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Are you reading a human-written article? Or?

“In other words, regardless of the actual source [human or robojournalist], participants assigned higher ratings simply if they thought that they read a human-written article.” — Graefe & Bohlken (2020) [4, p.57]
Are you reading a human-written article? Or?

Robojournalism, a topic of risen discussion, whether the future of human journalists is at stake. There were some quite recent news about human journalists losing their jobs for robojournalists such as Microsoft ‘to replace journalists with robots’ in 2020. Heard of Robojournalists ever before? No? Have a good read then!

Robojournalism, or Automated Journalism, is an awesome way to produce reports on repetitive events available to people and at the same time letting human journalists focus on work that requires research and human insight. In this process, some data is transformed into news reports written in some human language. This is achieved with natural language generation (NLG) techniques.

I hope you are aware of structured data and unstructured data. According to the research done by Graefe, robojournalism is the best fit when there is structured data available, and the topic is repetitive in nature. Some traditional use cases are sports, financial and weather reporting. He also stated the process of automated journalism in five phases, as stated below:

  1. Collection of data
  2. Identification of interesting events
  3. Prioritisation of insights
  4. Generation of the narrative
  5. Publishing the story

Sounds cool? Definitely! But what about the credibility or worthiness?

Automated journalism systems require input from domain experts to define domain specific rules, and criteria of newsworthiness. There are requirements for human journalists to fulfil for a news report to be qualified as good. From those requirements identified for human journalists, Leppänen et al. derived that a news generation system should be as belows:

  1. Transparent
  2. Accurate
  3. Modifiable and transferable to other domains
  4. Producing fluent output
  5. Based on data that is available
  6. Producing news that are topical

But where are they now? If the idea is old and the required technology exists, why is it surprisingly difficult to find examples of automatically generated news in major news publications?

According to Graefe and Bohlken, the number of media organisations listed as clients of NLG providers is rather small . Then again, they add: “…although this may have to do with reasons of commercial confidentiality.” Based on these notions, they argue that the field of automated journalism remains to be in early market expansion stage. They also anticipate that media houses might hold back with robojournalism while their readers would disapprove of automated news, could be conflictingly perceived by readers.

Graefe and Bohlken identified the following heuristics mentioned by Sundar to apply to this confrontation between human and robojournalists.

  • Authority heuristic suggests that readers should prefer human journalists, as they can be seen as a subject-matter experts.

  • Social presence heuristic suggests that readers should prefer human journalists as it feels more like interacting with a human than a machine.

  • Machine heuristic suggests that machine-generated news should be perceived as more objective than human-written ones as they do not include bias.

In a recent study, people score machine generated news lower just by knowing they are by a machine, may encourage news providers to hold back from stating that a story was reported by a robojournalist. Also, not stating the real reporter of a news report, be it a human or a robot, disrespects the requirement for transparency.

All tough the use of robojournalism might be progressing slowly, it has some clear benefits to it as mentioned by Graefe.

  1. Automatisation allows speedy publishing of news reports
  2. The scale in which reports are published can be increased
  3. Automated journalism systems are argued to be less error-prone as they do not make mistakes such as misspelling or calculation errors
  4. Automatisation allows personalisation of news report for smaller target groups — even individuals, and offering news on demand

In addition to the potential benefits, Graefe mentioned some limitations as well.

  1. An automated journalism system can only be as good as the data it uses. In other words, the availability and quality of the source data is key to success.
  2. Although the system might identify interesting events in the source data, it cannot ask the question “why?”. Thus, human validation and reasoning is still required.
  3. The algorithms lack ingenuity, and are thus limited in their ability to observe society and fulfil journalistic tasks.
  4. People prefer reading human-written rather than automated news, according to experimental evidence.

Robojournalism has many benefits to it and is already well suited for reporting repetitive events on which structured data is available. News automation allows us to report news for smaller target audiences, report same thing on multiple languages, and to personalise content–all this without creating massive additional costs. Despite the clear benefits, it is difficult to find examples of articles produced by robojournalists.


References

  1. Where are the robojournalists? by Miia Rämö (https://towardsdatascience.com/where-are-the-robojournalists-b213e475ca64)
  2. Graefe, A. (2016) Guide to automated journalism.
  3. Graefe, A., & Bohlken, N. (2020). Automated journalism: A meta-analysis of readers’ perceptions of human-written in comparison to automated news. Media and Communication, 8(3), 50–59.
  4. Sundar, S. S. (2008). The MAIN model: A heuristic approach to understanding technology effects on credibility. (pp. 73–100). MacArthur Foundation Digital Media and Learning Initiative.

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Parvej Saleh - AI Insights | Probyto - Your AI Success Partner
Probyto AI is a modern integrated platform providing management of the complete lifecycle of Artificial Intelligence (AI) based applications, starting from collaborating Ideas to scaling AI models.