Let’s look at other ways AI can enhance reader engagement by focussing on the back end.
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We don’t have to tell you how AI can threaten the credibility of news. There are numerous examples of poorly conceived or applied generative AI undercutting the credibility of publishers. Most newsrooms are rightly revolted. Many have set out their charters to manage the danger of AI content.
But there is so much more to AI than generative AI.
When you drill down into the risks of AI, it quickly becomes clear that the issues are mostly linked to content creation. Asked what uses of AI held the most reputational risk, a study of 314 media leaders in 56 countries for ‘Journalism, media and technology trends and predictions 2024’ over half the correspondents immediately pointed to ‘content creation’. Newsgathering was next on the list.
Only 11% of respondents felt that ‘back end automation’ was an issue, and when asked about ‘distribution and recommendations’ that fear factor fell to a mere 3%.
So let’s put generative AI to one side for a moment. Instead let’s look at what non-generative AI can do in the background to increase the efficiency of a news publisher, by helping it focus on what it does best. In particular, let’s look at what AI can do for that key factor that is often pushed to the very end of the entire news process: the reader.
AI can be a valuable asset for news publishers looking to enhance their engagement with readers. Here are several ways in which AI can contribute to better reader engagement:
1. Targeted Newsletters
Newsletters are one of the few bright spots in news publishing. AI-powered systems can segment audiences based on interests and reading habits. They can also perform the task of A/B testing to see what frequency and scheduling best suits even the nichest of niches. Those lessons can then be taken by publishers to reimagine other newsletter offerings or find underserved reader segments.
2. Personalized Content Recommendations
AI has the time, speed, and capacity to analyse reader behaviour and preference, it can start to provide personalised content recommendations with the power to learn to improve as it goes. Where tools such as AI Contentify started out as SEO optimising agents, they are now increasingly content recommenders that follow up on their own suggestions. AI can also reformat existing content to suit user needs; such as Argentina’s Clarín which uses an AI tool to let readers themselves decide how they will consume existing stories.
3. Sentiment Analysis
We’ve long accepted dehumanising metrics like ‘eyeballs’ and ‘clicks’ but what if AI meant we could start to understand how our articles make people feel? Tools like Overtone AI were initially launched to analyse the tone of publisher content. What’s becoming clear is that tone analysis tools, pointed at social media chats or reader comments, can help us understand how readers feel about our content, and what they prefer. As social media brands prove increasingly unreliable as a traffic driver, the value of delivering to the readers we have only increases.
4. Community and comment analysis and moderation
Following directly from that comes the idea of AI in community moderation. So many publishers have responded to the risks of comments sections by turning off the comments. That’s understandable since trawling through them to keep them clean is a full time job, but it also throws away the ‘diamonds in the dust’ – the key insights scattered here and there. AI, particularly AI with sentiment analysis can make content moderation largely automated, both flagging dangerous users to the editorial team, and drawing potential conclusions from this source of insights.
5. Automated Social Media Engagement
You might not trust AI to write your news, but ensuring it gets posted to social media is a back-end task that many journalists are either too busy, or not bothered enough to do. So publishers are looking to automate it, with texts taken from your human authors, and monitored for tone.
6. Engagement Analytics
Most of the above fall under the idea of community building and encouraging engagement. All of which means little without constant and ongoing monitoring which is time consuming, however important. Unless you bring in AI to assist. At Schibsted a little lateral thinking has taken one tool; the AI voices used to read articles aloud, and married to the analytics engine. The result is a tool that means journalists can ask at any time how their story is doing and be told about its performance, or that of their rivals’.
What all the above have in common is that they use the bandwidth of AI to look into the learnings that most newsrooms simply don’t have the resources to study. Those findings can then be presented and scrutinised by old-fashioned human intelligence. Instead of replacing editorial staff, they then help the newsroom come up with new ways of expanding reach and increasing reader engagement.
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