What Accessible AI means for Social Listening

If you don’t want to know about AI, don’t go into your social feeds right now.

Even for those locked in the most restrictive echo chamber, you won’t need to scroll far before bouncing off opinions, promoted content, influencers, news headlines, and more, all waxing lyrical about its transformative role.

The tech space is somewhat eulogistic about emerging technology, so it’s not without good reason that you might be taking this gold rush with a pinch of salt. As a company professionally questioning the quality of online information, we salute this.

However, what is unique about this technology, is the pace with which it is taking root. ChatGPT was only made public in November, and it already feels like it has a Google-esque level of influence when shaping information flows online.

What does this mean for Social Listening platforms?

In its current incarnation, one of the most agreed-on uses of AI is to take the heavy lifting away from various creative tasks currently done by humans.

Jobs such as writing, video production, image creation, making social posts, and building landing pages have all been targeted. The thinking is, why pay people to do these tasks in hours or days when AI can do them in minutes at a fraction of the cost?

Whether you agree with this or not, for social listeners, this can only mean one thing – more. More digital content, attached to more opinions, flowing out onto social platforms. And with more comes an increased threat to reputation caused by inaccuracy, maliciousness, and confusion.

As higher-quality content becomes commoditized and democratised – platforms monitoring the web for brands and large organisations will have to respond accordingly.

Not only will volume parameters have to be optimised to prevent a logjam of irrelevance, but AI’s ability to mimic human communication will also call into question where the threshold lies for flagging what has a bearing on reputation.

If a bot flaps its wings in a forest, can it cause a storm at HQ?

Solving these problems lies not in staring at graphs – all these will show is a move ‘up and to the right’ – but in optimising the filters we put in place, both human and technological, to consider this sea change in context.

We need to get better at filtering out irrelevance, acknowledge that the playing field is shifting, and build technology dynamic enough to scale, interrogate, categorise, and make decisions relevant to a changing data set.

Social listening has always been reactive to the platforms it monitors in some regard – so while the context may have shifted – our role hasn’t.

Only by adapting can we continue to tune out the noise to get to the heart of the real meaning because, while artificial intelligence may be driving the change, only humans can make the decisions.