiming to create higher quality content. These can find whether it is true or false but experts are needed to label content. Classification then improves the algorithms in the “grey areas”.

Nuance is the tricky point!

Problems

  • Data literacy is poor
  • Data claims to know you better than you know yourself, matching you to content…
    • What you can do: understand what processes look like and where your data is going.

Students can’t evaluate sources or different viewpoints constructively – more empathy is needed

Steps Going Forwards

Make the community bias visible.

  • We choose our media. Choose more than one.
  • Diversify perspectives. Have more than one viewpoint.
  • Democratise expertise

New technology allows gutter journalism which we have always had to be “published” wider than ever but accessibility and agency do fight fake news.

Why should businesses care?

  • Reputations are at stake when advertising platforms are discredited.
  • When disenfranchisement grows, the consumers change.

Why should you care?

  • You deserve better than fake news!

Moderator: 
David Bull, Vice President for Business, Economics, Political Science & Law publishing at Springer Nature, the world renowned academic publisher

Panelists:
Michael Parker, Membership Editor at The Conversation UK
Jennifer PybusLecturer in Digital Culture and Society at KCL and contributing author to the new book “Trump Media War
Lusiné Mehrabyan, Community Manager at FactMata
Heather Staines, Director, Partnerships at Hypothes.is

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