Social listening software is a form of automation; it automates the process of collecting data and picking up on important patterns and trends in online communications that could potentially empower businesses to make vital changes to facilitate new growth.
And automation is everywhere. According to US consulting firm McKinsey, three quarters of businesses are already automating processes in one way or another. With the benefits that automation can bring – minimising human error and freeing up key workers for more productive tasks, for example – this figure isn’t surprising. What is surprising is that McKinsey notes that ‘only a slight majority have succeeded at meeting their targets’. Why? Because businesses are overlooking the need for the human touch.
There have been numerous stories over the past few years, especially as automation has become more widely adopted, regarding the potential for robots to take over jobs. But the truth is that humans and automated technologies serve different purposes. Technology alone is no match for human ability.
Let’s look at IBM’s Project Debater as an example. Project Debater was created to show that machine intelligence can be equal to human intelligence in debate. However, while Project Debater has certainly impressed, there is a problem. Audiences have found that while the AI-powered machine can convey more information than its human competitor, it’s nowhere near as good at actually delivering this information.
“First, a debater needs to process large amounts of information and construct relevant arguments. Second, debating involves [explaining] complicated arguments in a clear and structured way. Third, you need to make those arguments matter to the audience. This requires the careful use of language, emotions, rhetoric, and examples. While a machine should excel in the first, the latter two may be challenging.” – European Debating Championship winner and World Debating Championships grand finalist Harish Natarjan, who won a debate against Project Debater.
As we can see, there is a limit to the scope of even the most innovative of technologies. Yet this is often overlooked, and what we’re left with is companies using automation to try and eliminate work. That’s not the point. Automation software isn’t about eliminating work; it’s about making the outcome of work more valuable and more beneficial through collection and analysis of big data that’s beyond the scope of human input.
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How Social Media Has Changed Language
Let’s start looking at automation software specifically in terms of social listening. One of the biggest issues today is that the language used on social media platforms is beyond the scope of today’s machine learning capabilities. And it’s because of ‘Twitter English’.
Twitter English is often thought of as being its own language. While it may have the same syntax as standard English, the pragmatics are entirely different. Consider how Twitter users communicate. They’re restricted by word count, which means that communications are forced to be short, while still conveying the full information.
How do we convey this information? Through strategic language changes.
Face to face conversations are simple, aren’t they? They have tone, and they’re complemented by facial expressions and body language, all of which helps us to bulk out our verbal speech with non-verbal communications to convey a message. But we don’t have that luxury online, and we’re limited in our verbal communications by social platform rules and regulations. And so we use visual representations. We use emojis to show how we feel, we use slang to create a more informal discussion, and we use initialisms like LOL to convey tone; all things that humans can draw meaning from.
And it’s not just Twitter English we have to take into account. There are actual language differences, too. In a study of bilingual Welsh school children, it was found that around one quarter use an equal split of English and Welsh when using Facebook. This introduces a completely new challenge for social listening tools, which are faced with trying to collect and analyse similar data pools in a selection of different languages.
Listening software goes beyond the scope of the natural human ability to collect, collate, and analyse data… but it’s no match when it comes to assigning meaning.
Human-centered automation is described as technologies which ‘enhance the capabilities and compensate for the limitations of human operators’, and that’s exactly what social listening tools do. They enable us to gather data that we wouldn’t be able to otherwise, but they still require intervention by humans capable of critical thinking.
Social Listening and the Human Touch
There are two distinct parts of social listening: data collection and analysis, and assigning meaning to that data and using this context to generate perception and drive action. Social listening tools are excellently equipped to handle the first part. In fact, they’re better equipped than humans because the collection and analysis of data is scientific. There are factual rules at play here, so if you only wanted to collect and analyse data, you could get by using listening software alone. But that’s not the point of social listening. The point of social listening is to deliver real, actionable insights that enable you to make data-driven decisions and implement data-driven change.
And that’s why the human touch is so important. It brings in the human element of understanding to ensure you’re always getting the most from your investment.
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