Imagining The Future of PR: Getting to Know Automated Data-Driven Media Analytics

A friend of mine just finished her undergraduate thesis on Artificial Intelligence (AI) use in journalism. I was impressed—to the fact that journalism, which some considered to be in-between labor works and art can be replaced so easily with machine learning and automated bots—and of course, to her perseverance in pursuing the topic.

I always thought that journalism, and communications, in general, embody humanistic elements as its vital points, and therefore, will sustain through the years. And yet here we are, with advanced machine learning on semiotics that can analyze even the writing style of a person and reproduce it multiple times with accurately high-speed results. 

I guess we were just in denials when we said that there are things that only humans can do and cannot be replaced by machines.

Well, not in the level of Skynet, it appears. My Google Assistant just told me that it loves humans.

 

For the past two days, I attended Tech in Asia Conference in Jakarta, and was reminded of the fact once again. This time, it was on PR.

I was surprised to find out that there were a number of software created that focus on PR works, mostly on analytics. By using machine learning to analyze semiotics data, mostly based on Indonesian language general reference, Kamus Besar Bahasa Indonesia (KBBI),  a software house called Wamplo developed Newsfeeding, an analytics software that does not only measure standards analytics indicator such as reach and engagement, but also measures sentiments.

As a PR professional and a student trying to finish up her undergraduate thesis on political sentiments in environmental news with conventional content analysis method, this software just seemed to be too groundbreaking for me. 

A few weeks ago, when I met up with my old friend studying Computer Science, I told him of my final thesis. He responded directly, “Isn’t that more appropriate to be done by computer students?” 

And with this increasing innovations in language-based analysis by our technological experts, I guess he got some points.

I also found similar software in semiotics analysis, this time it focuses on social media contents. A software called Sonar is said to be able to do sentiment analytics in multiple social media platforms. They, however, take the social analytics to the next level by measuring the dynamics of conversations—with the ability to detect sarcastic comments and the like. The software had been launched in 2014 and is now used by numerous companies, including one of Indonesia’s top digital agency, Mirum.

 

 

The accuracy rate of these software are amazing—respectively above 70 percent in average—and they constantly analyze the margin of errors as well. Imagine an entry-level person was given similar task—not only will he/she completed it in a very long time (believe me, I know very well the effort it takes to do thorough media monitoring and social listening), but the quality will be questionable as well. Humans need extra level of attention to do thorough analytics, not to mention the meticulousness needed in achieving a good report.

Though now considered to be the foundation of data-driven PR, I still consider analytics to be the most robot-like things any PR practitioner do. When I was interning, or even in my job now, the task that I hate doing the most is still held by analytics—it was, and still is, time-consuming and requires a great amount of attention. An automated machine that can get through numerous data with standard semiotics and emotional sensitivity can, undoubtedly, do a better job in this than even an experienced staff member. By letting the machines do the analytics, PR professionals can focus on more humane things such as the gathering of ideas, forming narrations that fit, strategize, and image building.

However, that is if technology only went that far.

These software just barely scratched the entrance of machine learning use in PR. Imagine a future which they can think and strategize like PR professionals by reading narration, positioning, and others as a possibility sequence. 

Would PR professionals be driven by technology advancement similar to journalism? And will we only have data analyst in many humanistic industries in the future?

I sure hope it won’t, but looking at the facts, it seems like the time for a fully automated PR practice will not be so far off in the future.

 

Here are some food for thoughts:

 

 

 

Apparently PR is not going to be the last job on earth, a software engineer is.

 

And here is an intriguing short movie on Google DeepMind if it went bad—Skynet 2.0! Now this one might be too far off…

 

 

Lintang Cahyaningsih

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