However, the most accurate model for predicting successful debaters combines information about social interactions and language, the researchers found after analyzing data from Debate.org, a website that hosts debates on a variety of topics.
"It turns out that the interaction of people on this platform is really predictive of their success," said Esin Durmus, a doctoral student in computer science.
"So if someone is trying to win an argument, they should focus on their social interactions, like discussing interesting findings with the people they're friends with," Durmus added while presenting the findings at the "Web Conference" in San Francisco from May 13-17.
The study, co-authored with Claire Cardie, professor of computer and information science, has implications for online debaters looking to improve and for developers of Artificial Intelligence (AI) systems seeking to expose humans to different perspectives.
"To assist automated systems that could maybe debate a human, the first thing to understand is what factors are important in persuasion.
"If this debater had information about people's backgrounds or past interactions, maybe it could then personalize the types of arguments it uses, to maximize the chances of persuading them," noted Durmus.
The dataset for the study included more than 67,000 debates on 23 topics. The researchers also collected nearly 200,000 voter comments on those debates, as well as personal information for more than 36,000 users.
"A lot of researchers are looking at what kind of language is important to be able to persuade people, and they basically look just at language, but here we are saying that to study this you should also consider other factors," Durmus noted.