Comparing humorous/satirical sites in the US vs France (update: 4/2021 vs 8/2020)

For today’s post, we decided to compare editorial focuses for media spotted as humorous/satirical vs August last year.


  • Politics replaces Sports
  • Lifestyle is lower, Education is higher


  • Society is now #1, Politics #2
  • Lifestyle is lower.
  • Still high on Sciences (vs US)
  • Life and Biological Sciences now in Top 6. Economy and Enterprise is out.

Aug 24th 2020 Post:

Editorial focuses of sites spotted as Humorous/Satirical:

Above are the Editorial Radars for the sites spotted as humorous or satirical in the US and in France.

Below are the weighted editorial focus (above 1% and as of August 17th)

Quick points:


    • All about People in the USA.
    • Wider in France with People, General and Sciences.


    • More masculine than feminine in the US
    • Masculine, Feminine, Home higher in the US
    • Child and Teen and Youth in France

Culture and Arts:

    • 2x more in the US

Entertainment and Leisure:

    • High and only in the US


    • Political Engagement, way higher in France.
    • Religion present and equivalent in both countries.

Politics, Economy and Enterprise:

    • In line with Society > Political Engagement, Politics is High and only in France.
    • Union high in Economy and Enterprise in France.

Education, Sports:

    • Both only in the US.


    • Only in France.

Why it matters:

Understanding how humor and satire is played out matters for your communication campaigns to get the perfect messages at the perfect place.

Want to know more? Let us know!



AI-powered classifications vs Keywords. Part 2/2: Evolution over time.

For content selection: AI-powered classifications can sense Editorial Orientations AND Evolution over time. Keywords cannot.

For years, access to knowledge was all about the presence or absence of keywords to trigger the selection of content: A 1-dimensional access, keywords based, to knowledge. Linear. Limited to 0 (absent) or 1 (present).

Last week, we covered the first advantage of AI-Powered Classifications vs keywords based selection, Editorial Orientations, and showed how the same event, on 3 different publications can have different Editorial Orientations.

This is an additional dimension to access knowledge.

Read postLet’s now have a look at a 3rd dimension: Sensitivity over time.

Perception of an event evolves with time, so do our AI-Powered classifications.

France has been through a lot of social movements with the pension reform the French government is pushing for.

From the beginning of the protests until now, the perception has evolved.

Let’s look at the same article and how AI classifies it at two different times.

This article was published on Dec 10th 2019:

Pension reform: “It would be a misdiagnosis to talk about minced runs.”  [google Translation] (Réforme des retraites: “Ne parler que de parcours hachés serait une erreur de diagnostic”)

On Dec 10th, top classification was:

We are at the beginning of the movement, Employment and Unemployment is the top classification.

On Dec 31st, top classifications are now:

3 weeks later, the very same article with the very same content is classified as Senior first, then Social Assistance and, now in 3rd, Employment and Unemployment

Clearly, after 3 weeks of protests, Aging and Social are topping the Employment dimension.

How can AI-Powered Classification do this?

In a previous post, we explained how our AI worked:

How our AI-powered classification works.

Every new article is classified as follow:

Which means the day the article is published, we use Classifications Datasets (aka bags of words) on that very day.

Classification Datasets are also updated to sync with every single classification and sense the depth of expertise over time. This means some words can be in and out and with a different weight over time. This means classifications are set, by default, for the day an article is published but can be re-run on a different day and produce a different classification. Like in real life, your perception of something evolves with time.

Why it matters.

Simply because time is a vital dimension of perception.

Simply relying on the presence of keywords to select content for analytics, expose your brand via advertising etc… is dangerous.

What’s true at publication time might not be at analytics time, or advertising time…

In the example above, you may or may not want articles about “Seniors”. At publication time, the article was under the radar, 3 weeks later it is classified as “Seniors”. Is it still where your brand wants to be exposed? are those content the one you want to analyze today? do those articles matter for the education of your teams?

Relying on keywords that are present in content forever, not only does not give you the orientation of the content but is not sensitive to the evolution of perception. And as we know, in Marketing:

Perception is reality.

Questions? Ask!


AI-powered classifications vs Keywords. Part 1/2: Editorial Orientations detection.[updated]

Going beyond a 1-dimensional access to knowledge.

For years, access to knowledge has been ruled by keywords presence. Search engines, corpus selection for business intelligence, DSPs for online advertising, Brand Safety, Watch alerts…

All is about the presence or absence of keywords to trigger the selection of content: A 1-dimensional access, keywords based, to knowledge. Linear. Limited to 0 (absent) or 1 (present).

Keywords presence does not sense angles, subtlety and orientations taken by the author (nor the sensitivity over time. Today’s meanings are the same any other day).

For example, the presence of “Christmas gift” might be “ok” but is it in a context of “Military defense” and “Weapon”? Can you maintain queries excluding all related, always evolving dictionaries of synonyms and be sure your brand won’t be exposed?

After all, a word can have several meanings depending on its context and the time it is read. AI-Powered Classifications are the solution:

AI-Powered Classifications are adding 2 more dimensions: Editorial orientations and timing context.

Today, we will focus on editorial orientations detection.

Next week, we will explain the sensitivity to the time of publication.

[update] The second part is now published:

AI-powered classifications vs Keywords. Part 2/2: Evolution over time.

Both, AI-Powered Classifications and keywords based selections are unbiased, universal and up-to-date. Because TrustedOut is AI-Powered, our machine learning guarantees the same non-humain, machine powered benefits.

Editorial Orientations Example:
1 event, 2 countries, 3 articles, 10 classifications.

The event: let’s take North Korea announcing a special Gift to the US.

The 2 countries: We then selected 3 articles from a Google Search on “North Korea Gift” for the US and “Coree du Nord Cadeau” for France.

The 3 articles: we randomly picked USAToday, CBSNews and Le Figaro.

Here are the top 10 classifications TrustedOut came up with. For each we’ve added how the media is spotted for its Political Orientations (beta)


Vase or missiles? US awaits Christmas ‘gift’ from North Korea’s Kim

1 General › Politics › Diplomacy
2 General › Politics › International
3 Industries › Aerospace And Defense › Weapon
4 General › Politics › Military Defense
5 General › Politics › Civil Defense
6 Industries › Energy › Nuclear Power
7 Industries › Aerospace And Defense › Naval System
8 General › Politics › Administration
9 Industries › Aerospace And Defense › Aerospace Systems
10 General › Politics › Government


No sign of “Christmas gift” from North Korea yet, but deadline looms

1 General › Politics › Military Defense
2 Industries › Aerospace And Defense › Weapon
3 General › Politics › Diplomacy
4 General › Politics › International
5 Industries › Aerospace And Defense › Naval System
6 Industries › Aerospace And Defense › Aerospace Systems
7 Industries › Aerospace And Defense › Missiles And Rockets
8 Industries › Energy › Nuclear Power
9 Industries › Aerospace And Defense › Satellite
10 Industries › Transportation › Ship

Le Figaro

Trump is hoping for a “nice vase” instead of a North Korean missile for Christmas. (Trump espère un «beau vase» au lieu d’un missile nord-coréen pour Noël)

1 General › Politics › Diplomacy
2 Industries › Aerospace And Defense › Weapon
3 Industries › Aerospace And Defense › Aerospace Systems
4 Industries › Aerospace And Defense › Missiles And Rockets
5 General › Politics › International
6 General › Politics › Military Defense
7 People › Society › Opinion And Idea
8 Industries › Aerospace And Defense › Satellite
9 General › Law › International
10 Industries › Aerospace And Defense › Aircraft

Editorial Angles

Here’s a summary of the classifications for the 3 articles:

A few remarks:

  • USAToday and Le Figaro top classification is Diplomacy. CBSNews is Military Defense

  • The 2 US articles have the same top 4. (in a different order)

  • Le Figaro does not have Nuclear Power in its Top 10

  • All have Military Defense. Only USAToday has Civil Defense

  • All have Aerospace and Defense > Weapon in their top 3

  • Only Le Figaro has Society > Opinion and Idea and Law > International in its top 10

  • For Industry > Aerospace and Defense, USAToday has 3, CBSNews has 4, Le Figaro has 5 out of their Top 10.

Here’s how TrustedOut saw the Aerospace and Defense Industry, back in October:

Corpus Intelligence for an Industry: Aerospace & Defense – October 2019

Next: Evolution over time.

How AI-Powered Classifications are sensitive to the time of publication: Meaning, Classifications evolve with the time as our “bag of words” are permanently updated and why it matters… Continue to part 2/2

Questions? Ask!

Lessons for growing publisher revenue by removing 3rd party tracking*

*Source: Brave

The end of 3rd party cookies

On January 14th of this year, Google wrote: Building a more private web: A path towards making third party cookies obsolete

How personal data are “broadcast”.

We highly recommend this document, “Behavioural advertising and personal data”, from Dr Johnny Ryan, where we can read:

“…every time a person loads a page on a website that uses real-time bidding advertising, personal data about them are broadcast to tens – or hundreds – of companies. Here is a sample of the personal data broadcast.

●  What you are reading or watching
●  Your location (OpenRTB also includes full IP address)
●  Description of your device
●  Unique tracking ID or a “cookie match” to allow advertising technology companies to try to identify you the next time you are seen, so that a long-term profile can be built or consolidated with offline data about you
●  Your IP address (depending on the version of “RTB” system)
●  Data broker segment ID, if available. This could denote things like your income bracket, age and gender, habits, social media influence, ethnicity, sexual orientation, religion, political leaning, etc. (depending on the version of “RTB” system)”

“We used to read the newspaper, now the news reads us.”

This quote from the Global Editors Network. We strongly encourage you to read the article using the quote as a title and try the section “What happens when you read an article online”. Below is a screenshot for

1 out of 5 happy for their data to be shared (UK, 2017)

In 2017, GFK was commissioned by IAB Europe (the AdTech industry’s own trade body) to survey 11,000 people across the EU about their attitudes to online media and advertising. GFK reported that only “20% would be happy for their data to be shared with third parties for advertising purposes”. [source]

Finding#1: Removing 3rd party tracking/AdTech and investing in Context increases revenue!

The first chart and the chart below are from the article from, “lessons for growing publisher revenue by removing 3rd party tracking” both demoing the revenue increase is attributable to removing 3rd party tracking and adtech.

NPO and its sales house, Ster, invested in contextual targeting and testing, and produced vast sales increases even with sites that do not appear to dominate their categories.

The Covid-19 market shock shifted the market from video to display

Finding#2: “legitimate publishers of all size can increase revenue”. The New York Times example…

On their site,, they wrote: “As of April 2019, we [The New York Times] removed all third-party data controllers from our homepage, section fronts and articles. … This reduced the amount of data we shared with third-party data controllers by over 90 percent. We are working on ways to improve this number…”

Finding#3. “Context is powerful.”

“NPO properties now provide no geotagging, no frequency capping, and no cross device measurement. Despite the absence of these features, extensive testing with advertisers has proven that the ads are effective, and advertisers are spending more with NPO than before.”

Next read:

TrustedOut partners with Xandr to bring new intelligence in targeting capabilities

Introducing the Brand Safety Report

The game is rigged: A former marketer shows you how Big Tech’s advertising practices harm us all

You have questions? Let us know!


Building a Corpus and Getting relevant articles from a list of articles.

Last week we demoed how, from a list of URLs, you can optimize your communication! This week, we will show you:

How to create a Corpus to feed your analytics tools and get more relevant articles from your selection of articles.

In other words: How can I get more of those articles I find relevant for my analytical/survey/study project?

Like last week, we will preserve the confidentiality of those involved in this real business case by not revealing names or original articles.

1/ The case: A study about a special look at Sports

Client is on a study about some specific aspects of Sports and gave us a list of few articles found interesting to explore.

They need more of those articles and, ultimately, feed their analytics tools with a Corpus made and always up-to-date with relevant sources.

2/ Learning from the classifications of those articles

As mentioned above, we will not share those articles to preserve the confidentiality of the client.

Here are the top, weighted classifications from the articles list:

3/ Creating a Corpus from those classifications

Client told us Sports was the target, so we’ll ask TrustedOut for Sources specialized in all Sports.

And will add the condition that those sources are covering one or more of the top classifications found above: Fashion, Communication and/or Digital Life.

Also, client wants to use the articles he gaves us found in France to explore a new market: the US.

From a list of french articles to a US-France Corpus

Mouse over to zoom. Click to full screen

TrustedOut returns 59 Medias, 96 sources representing an average close to 250 articles per day.

Here are 3 examples of sources found for this Corpus and their respective main profiles over the past week


  • People › Sports › Football And Soccer | 31.8%
  • People › Lifestyle › Fashion | 21.8%
  • People › Lifestyle › Luxury | 17.2%
  • People › Sports › American Football |7.0%
  • People › Sports › Cycling | 5.9%


  • People › Lifestyle › Fashion | 14.6%
  • People › Entertainment And Leisure › Celebrities | 11.3%
  • People › Culture And Arts › Music | 10.9%
  • People › Culture And Arts › Movies | 4.9%
  • People › Entertainment And Leisure › TV And Video And WebTV | 4.0%

Highlights Football

  • People › Sports › Football And Soccer | 31.5%
  • People › Sports › Table Tennis | 19.6%
  • General › Tech › Software And OS | 12.8%
  • People › Sports › Basketball | 11.3%
  • General › Tech › Digital Life | 10.5%

4/ Reading targeted articles

Let’s get the latest articles from our Corpus.

Below is what the beginning of the list of those articles looks like with URLs, time stamps and classifications for each relevant article.

Fashion classified articles?

Fashion, as seen above, was the top classification found from the list of articles that were given to us.

How about getting articles from our Corpus classified in Fashion?

Simply select this classification in the list of articles coming from your TrustedOut Corpus! Here are the first 2:

Want to read them?

Les maillots de gardiens 2020-2021 d’Umbro s’inspirent des annees 90

Best Outdoor Gear Deals of the Week | GearJunkie

Why it’s so critical?

The Corpus makes or breaks any analytics.

No matter how smart your analytics algorithm is, if you feed it with too few, too biased, too outdated, too broad… not only will you get twisted results from your genius algos but, worst, decisions made from it will be wrong and untrustworthy.

Trust your Corpus to Trust your Decisions.

We’ve shared 2 ways to build a trustworthy Corpus:

Criteria-based Corpus creation:

TrustedOut was made to get content corresponding to profiles you trust for a specific purpose.

Example-based Corpus creation:

These two last posts demoed how you can get more from a list of articles/URL.

Questions? Reach out!


Editorial Intelligence to find your communication targets.

The case: How, from a list of URLs, can I optimize my communication?

To preserve the confidentiality of those involved in this real business case, we will not share any name or URLs

In our exemple hereafter, the client, ACME Co., has compiled a list of article URLs related to its brand. We could use this list as is but ACME is also scoring every article with a popularity number made of mix of likes, retweets, comments…

All in all, we start with a list of URLs compiled by a client, with or without scores.

1. Editorial profiling for each URL.

For each and every URLs, TrustedOut returns the top editorial classifications.

2. Classifications/score weighting and taxonomy consolidation.

Per article, classification split and scores are weighted. Then, to align with the taxonomy, the 3 hierarchical layers are consolidated:

3. Tree Mapping learning.

We use Tree Mapping to get a visual of the table above.

TrustedOut Editorial Tree Mapping
Click to full screen

Here are some key learning:

3.1 People #1. Priority to Political Engagement

People is the biggest classification branch and Political Engagement should definitely be a priority. For campaigns, PR and watch.

Interesting, as well, are 2 related classifications: Series in Culture and Arts and TV/Videos/WebTV in Entertainment and Leisure.

3.2 Then, is Politics. 3 major classifications echoing People’s Political Engagement.

Very interesting, to see, way above, Political Engagement in People first and then 3 classifications of the same stem, Politics from the General branch.

Public Services, Civil Defense and Government are totaling 38,500, that’s almost 95% of the People’s Political Engagement (40,800).

It becomes easy, to orient your communication to resonate on this insight.

3.3 Sciences is all about Medicine and Health. Don’t miss Pharmaceutical and Drugs.

Same stem, Medicine and Health, 3 classifications with 2 clear split in importance: Pharmacy and Drugs and then, Care and Fluid.

Clearly, Pharmacy and Drugs, bigger than each Politics and half of the top notch Political Engagement, should be a focus.

3.4 Industries: All about Healthcare. 2 top classifications.

Interesting to see top 2 are, by far, Hospital and Clinic and Pharmaceuticals, both in Healthcare. 3rd and far behind is Manufacturing and Retail > Tobacco.

Interesting insight as well is to see the Industries > Medicine and Health > Pharmaceutical being half of Sciences.

Sciences first, then Industries helps with the agenda of your communication and branding efforts.

Bottom line: Focus on People and Sciences, knowing what to talk about for each.

Use the following insights for your communication, ad campaign, PR effort, Internal/External engagements…

> Tone: People and Sciences first.

> Address People’ Political Engagement knowing the 3 matters in Politics

> Approach Sciences’ Pharmaceutical and Drugs and develop on Healthcare.

> Have an eye on popular series and videos

Intrigued? Reach out!

TrustedOut’s Ad Campaign Curation: Simple, Safe and Permanently Updated.

Your Corpus is ready? So is your Curation for your DSP.

Short post today, as the process of connecting your curation made with a TrustedOut Corpus is ridiculously simple!

3 clicks…

Click #1: Get Media and Sources (as shown on the picture above)

Click #2: Connect

Click #3: Select Xandr (formerly AppNexus (Read Partnership announcement))

… and get a Deal ID ready for your DSP!

Insert this Deal ID in your DSP and let TrustedOut feed your campaign with Content you’ve selected.

Simple, Safe and Permanently Updated.

Simple as above reviewed the 3 click process.

Safe and Premium as demonstrated in this post: TrustedOut for Xandr: First results

Permanently Updated for 2 reasons:

1/ You can at all time go back to your Corpus, make changes and a click on “Save” will update the curation

2/ TrustedOut permanently updates its profiling, so if media are added or removed from your Corpus, they are also updated in your campaign.

Want to run a test?



Semantic BI and AdTech Business cases

Improve Your Problem Solving: 7 Skills to Tackle Issues -
(credit: ProjectManager)

Hereafter is a quick compilation of business cases solved with TrustedOut

Semantic BI:

Want to get and keep on receiving content similar to a list of articles you like?…

Building a Corpus and Getting relevant articles from a list of articles.

… or from a list coming from your watch tool or here, for demo purpose, “popular on Facebook”?

Create a corpus from a list of articles (ex. here: popular on Facebook).

Want to gauge the impact of a topic on others and compare between countries?

How Health is covered depending on Political Orientations. US vs France.

Want to know what one topic also covers?

Media covering Preventive Medicine also talk about…

Want to sense editorial trends over a specific period of time?

Editorial Trends during Coronavirus – USA Today, WSJ, Miami Herald, Le Monde, Le Figaro, Ouest-France.


How to best curate where your brand will appear to keep it consistent?

3 curation methods to ensure your brand is safe and visible.

Want to get off unmanageable, outdated and potentially biased black or white lists of keywords?

AI-powered classifications vs Keywords. Part 1/2: Editorial Orientations detection.

Want to see the difference TrustedOut makes?

A/B test: Ad campaign curation comparison. With/Without TrustedOut.

How to address a market?

Helping Brand Managers address the Football lovers market

Want the curation to be at the URL level?

Introducing URL Curation. Feeding BI and AdTech with context they need.

Questions? Let us know!


Content is all about Trust. Trust is Personal.

Credits: Gallup/Knight

“Bias in Others’ News a Greater Concern Than Bias in Own News”

This article from Gallup (illustration above) confirms what we wrote in December 2018

While distrust is general, trust definition is personal.

Bias perception is getting worse

In the highly recommended “Knight Foundation: American Views 2020: Trust, Media and Democracy“, subtitled “A deepening divide”, we can read:

“Nearly three-quarters of Americans say they see too much bias in the reporting of news that is supposed to be objective as “a major problem” (73%), up from 65% in the 2017 study.”

Bottom line: Use Content you Trust to make decisions you gonna trust.

TrustedOut is all about defining the content you trust and getting it to ensure total brand safety, maximize ROI, feed your analytics tools, keep your organization up to date…

Questions? Let us know!



Contextual relevance is key to customers. Profiling is key to Contextual relevance.

Credits IAS

Ads must be relevant to content.

In a report we encourage to read, IAS explores the power of context on consumer perception, we can read:

“Contextual relevance is preferred across all verticals

When shown articles representing different verticals, consumers were consistent: they always preferred contextual relevance. Across the board, consumers paired the advertisements they prefer with articles categorized in the same content vertical.”

The picture above shows the majority of consumers prefers to have ads relevant to the content where they are inserted.

It does make sense to avoid any opposition or distraction from the content.

Profiling makes content relevant.

TrustedOut’s Holistic Profiling works like this:

Which means, not only the content where the ads will be inserted is classified and gauged in expertise but the Perception and the Orientation of the Media of insertion are also gauged.

Ex: How it applies to Entertainment:

No more unmanageable, biased, irrelevant over time keywords

With TrustedOut, Classifications in our taxonomy define a Vertical.

For Entertainment, for example, brand classification “Entertainment & Leisure” comes to mind. But then, why not Information and Communication with its Motion pictures, Online Media, etc… and then why not Culture and Arts with its Arts, Comics, Dance… and then what about content about Eating and Drinking?…

Geo: USA, As of 2020/08/28

Why it matters?

No dependance of unmanageable, irrelevant over time lists of keywords.

TrustedOut qualifies every piece of content at the moment of use. Expressions and their weight are permanently updated.

An amazing opportunity for greater context relevancy.

In our example above, adjust ad messages to the type of Entertainment. Greater context relevancy, greater approval from the customer!

Relevant… and safe!

Now that context is relevant, but…
… is the publisher of this content spotted as Fake News, Junk Science, Conspiracy Theory, Revisionism or Hate News?
… is the publisher politically oriented? Religiously oriented? Humorous/Satirical?

Say you are looking for Entertainment in the largest sense as shown above but you don’t want publishers spotted with toxic content, not far right or far left and not humorous/satirical. No filter on Religion.

Your ad campaign will run within this corpus:

Related read:

TrustedOut partners with Xandr to bring new intelligence in targeting capabilities

TrustedOut for Xandr: First results

Questions? Let us know!