It’s hard to dispute that the world, and certainly the modern workplace, is a highly connected place. Understanding how that interconnectivity works is crucial to appreciating the social business world. A recent study, led by researchers from Notre Dame University, set out to shed some light on the matter.
It reveals that the knowledge we glean from social strategies within networks gives us a good chance of understanding how those networks evolve and function, with some clear implications for how social businesses are managed.
The researchers were hoping to uncover the social strategies people use within their social networks, and use those to construct models that would be able to detect characteristics about those users, such as their demographics, based upon nothing more than their communication behaviours.
The study utilized some 1 billion communication events (classed as either a phone call or text message) from 7 million anonymized users of a mobile phone network. The researchers believe that the data, and their analysis of it, gives them fantastic insight into user characteristics.
“The key aspect of our work is that just with the knowledge of the structure of the social network, we are able to accurately infer social strategies and demographic information such as age and gender,” they say. “We had no identifying information about users available in our data.”
At the heart of the model generated by the study was what the researchers call the Who Am I approach to predicting both the gender and the age of each user in the network.
“The proposed WhoAmI method is a graphical model-based machine learning algorithm,” they explain. “Compared with the traditional machine learning algorithms where only the correlations between demographics and attributes of each user are considered, the WhoAmI method can also model the structural correlations between different users.”
Findings from the study
The study revealed that young people are generally more active in broadening their social circles. Older users by contrast would tend to have a smaller circle of friends that they were very close to. Perhaps not surprisingly, interactions with the opposite sex also tended to drop after the age of 35.
“The discoveries characterize the properties of human communications regarding the demographic profiles and further show us how the social strategies change over time across one’s lifespan,” the researchers suggest.
So how might this affect communication in the workplace? They believe that their methods could be used just as effectively in other contexts, one of which would be in workplace communications. Suffice to say, there are already various solutions on the market that look to explore the way employees engage with one another, but few have yet undertaken a time lapsed study highlighting the impact of, for instance, an enterprise social network on communication patterns, or a before and after an open innovation challenge.
It seems inevitable that network mapping will be deployed to an increasing degree in the workplace, not only to understand communication patterns more effectively, but also to understand the spread of certain behaviours through a workforce. It’s a subject that the researchers will be exploring in more depth in future studies.
“Specifically, we would like to characterize human communications in terms of socio-economic and cultural conditions,” they say. “This direction would truly put our research into practice.”
The paper was published in conjunction with the 20th Conference on Knowledge Discovery and Data Mining, which brands itself as the premier conference on topics such as data science, data mining, knowledge discovery and big data.
