New TrustedOut.com is here.

A new version of TrustedOut.com is now available!

As our technology and commercial offers continue to evolve, posts are no longer enough and we had to update our site as well.

We decided to re-organize our site in 2 groups:

Where, Why, How:
Vision, Benefits, Technology.

Our Vision is to make Trusted Content a utility, so everyone in any organization can tap into content they trust and thus, trust any decisions made  from it. More…

Our Benefits are coming from our AI-Powered Classifications which brings no-biais, always up-to-date universal expertise to profile any piece of content. This provides unmatched advantages vs keywords, such as Editorial Orientation and Evolution over time. More…

Our Technology is proprietary Machine Learning for Tailor-Made AI-Profiling. It’s all about datasets permanently updated for internal and external classifications. More…

Who:
Solutions for Business Intelligence, Business Watch, Online Ads, Publishers.

4 dedicated pages to the solutions we provide to 4 different type of customers:

For Business Intelligence Analysts: 

What we are fixing for Business Intelligence:
Cannot trust what’s out of your BI Tools if you do not trust what’s in.
How we are fixing it:
Feed your BI tools with content profile you define.

Read Solutions for Business Intelligence…

For Business Watch Users:

What we are fixing:
How to align Executives, Collaborators and Partners with Company Goals?
How we are fixing it:
Feed your RSS readers and newsletters tools with Corpuses from your BI Tools.

Read Solutions for Business Watch…

For Online Advertisers, Agencies and Publishers

1. Brand Safety: What we are fixing:
How can we make sure a Brand is totally safe?
How we are fixing it:
Build and Manage your Whitelist with your definition of what is safe for your Brand.

2. End of Cookies: What we are fixing:
How to target audience in absence of cookies?
How we are fixing it:
Grab Intelligence from the targeted article’s classifications.

3. Reporting laws: What we are fixing:
How to deal with government reporting laws such as the Avia law?
How we are fixing it:
Report Media with their profile made by TrustedOut. AI-Powered. Unbiased, Universal and always Up-to-date.

Read Solutions for Online Advertising…

For Publisher’s Executives and Editors:

What we are fixing:
How am I perceived? How do I compare? Are my Editorial efforts visible?
How we are fixing it:
Get instant Profiles on a Media, a Source and/or Articles.

Read Solutions for Publishers…

Questions? Let us know!

Must read posts.

1. Our vision:

Trusted Content as a Utility

  • Distrust in Media is a major, major issue.
  • No trust in content, No trust in decisions made from it.
  • Trusted Content should be like water or electricity: A Utility.
  • Define the content you trust for every segment of your business.

Trusted Content as a Utility

2. Our unique technology.

2.1 AI-powered classifications vs Keywords.
Editorial Orientations detection.

Going beyond a 1-Dimensional access to knowledge

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

Read the full post to see example of a single event treated in 3 different ways in 2 different countries

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

2.2 AI-powered classifications vs Keywords.
Evolution over time.

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

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

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:

Why it matters.

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

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

Questions? Ask!

AI-managed taxonomy to keep up with language evolutions.

Measuring against datasets.

We explained in the following post how our classification works:

How our AI-powered classification works.

For the taxonomy, words from articles are measured vs Classifications datasets (a.k.a. Bags of words)

To gauge how a media is perceived, we, this time, measure what is said vs Perceptions datasets


Matching and weighting are making the score of the document. This is how we know where an article belongs and its level of expertise/severity.

AI to keep datasets permanently relevant.

The perception we have on an event evolves over time. So should the classification of this article. To make it happen, we keep the datasets always up-to-date. As seen in last week post, the classification of an article evolves with time:

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

 

The evolution domino effect: Languages impact datasets, which impact taxonomy.

The way we talk about something evolves with time. In fact, the dialect of the tribe made of people concerned about something evolves over time. More specific, cooler, geekier, newer… whatever is the reason, we love changes, we love new ways to express ourselves.

Datasets updates. Permanently.

Datasets must be rebuilt and updated permanently to catch new words, new importance on words, up and down, and the disappearance of some.

Overlapping intelligence. On watch.

As datasets evolve they may make a classification overlap over another which will weaken the taxonomy.

We measure overlapping, permanently as well, and if one is detected the taxonomy evolves consequently.

Recommended (re)Read:

Trusted Content as a Utility

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

Questions? Shoot!

Economy and Enterprise – US vs France Taxonomies.

France’s Economy and Enterprise Taxonomy.

Let’s ask TrustedOut, for France, what is the Taxonomy of all media covering the “Economy and Enterprise” Classification group over the past 7 days.

Here’s the corpus query:

Showing 373 Medias and 750 sources

and the taxonomy DNA:

USA’s Economy and Enterprise Taxonomy.

Let’s ask TrustedOut, for the US, what is the Taxonomy of all media covering the “Economy and Enterprise” Classification group over the past 7 days.

Here’s the corpus query:

Showing 1,961 Medias and 3,645 sources

and the taxonomy DNA:


Comparisons:

France USA
General, 43.6% General, 41.9%
General > Economy and Enterprise, 20.2% General > Economy and Enterprise, 21.5%
General > Economy and Enterprise > Economy, 6.2%
General >  Finance, 5.2% General >  Finance, 5.4%
General >  Law, 6.5%
General > Tech, 8% General > Tech, 8%
Industries, 22.2% Industries, 19.1%
Sciences, 6.7% Sciences, 7.7%
People, 27.3% People, 31.2%
People > Culture and Arts, 7.3%
People > Sports, 6.6%
People > Lifestyle, 5.9%

How to read the table above: The percentage means how much of the classification datasets are in the publication. Ex: In the US,  media covering Economy and Enterprise have also 5.9% have words belonging to the classification Lifestyle which is part of People.

In France, media covering “Economy and Enterprise” also talk:

Deeper in Economy
Law
Culture and Arts
Sports

While, in the US, media covering “Economy and Enterprise” also talk:

Lifestyle

Fine tuning your corpus to compare apple to apple.

Want to compare countries for a product launch but do not want the Lifestyle classification in the US?

Simple add the IS NOT Lifestyle taxonomy:

Voila. Now run your analytics on those 2 corpuses or/and Get the corresponding whitelists…

Questions? Contact us!

 

 

Fine tuning your corpus to perfect analytics and build brand consistent whitelists.

Let’s have a look at some cool product updates our alpha-testers can enjoy since last night.

Demo scenario: Let say we want to create a Corpus for the US Food Market for some analytics on our brand and a new ad campaign coming right up.

1/ The broad definition. Country and Taxonomy.

Add country, select United States.

Add Taxonomy IS made of these two classifications :

  • Industry > Manufacturing and Retail > Food and Beverages
  • People > Lifestyle > Food and Beverages Services.

As you know, TrustedOut also profiles the level of expertise and the sensitivity on news for each media over the period of rolling time the taxonomy is computed. Here we want ALL levels and a taxonomy, stable, over the past rolling quarter (-90 days from today). We do recompute and update everything permanently.

We have 4,003 media for our Whitelist and 10,027 sources to feed our analytic tools with.

2/ Refining the target. Excluding a classification.

For this effort, we do not want media specialized in Food Processing, profiled over the same period of time, so we exclude it from our Corpus like this:

  • IS Industry > Manufacturing and Retail > Food and Beverages
  • IS People > Lifestyle > Food and Beverages Services.
  • IS NOT Industry > Agriculture > Food Processing

We now have 487 media and 732 sources.

3/ Hand picking media we do or do not want. By name, by URL.

(this is an example. nothing personal for those sites 🙂

From past experiences, we do not want to work with anything related to foodnavigator.com and its subsidiaries, neither do we want a site named “Food processing”. Clicking on “Get” and Scrolling through the media list TrustedOut gives me, I see they, indeed, are in the list:

Let’s remove them.

Let’s tell our corpus to add the following conditions:

  • Name DOES NOT CONTAIN “Food processing”
  • Website DOES NOT CONTAIN “foodnavigator” in its domain

Voila. 484 media and 726 sources.

Corpus is ready to feed our BI Analytic Tool and be our whitelist to imported in our DSP.

Questions? Shoot!

 

 

How our AI-powered classification works.

We receive a lot of questions about how our AI-powered Classification is working, so we decided to make 2 drawings to explain how it works.

1. The Taxonomy.
Gauging the media from the Inside.
Including expertise and sensitivity.

A media has sources which each publishes new articles. From every new article, we solely keep useful words (no stop words such as “the, is, but…”) which we call an “abstract”.

Every single work of this new abstract is matched vs several hundreds of datasets. Every single classification in our taxonomy has its own dataset.

Each 1 million abstracts, this means 75,000,000,000 operations.

This method is close to the tribe model. Every tribe uses a dialect made of words signing this dialect. When you recognize a dialect, you recognize a tribe. Here is a classification. Depending on the number and the weight of words, we are able to gauge the level of expertise in the classification. This gives us a score for the article.

Playing with the length of past days, we can also gauge the sensitivity to news for the sources. Compiling sources tells us where the media stands.

Bottom line: We have a universal taxonomy, always updated and able to be filtered by expertise level and over 3 periods of time to sense time sensitivity (and trend forming, but we’ll tell you more real soon)

2. The Perceptions.
Gauging the media from the Outside.
How is a Media “spotted as”.

“Fake news”, “Junk science”, and other toxic appreciations are tangible. Rarely, can this be intangible (for sure), because those are up to appreciations. What is “fake news” to some people, is not for others. This is why we treat those appreciations as “spotted as”, or “perceived on the internet as”.

Same as for our taxonomy, perception is an appreciation, but where taxonomy is about the publisher itself, we call it the Publisher Inside, the perception is the Publisher Outside, or how the publisher is perceived for those terms.

To do this, we collect how the publisher is perceived on the Internet, strictly excluding any publisher properties.

This gives us pages and words which we match with Perception datasets (one for “fake news”, one for “junk science” and so on) in a similar way explained above.

We then have a score which when above a threshold make the publication “spotted as fake news” for example.

Bottom line: We sense how a publication is perceived as not one person or group can make any universal statement.

Questions? Shoot!

 

Adding News-Sensitivity in our Taxonomy.

Taxonomy classification over different periods for CNN Politics.

Brand Safety 2.0 is about brand values.

As we wrote in our previous post: “Brand Safety 1.0 was about toxic keywords, 2.0 adds brand values.”

Brand values are tangible. So must be media profiling.

To gauge brand values, which are made of tangible perceptions, the matching publisher brands must be profiled with content classification, using AI to be unbiased, universal and always up-to-date.

Our AI-based Taxonomy and massive data processing already allow universal taxonomy AND expertise depth…

We’ve presented, in previous posts, our universal taxonomy and its DNA view: “Media profiles are key to Business Intelligence and Advertising.”

… announcing today, new-sensitivity in taxonomy!

Playing with periods of time in the past, past week, past month, past quarter, we are now able to classify accordingly our classification and thus, here, our taxonomy.

In other words, depending on the marketeer project and brand values, TrustedOut will be able to deliver news-sensitive or stable media.

No UI yet, but we couldn’t keep this for ourselves, here’s how CNN – Politics looks like over past week, month and quarter.

A quick read is:

International:

… disappears from top 5 over the period of time of the past quarter (-90 days). This might be due Iran and trade war/mexico stuff. Depth is also getting lower with time.

Political party:

… goes lower with time.

Defense gets civil over time and Education and LGBTQ are very news sensitive. Disappear over time.

New UI and new killer feature coming up…

We will include news-sensibility in our Corpus definition and, teasing again, we’ll reveal a killer features using this brand new and unique capability,

Stay tuned.

Feel free to reach out if you have a question!

 

 

 

 

 

 

TrustedOut in 1-page.

1-pager are very popular. Everything in 1 page. Here’s ours:

TrustedOut:
A database of AI-profiled Media.

”For analytics and brand safety,
what’s not Trusted In, can not be Trusted Out.”

Questions? Shoot!

New demo page showcasing TrustedOut and BI, Ads and PR

A new demo page has been added to TrustedOut.com

The scenario

 

ACME is a sport car maker launching a new model extensively using Artificial Intelligence (AI). ACME has 2 main countries, US and France and wonder what market to test first.

1. Corpus Intelligence for Business Intelligence: Market selection.

New corpus, the CMO (or Marketing Manager) defines 3 conditions to be necessary.
a. Where are the publications? We said France and the United States
b. What should these publications be about? ACME wants to grab how AI is perceived from publications covering Politics, for regulations, Law, for any legal aspects, Tech, to gauge technology used and perceptions and, of course, Transportation, for anything car related.
c. Want to be safe from any toxic content? Of course, no fake new and no junk science TrustedOut classification knows how gauge the expertise level of a source and how sensitive to the news the taxonomy should be. At this stage, we want generalist publications by setting the expertise level to “Covered” Here is the corresponding query for our Corpus, which we are going to name “ACME AI in new model”.

Go to the demo page >

2. Corpus Intelligence for Brand Safety & Campaigns. White listing.

ACME’s CMO wants to check if Pure Player Media (media only available online) is a good target. After all, Pure Players should be more reactive and not having to sync print, for example, that can be daily, weekly or monthly, with immediate online publishing. Let’s go back to TrustedOut and change the Corpus as follow: a. Where are the publications? We now want to limit to France. b. Select Pure Players? We want media where “out of digital” is set to None to only get those not publishing on any other support.

Go to the demo page >

3. Corpus Intelligence for Coverage & Content Analytics. PR campaigning.

Digimind gives us the key concepts to write our Press Release: European Union/Commission and Neuronal Networks. With the Corpus we have what publications to target, with those key concepts we have how to write a Press Release that will interest those targets.

Go to the demo page >

Questions? Shoot!

Deck and demo from our 1st public event: TrustedOut+Digimind.

It was this Thursday morning and it was great. It was our first public presentation and it was great to partner with Digimind to show why TrustedOut can make Intelligence smarter and trustworthy. Merci Aurelien and Valentin.

The deck. TrustedOut.com/Digimind

Deck is in english. If you have question, let us know with the form below.

The demo. Step by step.

The scenario

ACME is a sport car maker launching a new model extensively using Artificial Intelligence (AI). ACME has 2 main countries, US and France and wonder what market to test first.

Step 1. Corpus Creation for country comparison.

New corpus, the CMO (or Marketing Manager) defines 3 conditions to be necessary.

a. Where are the publications? We said France and the United States
b. What should these publications be about? ACME wants to grab how AI is perceived from publications covering Politics, for regulations, Law, for any legal aspects, Tech, to gauge technology used and perceptions and, of course, Transportation, for anything car related.
c. Want to be safe from any toxic content? Of course, no fake new and no junk science

TrustedOut classification knows how gauge the expertise level of a source and how sensitive to the news the taxonomy should be.

At this stage, we want generalist publications by setting the expertise level to “Covered”

Here is the corresponding query for our Corpus, which we are going to name “ACME AI in new model”.

Once ready, “Save” will show us how many media and sources our Corpus will include…

… and the Taxonomy of your Corpus.

Let’s now connect your Corpus to Digimind to get Social Intelligence from your Corpus. Process is simple, click on “Get” and, instead of “Downloading” a csv or json file with all media and sources, which will not be up dated at all time, click on Connect and pick Digimind.

Your “ACME AI in new model” Corpus is now live and accessible for any projects related to this corpus definition. TrustedOut will continue to update it, all the time, with relevant media and sources.

Digimind collects content from those media sources, so no need to also connect “article abstracts” with Digimind.

Step 2. Comparing countries on AI.

As the Corpus is immediately available and up to date in Digimind, we can read the following top concepts in both countries about AI.

ACME is very sensitive to ethic in AI, so consequently pick France as the first country to test its new model to handle this ethic topic super carefully.

Step 3. Best media profiles for ad campaigns.

ACME’s CMO wants to check if Pure Player Media (media only available online) is a good target. After all, Pure Players should be more reactive and not having to sync print, for example, that can be daily, weekly or monthly, with immediate online publishing.

Let’s go back to TrustedOut and change the Corpus as follow:

a. Where are the publications? We now want to limit to France.
b. Select Pure Players? We want media where “out of digital” is set to None to only get those not publishing on any other support.

“Save”. And now we get these amounts

Step 4. The perfect mix ethic and Business for a 1st ad campaign.

While France is more “Ethic” on AI, Pure Players are more Business Oriented vs all. ACME CMO is seeing the growth from 37% (all media) to 45% (Pure Players) in business for this selection of media as the perfect vehicle to test an ethic message onto business oriented people.

Step 5. Talk to the talk in AI.

Now, ACME wants to launch its first Press Release and wants to address first the geek, very technical community.

Let’s go back to TrustedOut and make the following changes:

a. What should these publications be about? We now want only Tech and Transportation publications
b. How expert? Dedicated.

… and, of course, more specialized pubs means less as a total:

Step 6. Key concepts for an optimal PR campaign.

Digimind gives us the key concepts to write our Press Release: European Union/Commission and Neuronal Networks.

With the Corpus we have what publications to target, with those key concepts we have how to write a Press Release that will interest those targets.

Bottom line:
TrustedOut+Digimind = Market selection, Optimal ad budgets and Perfect PR.

Questions? Shoot!