Time for journalists to acknowledge that they write from a set of values, not simply from a disinterested effort at truth?

As Columbia Journalism Review puts it, “one gets the sense that the pitch of anti-press sentiment is now the most fevered it’s been since the founding of the republic. In fact, presidents from George Washington on, including Thomas Jefferson… judged newspapers to be full of lies. “. Sounds familiar?

Credits: cjr.org

Readers responsibility to discern for themselves the difference between what can be trusted as factual and reporter’s judgment. 

“… the old days of ritually objective news reporting (he said/she said) are not gone but have been reduced in importance from the 1970s on, as mainstream outlets have increasingly emphasized analysis in news coverage—not quite so much “who, what, when, where” as “why.” There has been a profound cultural shift in journalism during this period. The limitations of straitjacketed objectivity came to be understood and journalism began to embrace the necessity of interpretation… In the face of the severe economic problems afflicting daily newspapers, leading metro dailies have continued, whenever possible, to pursue aggressive, analytical journalism. This places great responsibility on readers to discern for themselves the difference between what can be trusted as factual and what represents the reporter’s judgment—a judgment that, however conscientious, goes beyond documented facts.”

“It may also be time for journalists to acknowledge that they write from a set of values, not simply from a disinterested effort at truth.”

“This will not be easy, since journalists have spent decades denying that their personal values have anything to do with their news reporting.”

Trust is personal. Personal is Trust.

“Tom Rosenstiel, the executive director of the American Press Institute, told her that for many people, “there’s ‘the media’ (bad) and there’s ‘my media’ (fairly good).” Likewise, he noted, people have little faith in Congress but think their own local representatives are okay.

Sounds familiar? Yep. We wrote about this…

developed in this post: While distrust is general, trust definition is personal.

Bottom line: Media should strengthen their brand values with the upmost  transparency to increase Reader’s trust.

Questions? Shoot!

TrustedOut AI-Operated Classification

Data Collection and Content Classification.

Our database of Media profiles has 2 distinct jobs. Collecting intangible data, like revenue, ownership, years online…) and Classifying content for our taxonomy and how sites are “spotted as” (like “fake news”, “junk science”…)

Data Collection is a multi-references, cross checking and evolution watch crawling exercise when…

Content Classification is all about Machine Learning.

And all about “bags of words”. For every classification job, we build datasets made of words onto which the frequency of occurence is used to train a classifier.

As mentioned above, we have 2 types of Classification: Taxonomy and “spotted as”.

Taxonomy Classification.

As in the graphic above, every articles is matched against our taxonomy datasets so we can classify each and every article. This gives us a clear picture of a feed, and thus, the whole media.

This, of course, makes a (big) lot of operations: 75,000 per article. Yes, 75 Billions ops per million of articles daily.

Taxonomy fun facts (as of today!)

Taxonomy DNA

Hereafter is the visualization of the New York Times, Tech section’s DNA.

Sensitiveness and depth customization. Tailor-made for the analyst.

Datasets used to classify articles can use a customized buffer of time for those datasets and thus, manage how sensitive to daily news the taxonomy will be. In addition, cliffs can also be customized to select a depth of expertise, from “dedicated” to “covered” or even “all sounds”. Both combined, plus the “always up-to-date” factor, makes our taxonomy perfectly tailor-made for the job the analyst wants to run. Reason why we use “Corpus Intelligence” as our tagline.

Enterprise mapping.

We can also link our taxonomy to our Enterprise Client’s taxonomy, so Corpus Intelligence can use the client’s business environment, (We’ll cover this in a dedicated post later. If you can’t wait, ask using the form below)

“Spotted as” Classification.

Point of being AI-Operated is we do not have any emotion or opinion. Everything is made for our client to define what they truly need and trust for content.

TrustedOut does not score nor judge anything or anyone. In addition, notions like “fake news” is not as cristal clear as people may think. The “Media, Trust and Democracy report” says it perfectly in its introduction: “Concern about “fake news” is high, but we can’t agree on what that means.”

A vivid picture on how a Media is “spotted as”.

As, TrustedOut profiles Media and their brand values, we have developed a sophisticated way to classify how a Media is “spotted”. In other words, we do not score or judge, we tell you if a Media is “spotted as” a fake news publication, for example.

In addition, the way a Media is “spotted as” varies over time. Some are getting worse, some are just revivals of previously shutdown ones, some are, of course, fixed and improved. This is why it’s mandatory to keep an always updated classification. And consequently, have your Corpus of documents always up-to-date.

Works with any terms. Bad or good.

“Fake news” is always the first coming to mind, then all toxic or suspicious terms like “Extreme bias”, “Junk Science”… but it can also works perfectly for neutral or positive terms, like “Visionary”, “Optimistic”… This opens doors to Enterprise-wide personalization.

Questions? Shoot!

 

 

 

 

 

In California, “Cannabis industry more trusted than Social Media”.

Our takeaways from this recommended Wired article:

“58 percent of Californians think the tech industry should be “more regulated,” up from 46 percent in 2018. An even larger group, 68 percent think the tech industry has been “under-regulated” rather than “over-regulated,” up from 62 percent in 2018 and about 59 percent in 2017.”

Level of trust in marijuana dispensaries and growers—44% and 43%. Trust in social media—33%.

Credits: Edelman

Failure to protect data and lack of privacy.

“Among employees, privacy and security were the top worries. Of 11 possible concerns about the tech industry—from increasing housing costs and income inequality to a possible tech bubble collapse—57 percent of workers said their primary concern was “failure to protect from data security threats,” tied with “lack of privacy/my data is shared too much.””

Gonna be ok.

“For “tech” as a whole, 61 percent of respondents said they had a high level of trust that the industry would do what’s right. For “startup companies” and “the sharing economy,” the figures were similar to the pot industry—47 percent of respondents said they trusted companies in those sectors to do the right thing.”

High expectations for an outsize impact.

“Sixty-seven percent of respondents said tech leaders should be doing more to improve California. Given the industry’s outsize impact, 81 percent said tech should do more to improve local issues, up from 75 percent in 2018 and 76 percent said tech leaders are obligated to do more on societal issues, up from 71 percent last year.”

Get information from traditional Media, Conversation on Social Media…

As we wrote:

Get information from Traditional Media, have conversation on Social Media. Not the other way around.

Questions? Shoot!

 

Keywords (Data) Voids: Misinformations via Google and Bing.

Credit: pexels.com

In decreasing order of Trust in News: Media I use, Media Overall, Search engines and Social Media.

From the must-read Reuters Institute and Oxford University Digital News Report, you can read the following for the US:

Misinformation using keyword voids via Google and Bing. The “evil unicorn problem”.

Keywords Voids, also known as Data Voids, might not be the only reason for this low level of Trust but it’s important to know how this works.

Desperately seeking quality content.

Every one of us searches Google 3-4 times every day.

But every searches are not equal. Lots of searches are too vague and thus will return lots of noise and (yes!) 15% of all searches on a yearly basis were never searched before.

Bottom line, in the too vague a query, you will add more words and the combinaison may not have much quality content. Same for searches never searched before.

This means there are many search terms for which the available relevant data is limited, non-existent, or deeply problematic. We call these “data voids” or “keywords voids”

The malicious exploit: Wide open door to misinformation and manipulation.

Typology of Keywords/Data Voids (source (highly recommended read): Data Voids: Where Missing Data Can Easily Be Exploited)

Active Keywords/Data Voids on breaking news.

“Data voids that are actively weaponized by adversarial actors immediately following a breaking news event, usually involving names of locations or suspects in violent attacks (e.g., “Sutherland Springs” or “Parkland.”)”

Active Keywords/Data Voids on problematic terms.

“Data voids that are actively weaponized by adversarial actors around problematic search terms, usually with racial, gendered, or other discriminatory intent (e.g., “black on white crime” or “The Greatest Story Never Told” or “white genocide statistics.”)”

Passive Keywords/Data Voids on a particular group

“Data voids that passively reflect bias or prejudice in society but are not ac- tively being weaponized or exploited by a particular group (e.g., “CEO.”)”

A byproduct of cultural prejudice.

Not an easy task. “Data voids are a byproduct of cultural prejudice and a site of significant manipulation by individuals and organizations with nefarious intentions. Addressing data voids cannot be achieved by removing problematic content, not only because removal might go against the goals of search engines but also because doing so would not be effective. Without high-quality content to re- place removed content, new malicious content can easily surface”

Responding to data voids requires making certain that high-quality content…

“Unlike other forms of content moderation, responding to data voids requires making certain that high-quality content is available in spaces where people may seek to exploit or manipulate users into engaging with malignant information.”

… but only you can decide what is a “quality content”.

This is why we are building TrustedOut. Am AI-Operated database of media profiles. Unbiased. Up-to-date. Universal.

Questions? Shoot!

 

“Local leads to trust”

“The shorter the distance between our neighbors and our news, the stronger our community.”

This article from NiemanLab about an event organized by the Knight Foundation is a perfect follow up to our previous post:

The decline of local newspapers impact on democracy.

Saving the Soldat Local News

… and, as a reminder the Knight Foundation did commit an addition $300M to support journalism and local news

Saving journalism. [updated 2/19/19]

The American Journalism Project

Things are definitely moving with the launch of the American Journalism Project, a venture philanthropy effort co-led by Chalkbeat founder Elizabeth Green and Texas Tribune founder John Thornton, with $42 million in its first fund.

Attention, money, efforts… we’ll keep you updated on this. Stay tuned.

Questions? Shoot!

 

 

“Why customer trust is more vital to [media] brand survival than it’s ever been”

We recently wrote in the post below:

“Your Trust is based on the publisher brands you value.”

TrustedOut’s market

Because Trust and brands are so linked, including for Media, we wanted to share our takeaways from this CMO (Chief Marketing Officer) article “Why customer trust is more vital to brand survival than it’s ever been” [article applies beyond Australia].

Authenticity is key to trust, so how do [media] brands build this in a world of digital and social upheaval?

“Simply saying a brand is going to do something, without backing it up with actions, is a consumer disaster waiting to happen.”

“It’s about being transparent, doing what is expected and shared values. Key to this is the internal culture of the brand becoming more evident,” she [Qualtrics customer experience subject matter expert and principal consultant, Vicky Katsabaris] says. “The expectation is you deliver to those values with more purpose-driven activities so you are living and breathing the values.

Gaining trust: demonstrate [media] brand clarity of purpose and core values and be transparent with all policies and procedures.

“Board and staff members need to adhere to these ethical standards as, in effect, they are the brand and only they can elicit consumer trust,” he [Director of brand agency Hulsbosch, Jaid Hulsbosch] says.

To do this, a corporation and its brand needs to be determined to demonstrate brand clarity of purpose and core values and be transparent with all policies and procedures”

2019 predictions.

This confirms our predictions for 2019…

Top 2019 predictions: Privacy and Transparency

…  and confirms the uptrend in trust in media lately.

 

 

 

The decline of local newspapers impact on democracy.

 

A recent study published in Oxford’s Journal of Communication (and available here) shows some very interesting links between the impact of losing a local newspaper and the increase of bipartisan (left or right) votes. Here are our takeaways:

Newspaper Closures Polarize Voting Behavior

Missing local news has a negative impact on political outcomes

“Local news sources are not merely suffering in this new marketplace—many are disappearing for good (Hindman, 2009; Shaker, 2014). As newspapers close, other local media are not emerging to fill the information gaps, with negative impacts on important political outcomes”

Less local news, less regard on local politics.

“Another emerging literature details negative consequences of declining local news. Where local newspapers are weaker, people know less about their representatives and subnational governments and turn out at lower rates (Hayes and Lawless, 2015, 2018; Kübler & Goodman, 2018; Shaker, 2014), and municipal governments spend less and borrow at higher rates (Gao et al., 2018; Yazaki, 2017). ”

The Nationalizing Media Environment and Political Polarization

Less news opinions creates more, national-based, bipartisan decisions

“Declining access to quality local news is harmful to voter behavior and responsive governance, leading to more corruption (Arnold, 2004; Besley, Burgess, & Prat, 2002; Campante & Do, 2014; Strömberg, 2004) and lower voter turnout (Schulhofer-Wohl & Garrido, 2013). In the absence of quality local news options, Americans may rely on partisanship and national news to inform their political decisions (Hopkins, 2018; Trussler, 2018).” A relative reduction of local news in the media marketplace may result in less exposure to local news and more regular exposure to national media, with significant effects on engagement and partisan voting (Clinton & Enamorado, 2014; Hopkins & Ladd, 2014; Hopkins, 2018).

When a local newspaper closes, split-ticket voting decreases by 1.9%.

[Split-ticket voting refers to when a voter in an election votes for candidates from different political parties when multiple offices are being decided by a single election, as opposed to straight-ticket voting, where a voter chooses candidates from the same political party for every office up for election. – Wikipedia]

Less local news mix local and national matters.

“Our findings connect the literature on the polarizing effects of the changing news environment to scholarship on the negative democratic consequences of the decline of local news: just as adding the internet or partisan cable news to the media environment can influence voting behavior, removing a local news source from the marketplace may polarize the choices citizens make”

So, now, what?

We wrote on Jan 17th, the post below announcing Google, Facebook, and now the Knight Foundation have reach a whooping $1B financial support quality journalism and for local news…

Saving journalism. [updated 2/19/19]

Also, on Feb 11th, we published this:

Trust, Media and Democracy

Why it matters to us

Quality journalism is mandatory for democracy and vital to Media brand values. The foundation of TrustedOut Media profiling  to provide sources Analysts will define as their need and trust in Business Intelligence, Advertising and PR.

Questions? Shoot!

Presentation to the GESTE (major online editors in France)

We were very proud to be invited to present at the latest GESTE (major online editors in France) event about “Trust and labelization“.

The presentation.

Click to run presentation

 

The Table of Content.

The problem:
Distrust in media.

The consequence:
In decision-making, only what is trusted in can be trusted out.

The point:
Distrust is general, trust is personal. No universal list.

The logic:
Trust is about reputation. Media Brands are about reputation.

The solution:
Industrial Profiling Media Brands.

The application:
Easy querying, live feeding.

The technology:
Machine learning, Web crawling, big data and microservices to self-feed, self-grow and daily validations

Our live taxonomy:
Permanent machine learning, customizable sensitiveness & specialty depth,
enterprise mapping.

The opportunity:
BI, Ads & PR

Question? shoot!

 

Get information from Traditional Media, have conversation on Social Media. Not the other way around.

Misinformation and biases infect social media, both intentionally and accidentally

This highly recommended article from The Conversation exposes 3 types of bias identified by Indiana University. Hereafter are our takeaways.

1/ Bias in the brain

More information means less quality content shared

“Cognitive biases originate in the way the brain processes the information that every person encounters every day. The brain can deal with only a finite amount of information, and too many incoming stimuli can cause information overload. That in itself has serious implications for the quality of information on social media. We have found that steep competition for users’ limited attention means that some ideas go viral despite their low qualityeven when people prefer to share high-quality content.”

Beware emotions in headline trap

“One cognitive shortcut happens when a person is deciding whether to share a story that appears on their social media feed. People are very affected by the emotional connotations of a headline, even though that’s not a good indicator of an article’s accuracy.”

What matters is where it’s coming from.

“Much more important is who wrote the piece.”

TrustedOut foundation: profile who’s behind to evaluate your trustworthiness appreciation and the path to greater trust in media:

Optimism and method for greater trust in media.

2/ Bias in society

Like seeks like (“Birds of a feather flock together”)

“When people connect directly with their peers, the social biases that guide their selection of friends come to influence the information they see. …social networks are particularly efficient at disseminating information – accurate or not – when they are closely tied together and disconnected from other parts of society.”

“Us vs Them”

“The tendency to evaluate information more favorably if it comes from within their own social circles creates echo chambers that are ripe for manipulation, either consciously or unintentionally. This helps explain why so many online conversations devolve into “us versus them” confrontations.”

We are right. Distrust in fact-checking

“…during the 2016 U.S. presidential elections [analysis] shows that Twitter accounts that shared misinformation were almost completely cut off from the corrections made by the fact-checkers. When we drilled down on the misinformation-spreading accounts, we found a very dense core group of accounts retweeting each other almost exclusively – including several bots. The only times that fact-checking organizations were ever quoted or mentioned by the users in the misinformed group were when questioning their legitimacy or claiming the opposite of what they wrote.

3/ Bias in the machine

Getting more of the same. Accurate. Or not.

“The third group of biases arises directly from the algorithms used to determine what people see online. Both social media platforms and search engines employ them. These personalization technologies are designed to select only the most engaging and relevant content for each individual user. But in doing so, it may end up reinforcing the cognitive and social biases of users, thus making them even more vulnerable to manipulation.”

Illusory truth effect. Repeat until it feels true.

“For instance, the detailed advertising tools built into many social media platforms let disinformation campaigners exploit confirmation bias by tailoring messages to people who are already inclined to believe them. Also, if a user often clicks on Facebook links from a particular news source, Facebook will tend to show that person more of that site’s content. This so-called “filter bubble” effect may isolate people from diverse perspectives, strengthening confirmation bias.”

Popularity bias. More clicks makes it feel more true.

“Another important ingredient of social media is information that is trending on the platform, according to what is getting the most clicks. We call this popularity bias, because we have found that an algorithm designed to promote popular content may negatively affect the overall quality of information on the platform. This also feeds into existing cognitive bias, reinforcing what appears to be popular irrespective of its quality.”

Get information from Traditional Media, have conversation on Social Media. Not the other way around.

Unsurprisingly, and somewhat reassuring, numbers from Reuters/Oxford (hereafter for the US) show trust in social media are the lowest with 13% vs 34% for news overall and the highest at 50% with News/Media I use. (we developed this with this post “While distrust is general, trust definition is personal.“)

Related posts:

Saving journalism. [updated 2/19/19]

Top 2019 predictions: Privacy and Transparency

TrustedOut’s market

Questions? Comments? Contact us!

 

 

Older people share more fake news.

Age predicts behavior better than any other characteristics (even party affiliation )

Researchers at New York and Princeton Universities, through their recent surveys, are saying older users shared more fake news than younger ones regardless of education, sex, race, income, or how many links they shared. [source: The Verge]

7 times more fake news sharing

“But older users skewed the findings: 11 percent of users older than 65 shared a hoax, while just 3 percent of users 18 to 29 did. Facebook users ages 65 and older shared more than twice as many fake news articles than the next-oldest age group of 45 to 65, and nearly seven times as many fake news articles as the youngest age group (18 to 29).”

Profiling media sources…

“It won’t be easy: how to determine whether a person is digitally literate remains an open question. But at least some of the issue is likely to come down to design: fake news spreads quickly on Facebook in part because news articles generally look identical in the News Feed, whether they are posted by The New York Times or a clickbait farm.”

… to build trust.

Profiling sources so limit fake news spreading is similar, in logic, to profiling sources to limit misleading intelligence. We call it “Corpus Intelligence” and will focus on B2B solutions. In production end Q1 2019.