Measuring influence is a tricky thing, but two professors out of New York University say they've designed a new research method that should help. In a paper published by Science Magazine this week, Professors Sinan Aral and Dylan Walker report both the details of their new method, and the results of their corresponding analysis of 1.3 million Facebook users. The analysis looks at how one commercial movie application was adopted across the social networking group. Among the results, Aral and Walker say they found that:
- Men are more influential than women
- Women influence men more than they influence other women
- Older people (30+ years) are more influential and less susceptible to influence than younger people
- Married people are the least susceptible to influence in the decision to adopt the product they studied
- Influence and susceptibility trade off, meaning people who were more influential tended not to be susceptible to influence and people who were susceptible tended not to be influential
- Some people are clearly more influential than others and are themselves connected to other highly influential people, giving them the potential to be 'super-spreaders'
The type of analysis conducted here is important in part because of its potential impact on algorithmically-driven communications. Marketers want to know who to target with promotions. Political campaigns want to know how to spread their messages quickly. And everybody wants to be able to act automatically on audience insights that are both accurate and meaningful.
The important contribution of our method is that it avoids known biases in current methods such as homophily bias. Homophily means that we tend to make friends with people like ourselves. For example, if two friends adopt a product or behavior one after the other, current methods have a hard time distinguishing whether it is because of peer influence or if the friends simply have similar preferences and thus behave similarly.
The future of influence tracking will have wide-ranging consequences. If researchers can improve the process, it's likely there will be plenty of people willing to pay for the results.