Knowledge Mobilization (KMb): Multiple Contributions & Multi-Production Of New Knowledge

Relevant-Signal To Data-Noise Ratio

signal noise

In science and engineering we often hear about the signal to noise ratio – a concept that compares the level of a desired signal to the level of background noise.  Although this is a technical term commonly used for electrical signals or biochemical signaling between cells, it can also be applied in the world of social media. In my own social media use I call this relevant-signal to data-noise ratio.

How often do we sift through Twitter feeds or Google search results to find what is relevant to our online research while also being inundated with data-noise?  I always keep this in mind when I’m doing digital research.  I can often find my Twitter feed filled with tweets that are relevant to digital research – and plenty more that are simply data-noise. Understanding the social media concept of relevant-signal to data-noise ratio can help us use social media in a more effective and productive manner and keep us focused on the more relevant information and knowledge sharing that makes using social media – especially for knowledge mobilization (KMb) – a better and more valuable experience.

As a community-based digital researcher, I was involved in a research project and book chapter publication with the Knowledge Mobilization Unit at York University, working with York University’s Executive Director of Research & Innovation Services,  Dr. David Phipps and York’s KMb knowledge broker,  Krista Jensen.  Our research project looked at Applying Social Sciences Research for Public Benefit Using Knowledge Mobilization and Social Media.  One of my contributions to this project was analyzing online profile keywords used on Twitter to advance our understanding of how individuals might use a social media platform like Twitter to connect and form collaborative relationships and like interests. Like interests are the foundation of communities of practice.

This important concept of relevant-signal to data-noise ratio  can be conceptualized by the following equation:

R-S:D-N = A (amount) of relevant-signal

                 = A (amount) of data-noise = 50

Basically, what this formula means is that the relevant-signal to data-noise ratio is equal to the average amount of what is a relevant-signal divided by what is the average amount of data-noise. To use this equation, for example, on a Twitter feed of someone I’m following on Twitter, I will often seek the keywords that are relevant to my digital research on a page of profile tweets. This can easily be done using the Ctrl-F Find function on any computer. I type in the keywords I’m looking for and – for convenience sake – I hold the amount of data-noise is going to be at least half or fifty-percent – as in a 50-50 chance.  This is why I have the amount of data-noise equal to 50.

When I find my keywords at least twenty-five-percent (25%) of the time or more (at least half of my 50-50 chance of finding data-noise), I will continue to follow this Twitter feed. If the amount is less than 25% – it’s filled with too much data-noise for what is relevant to my research interests, and I often make the decision to un-follow. I find this equation very helpful in making decisions about who to follow by weeding-out more of the data-noise.

All real measurement is disturbed by noise – and social media is no exception. As a research tool, social media is now being recognized as a valid part of gathering, exchanging and creating new knowledge, and as part of doing valid research.  However, many are still not effectively using social media in the best possible way to do this, and are still being swamped by a deluge of information and data-noise not relevant to knowledge sharing interests.  Or worse, people feel they need to connect broadly so as not to “miss anything”.  Remember, social media is NOT a popularity contest.  Attempts to measure or analyze your online success with what can be called as vanity metrics is irrelevant. It’s quality NOT quantity that counts in social media – so you may have to un-follow and eliminate some of that data-noise to find the relevant signal. I hope this relevant-signal to data-noise ratio equation is helpful for you in this process.

Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Google+ photo

You are commenting using your Google+ account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

%d bloggers like this: