Our take on LinkedIn’s 50 Big Ideas for 2019

Credits: LinkedIn

We liked this LinkedIn’s post and wanted hereafter to share our takeaways (@X refers to the Big Idea X):

The desire.

A. The desire for Trust in Businesses.

Via paying local taxes and regulations @#9. “Governments will seize the opportunity to regulate Big Tech.” and @42. “Order comes to the Wild West of data collection”

Via a clear fear of sizes getting too big “before it’s outside our control and we can’t see the consequences of it” @31. “Businesses will favor integrity over growth.”

B. The desire for Trust in exchanges.

@37. “We will reach peak outrage.

In the last couple of years, public opinion has been driven by “polarized tribes,” says Willow Bay, dean of the USC Annenberg School of Communication and Journalism: “Outrage has been modified, optimized, personalized and, of course, monetized.” Outrage, like fear, is helpful in the short term but unsustainable in the long term, she says. “Many do not want to live in a state of semi-permanent outrage, they’re simply tired of it,” she adds. “And I believe increasingly, people are going to want to reclaim consensus, collaboration and shared values rather than polarizing ones.” While Bay is referring to the United States, any country where people discuss politics on social media will recognize a version of this. She points to a study by More In Common which showed that 67% of Americans did not conform to partisan ideology or had disengaged from politics. They’ve been dubbed the “exhausted majority.””

And, @43. “We will ask ourselves hard questions about what free speech means.”, the classic “Free speech vs diversity and inclusion” on which we posted this a few days ago: “Media trust over education stages

C. The desire for Trust in Brands

@45. “Brands won’t be able to stay neutral. Consumers and employees increasingly expect companies to take a position on the day’s issues and live their values…”

This needs for Brand values to be shared and stood for is in line with the role of Brands as explained and applied by Edward Bernays.

Of course, Brands applies to media brands and thus the need to profile them to build your perimeter of trust, mandatory to feed your analytic tools and guarantee your Brand Safety. This is what TrustedOut is about.

The need.

The need for AI.

@#7. “AI will be in every industry and every job”. Of course, we agree. We are using AI to avoid human physical limitations and bias.

Another way to say it is we believe AI is an element of the desire for Trust.

The need for ethic in AI.

We are aware of the risk of a fraudulent, oriented AI. IBM launched a tool to detect bias in AI , the excellent “Weapons of Math Destruction” (PDF here) and many more… This is why transparency with our AI is key to us, we will not have human entries so everything can be explained, nothing will be editorialized, no judgment, just collections and classifications machine-driven.

This is also why we were super proud to be finalist at the recent “The Robot of the Year” event, focused on Ethic AI.

Reminder: We solely focus on media profiling and are not doing any article fact checking, nor author scoring (Question #4 in our FAQ)

 

Taxonomy DNA (cont.) – comparing a specialist vs a generalist

Following our Introduction to Taxonomy DNA, we would like here to showcase the sensitivity of our AI-operated taxonomy.

Comparing a specialist, Techcrunch, and a generalist, the New York Times – Technology.

Taxonomy DNA views: Both 12/18/18, 3% threshold, 7 day rolling learning (a post on this later on).

Techcrunch

Techcrunch – Taxonomy DNA – 12/18/18 – 3%,7d

The New York Times – Technology

the New York Times – Technology – Taxonomy DNA – 12/18/18 – 3%,7d

Top 10 categories

Interesting to watch the 4 first categories been the same with more on people for the NYT and more on Industries for Techcrunch., then NYT has Law, Politics, when Techcrunch has Finance and Hardware.

Finally, AI was pretty precise to classify Lifestyle and Digital Life for the NYT and Digital Tech for Techcrunch.

Why it matters.

TrustedOut Corpus Intelligence permits our users to create and maintain corpuses, precisely shaping out their definition of their trust for their analytics. With the example above, shall a study be on Tech AND Law, the NY Times – Technology section would be selected and not Techcrunch.

Like for any survey, the sample onto which the survey will be based on, makes or breaks the trustworthiness and the serious of its outcomes.

Trusted in, Trusted out.

Below is an example of the Corpus creation UI in TrustedOut.

The screenshot above comes from the “Country comparisons” Business Case.

 

 

Of trust, Facebook and French Yellow Vests.

In our previous post, “While distrust is general, trust definition is personal.“, we saw an increase in News reading while an increase in distrust in media and a clear split in trust between overall media and the media you read.

Here are the numbers from Reuters Institute and Oxford for France in 2018 (June):

and here are the comparable numbers for the USA:

Quickly, one can read French people pay less for online news, use more ad blockers, trust less the media they use. Matter of fact, the ratio News I use vs Overall Trust is almost 3 times less in France (only 17% more trust for Media I use”) vs the US (47% more trust for “Media I use”)

2 points are interesting in the context of the Yellow Vest in France:

French trust in overall media is increasing (+17%) while the US it’s decreasing (-11%)

Well, not for the Yellow Vests.

As written in Le Figaro (en French) “the anti-media rhetoric is a constant in the discourse of “yellow vests”” and in Le Monde (en French) “anti-media rhetoric, fuelled by press attacks against the movement’s opacity and anti-democratic nature.”

Social networks are the less trusted.

Well, not for the Yellow Vests.

But first, what is the place of Facebook in getting the news?

In America, overall Facebook IS NOT prominent.

When in France, Facebook IS prominent.

And the role of Facebook with the Yellow Vests is significant as The Verge writes “How Facebook Groups sparked a crisis in France“, including an excellent point on the new algorithm which could be linked to what Bloomberg names “France Faces a Typical Facebook Revolution

All this confirms the role of trust within media which is the fondation of TrustedOut Corpus Intelligence. For this article, I decided to trust major media sites identified with high traffic and years in business.

More on this? Country comparisons

While distrust is general, trust definition is personal.

Here are 3 interesting facts (US data): 1/ people are spending more time following the news, according to Pew Research Center, 2/ distrust in news is severe and growing with 72% believing traditional major news sources reporting news they know to be fake, or purposely misleading according to a poll from Axios and SurveyMonkey and finally 3/ Trust in news depends on which news media you mean according to the Media Insight Project.

As the content you use makes your education, your opinions and, most importantly, your decisions-making, defining your trust is mandatory. This is the foundation of TrustedOut. We call it Corpus Intelligence. First targets: the $4B spent in text analytics ($10+B by 2023) to make this intelligence trustworthy and also everyone concerns with Brand Safety to help them define precisely their trusted brand perimeters.

PS: Must read article (This chart is coming from it): ‘My’ media versus ‘the’ media: Trust in news depends on which news media you mean