May 25, 2017
AI and the digital publishing evolution: what’s next?

AI and the digital publishing evolution: what’s next?

By Giovanni Strocchi

Robot assistants guiding shoppers around stores, self-driving cars cruising the highways of New York, and automated lawnmowers tidying up the backyard — this is life in 2017, the year that artificial intelligence (AI) became an everyday convenience tool.

Smart, labor-saving technology isn’t just helping streamline services and chores; it’s also being used in many sectors as a performance optimizer, especially in online publishing.

Digital publishers are increasingly using AI to enhance content appeal for both audiences and advertisers, and to ensure brand safety – Google, for instance, has just declared an incremental focus on AI-powered brand protection. After unwittingly placing ads alongside extremist content on YouTube, the company has announced plans to deploy machine learning that will identify potentially offensive content at scale.

Meanwhile, others are already well on the road with advanced semantic technologies designed to take a closer look at content; offering a granular view of what the words on a page mean, how individuals interact with them, and what this means for ad targeting.

So, with AI innovation moving rapidly, what’s next for publishers and their audiences?

Refining insight with Natural Language Processing 

The problem with many contextual,“keyword-based” analysis systems is that they’re imprecise. They often count words as ‘character strings’ so that analysis runs faster, but in doing so, removes crucial contextual understanding and insight, making ad matching difficult. To address this, progressive publishers are turning to AI machines that use tools such as Natural Language Processing (NLP). This semantic cognitive technology understands each word on a page as a human brain does,  to extract the true meaning,, which  results in precise classifications.

True NLP cognitive solutions have the ability to replicate human understanding and read content as we do, with semantic technology that can pick up on subtle changes in the meaning of words according to their context. The word ‘aged’, for example, can describe how old someone is or the state of a mature wine or cheese. NLP can spot the difference.

By unlocking the real meaning of the digital content audiences engage with, publishers are starting to gain deeper insight into their attitudes and values, which can be used to identify online communities based on areas of interest — a vital asset for effective segmentation.

Creating 360-degree user profiles

It goes without saying that today’s consumers expect a high level of personalization, but meeting these expectations requires granular data analysis that has been difficult to execute quickly and accurately — until now. When combined with other data types, such as demographic, psychographic or behavioral data, the insight NLP analysis produces can create something that enables truly personal content tailoring: 360-degree user profiles.

An all-encompassing picture of individual likes, dislikes, and attributes, these profiles provide publishers with the information required to develop unique content and ensure ads reach the right user, in the right context. As demand for personalization rises, so will the use of such 360-degree profiles in helping brands reach their target audiences.

Using AI to go beyond static profiles 

As publishers look to outshine competitors and keep ad revenue flowing, there is a growing need for ad targeting that doesn’t just meet current consumer needs, but also anticipates what they want next. AI-based tools are emerging as the best method of achieving this.

Advanced cognitive technologies not only enable publishers to turn chaotic data into orderly profiles, but also analyze responses to digital content and predict which content, discounts or products individuals will react positively to in the future. Armed with this data, publishers will have the means to significantly enhance advertiser demand — offering brands the chance to target audiences with the highest propensity to buy their products — and make sure website content stays fresh by updating content in line with user needs.

Keeping brand reputation safe 

Last but not least, there’s the role that AI is set to play in brand safety and the increased awareness of NLP tools in their capacity for guarding brands against inappropriate ad placements. As advertising becomes ever more programmatic, NLP will play a leading role in ensuring brand safety – after all, the risk of mismatched ads is far greater with automated auctions that trade billions of impressions in milliseconds.

By assessing page content before ads are placed, AI-based NLP semantic tools can help guarantee that environments are relevant, and safe —and help tackle the issue of fake news.

While the industry has not yet agreed on the definition of fake news, it still poses a threat to publishers by bringing digital advertising into disrepute. Technology such as NLP will soon be at the frontline of precise content assessment to complement and integrate any “human-expert evaluated” fake news solution, such as identifying and erasing any extreme partisan content, or hate speech,  which is often the product of a potentially fake news source.

For consumers, AI might mean smart gadgets and more spare time, but for publishers it’s an industry-wide evolution that’s allowing them to gain a unique understanding of audiences, optimize content, and keep digital ad revenues high — and the best news is, it’s set to do a whole lot more.

From: Talking New Media