Drew Margolin

Associate Professor

Drew Margolin, professor of communication at Cornell University, studies the way people communicate online and the role of accountability, credibility, and legitimacy within social networks. He says Twitter’s new reporting tool doesn’t prevent users from sharing false news, and may even become a tool for discrediting legitimate voting information.

Margolin says:

“This is a useful step in comparison to the previous laissez-faire approach. However, it is important that Twitter policies (and other social media companies’ policies) account for the underlying dynamics of disinformation campaigns, rather than their particular manifestations in past elections. 

“Disinformation campaigns exist because, in politics, there are strong incentives for some individuals, groups or parties to persuade the public of things that are not true.  In social media, these individuals, groups or parties can freely enter the public sphere and broadcast misinformation without editorial review. Creating a reporting system doesn't change this fact. It only shifts the strategy. In particular, now we should expect that legitimate voting information will be ‘flagged’ by those for whom confusion about when, where, or how to vote would be advantageous.

“So, the devil is in the details of what happens after something is flagged. How is its legitimacy judged? In particular, since voting policies are presumably determined by the government, it would seem that the government would have to ‘validate’ any claims about where, when or how to vote. Is that what Twitter is doing — consulting with voting officials?  Or are they relying on third parties, such as election observers? These decisions will in turn influence the way that the disinformers adapt.”

Shawn Mankad

Assistant Professor

Shawn Mankad, professor at the Cornell SC Johnson College of Business, studies machine learning, innovation, entrepreneurship and technology. He says the success of Twitter’s new reporting tool will depend on the algorithm behind it, and whether it strikes the right balance in catching as much misleading information as possible, without jeopardizing legitimate content.

Mankad says:

“Twitter’s new tool will surely be based on machine learning, and therefore require careful tuning so that the tool balances how it labels content. If the algorithm is too strict, then legitimate content might mistakenly be labeled as misinformation. In the other direction, Twitter could err by not catching all misleading content and letting some of that misinformation go unflagged.

“Their ability to minimize these errors (called false positive and false negatives in machine learning), will largely determine how well the tool performs in weeding out misleading information.”