Advances in neuroscience and genetics have opened up profound and difficult legal issues regarding individual behavior. For example, before her tragic death the late Jamie Grodsky published a set of stunningly good articles on the impacts of genetics science on environmental law and toxic torts, and my colleague at Vanderbilt, Owen Jones, heads a vast research project on neuroscience and the law.
But at the other end of the spectrum, rapid advances are also underway in how we understand crowd behavior, and there are legal issues waiting to boil over. Like many of the issues covered in Law 2050, these advances are the direct result of the Big Data-computation combo, in this case aimed at the science of social networks (and I’m not just talking about the NSA…uh-oh, probably by just saying that they’ll start following my posts!). Of course we all know that Big Brother and even our friends and businesses are snooping through our social media. As the International Business Times reported earlier this week, for example, insurance companies scour claimant’s social media posts at the time of the accident to detect fraud, admissions of fault, and so on. My focus here is different–it’s on how we can learn what an individual does from studying his or her social network behavior, not just what he or she communicates to it (see here for a great summary of legal issues surrounding the latter).
For example, researchers studying the equivalent of Twitter in China, Weibo, reached findings about the flow of emotions in social network suggesting that anger spreads faster than does joy. As they summarize their paper‘s findings:
Recent years have witnessed the tremendous growth of the online social media. In China, Weibo, a Twitter-like service, has attracted more than 500 million users in less than four years. Connected by online social ties, different users influence each other emotionally. We find the correlation of anger among users is significantly higher than that of joy, which indicates that angry emotion could spread more quickly and broadly in the network. While the correlation of sadness is surprisingly low and highly fluctuated. Moreover, there is a stronger sentiment correlation between a pair of users if they share more interactions. And users with larger number of friends posses more significant sentiment influence to their neighborhoods. Our findings could provide insights for modeling sentiment influence and propagation in online social networks.
It’s only a matter of time before clever lawyers start using similar techniques to inform questions of intent, motive, reputation, liability, and so on. For example, if it could be shown that a person’s social media network flared up with anger (e.g., hostile comments or rumors about a spouse) shortly before the person committed a crime, that could prove influential in determining motive. Similarly, social network analytics could be used to measure the reputation impact of alleged libel or slander, consumer confusion in trademark infringement claims, and market perceptions in shareholder derivative claims–basically, anything that involves crowd behavior. Of course, there will also be a swarm of related legal issues such as privacy, data breaches, and admissibility in legal proceedings. So, just as scientific advances at the genetic and brain level are fueling legal issues regarding the individual, so too are advances in the science of social networks likely to open up new legal issues regarding crowds as crowds as well as their impacts on individuals.