By: Antony Cousins, CEO at Factmata

In light of the pandemic, the need for fact-based information has grown exponentially. Recent technological trends have digitalised news and media outlets, increasing people’s reliance on inadequately researched and verified user-generated content. Given the myriad of sources available online, the risk posed by misinformation and inaccurate data is greater than ever before.  

When it comes to consuming news these days, people are spoilt for choice, given the sheer diversity of content available that cover a plethora of topics and trends. Nowadays, the Internet is seen as a necessary instrument that keeps people up to speed with current affairs. The difficulties posed by reporting breaking stories and fast-emerging trends have made the modern-day journalist’s work more complex. 

The Spread of False Narratives

On average, there are 500 million tweets shared a day, and new content is increasing exponentially. The past two years is an example of this, with Covid-related content generated faster than humans can analyse or check for accuracy. 

The reliance on brands and people, such as journalists or peers, to provide credible information poses a significant challenge for the internet. With Edelman finding nearly 1 in 2 people believe the government and media are divisive forces in society and over half of Gen Z and Millennials currently boycotting at least one brand – these factors are adding further expectation and pressure which can drive the spread of false and misleading narratives. 

Marketers have had to learn to interpret and react to events quickly. Communications teams have had to frequently navigate through a sea of information and opinions to decipher emerging trends and ideas. The fight for faster analysis of changing opinion and intent is fierce. Making sense of content is essential for marketers to stay ahead of the curve. 

The Downfall of Sentiment Analysis

Previously, the manual analysis of mass mentions about a person, brand, or story has foregone the nuances of language, geo-local, political, and cultural context. Identifying new narrative trends and predicting future sentiment and intent was not possible. 

Automated sentiment analysis was developed to overcome challenges in media monitoring. Although they’ve been updated and improved over time, sentiment analysis models are fundamentally flawed – they cannot understand the context. Inconclusive and harmful content that is potentially abusive or offensive is missed due to seemingly positive language.

Sentiment has been king for over a decade, but now there’s a new kid on the block. Businesses can leverage the modern capabilities of AI and other technologies to transform how they predict and stay ahead of narratives before they break. 

Making Insights Actionable 

The challenge for marketers analysing online content is to convert opinions into an intention. Progression in natural language processing has meant brands can understand the true intention of their audiences by looking at content in context. 

Rather than simply classifying words objectively, stance analysis models consider them subjectively towards a given topic. By accounting for nuances in language, insights are created quicker, and predictions are more accurate.

Every opinion counts. When it comes to mass data analysis, no longer do teams have to endure hours of manual labour to account for the limitations of sentiment models.

Automating Mentions With AI 

New narratives can be identified as soon as they start to develop. If left unchecked, trends can grow and reach thousands of people with harmful consequences. 

Artificial intelligence can assess the risk of online content about a brand or industry, driving efficiencies and increasing the quality of understanding conversations, perceptions, and intent. AI can also identify the influencers driving these narratives, enabling counter-measures to be deployed before they go viral and hit mainstream audiences. 

People can say the same thing in different ways on an increasingly disparate set of platforms but these mentions are all driven by the same narrative. Narrative monitoring of this kind automates the process of identifying mentions with this shared meaning. 

What to Expect from Narrative Monitoring?

Nowadays, as social media becomes more and more ingrained in our everyday lives, it also plays an important role in how many people access the news. The growing presence of virtual platforms has undoubtedly raised individuals’ awareness of important events,  but at the same time can leave them exposed to the dangers of poorly researched and verified content. 

With the cascading effect false narratives can have on reputational damage, brands are constantly monitoring sentiment to try and understand consumer behaviour and the key drivers behind public opinion. Monitoring the media for harmful narratives by measuring and analysing intent using ‘stance’ with the use of AI can track misinformation and identify the influencers behind the harmful content.

The days of sentiment analysis are now long gone. Stance analysis can enable brands to better safeguard themselves from harmful or threatening narratives, whilst enabling them to make sense of what is being said about a topic across online platforms. 

This transition into an increasingly digitalised era, dominated by the metaverse, is proving treacherous for brands. Today, rapidly dispersing high volumes of data poses a risk to brand safety, calling for preventative measures to be taken with immediate effects, such as narrative monitoring.