Figuring out Feelings

Understanding well-being and how we feel is very much in our consciousness. Organisations now see staff well-being and personal happiness as key components of a sustainable way of working and a big influence on staff retention. At a societal level we are concerned about how stressed and unhappy our youth are, how lonely our elderly might be and the increasing suicide rates in many developed countries. If we had a way of measuring and trending how people feel, that may go a long way in designing early intervention programs and reviewing the efficacy of the various initiatives deployed by organisations and society itself.     

I read something which got me thinking a little more about this. Researchers at the University of Manchester and Indiana University have put some science behind this idea of  ‘sentiment tracking’.

They looked at how global emotion and mood, as measured via something like Twitter, could predict stock market activity. They investigated whether measurements of collective mood states derived from large scale Twitter feeds, correlated to the value of the Dow Jones Industrial Average (DJIA) over time. They analyzed the text content of daily Twitter feeds by two mood tracking tools, OpinionFinder that measures positive vs. negative mood and Google-Profile of Mood States (GPOMS)

Their results indicated that the accuracy of DJIA predictions can be significantly improved by the inclusion of specific public mood dimensions. If you want to predict closing prices on the Dow Jones, have an eye on the Twitter feed.

This raises some interesting questions about how we look at social media data or activity. It may justify a more qualitative approach. When it comes to social media marketing, we may need to look at how our followers feel, rather than just counting them.

While causation and correlation could be difficult to nail down, there is perhaps something in this for the Data Scientists among us. There is a plethora of online content generated by users across all forms of social media, text, images, video. 

Parsing online content and cross referencing this with behavior may create some predictive tools or highlight some change in public mood. Looking at which emoticons are used and how frequently may be one place to start. Too many ASAPs in emails might signal someone under a little stress, lots of work emails sent at night or weekends could be from someone on the road to burnout. 

The take away is that with a focus on the Internet of Things, we could be relying on devices to tell us more about the world and how we live our lives. However a lot of this information could already be out there, we just have to look for it.  

Too Much Information

We are all familiar with TMI, Too Much Information. Usually we hear it when we are exposed to some gory or elicit detail we’d rather not hear. It has however a more serious application when it comes to how we make decisions.

In these days of big data, ubiquitous connectivity and apps at every hands turn we often have lots of data about the choices we need to make in life, be that at work or at home. Too much information however can impede our ability to make decisions.

On the face of it, this is counter intuitive. We like to think that more information drives smarter decisions; that the more details we absorb, the better off we'll be. Knowledge is power and information feeds knowledge.

However when presented with loads of facts, we have difficulty in selecting out the items that really matter, it takes time and effort that we may not have. Worse still, the key pieces of information that should influence our decisions remain hidden and unused.

Malcom Gladwell makes this point in his book Blink. He tells a story about Cardiologist Lee Goldman at Chicago's Cook County Hospital.

Goldman used some statistical rules which mathematicians designed for telling apart subatomic particles. He fed a computer data of hundreds of files of heart attack cases and crunched the numbers into a “predictive equation” or model.

Four key risk factors emerged as the most critical tell tale of a real heart attack case:

1. ECG (the ancient electrocardiogram graph) showing acute ischemia

2. unstable angina pain

3, fluid in the lungs

4. systolic blood pressure under 100

Previous to this Cardiologists took quite an amount of time looking at patients, got information on weight, gender, lifestyle, how they came to be admitted, what their living conditions were at home. Goldman got Cardiologists to park their instincts and thirst to find out everything about a patient and used his 4 simplistic reference points. 

Outcomes improved on wards and fewer of the patients who were sent home from ER represented back at a future date with a heart attack. Using four simple facts that mattered led to better decisions.

Gladwell makes another point on how knowing less can improve our decisions. Not quite as serious as heart attacks but still interesting. For years it was believed that women could not play in orchestras as well as their male counterparts. Men dominated the ranks. 

Then someone had the bright idea to put in a screen for auditions where the musician was only heard and not seen. The blind audition was born. The number of women in major US orchestras increased fivefold. Decision makers knew less about their applicants (their gender) and now made their call on what was the core question, how well someone could actually play.

The take away is that when you have a decision to make, look at the information available to you. Try to critique what is important what and what is background noise. Write down the facts that influenced your decision, were these the facts that really mattered? Try to resist gathering KPI after KPI just because you can.