College of Liberal Arts

When an Unexpected Disaster Strikes, Abstract Language Helps

Mon, Jan 28, 2019
When responding to unexpected disasters, public leaders should keep language simple, researchers said.
When responding to unexpected disasters, public leaders should keep language simple, researchers said.

Shocking events, such as mass shootings and natural disasters, can create causal uncertainty, leading many to ask, “Why did this happen?” For public leaders, providing their communities with answers can be difficult, but new research on Twitter users shows the best messages are simple and on point.

The study, conducted by researchers at the University of Nevada, Reno; Tulane University, and The University of Texas at Austin, was published in the Journal of Interactive Marketing. It examines what types of language are positively received during times of heightened causal uncertainty and who the message is and should be coming from. This research is a part of a larger body of work that investigates the relationship between causal uncertainty and abstract thinking.

“Abstract language is simpler and gets at the gist of something, focusing on broad themes, general principles or ideas. This doesn’t mean it’s uninformative or vague; rather, it deals with key information about things,” said the study’s co-author Marlone Henderson, UT Austin associate professor of psychology. “Concrete language provides more context and richer sensory details, but the information can be superfluous and distract from the core message or point. During times of causal uncertainty, people want a simple, straightforward messages.”

Focusing on nine unexpected, high-profile events from 2016, including the Orlando nightclub shooting, Brexit and the Nice terrorist attack, researchers examined how one’s preference for an abstract cognitive process is manifested in social interactions. For control data, researchers examined Twitter responses to Hurricane Matthew, a negative event with less causal uncertainty due to weather forecast warnings.

“We focused on social media since it has emerged as a powerful tool for individuals to directly communicate with others in times of causal uncertainty,” said Jae-Eun Namkoong, principal investigator and assistant professor of marketing at the University of Nevada, Reno. “This is also reflected in the U.S. government’s investment in studying how to better utilize social media for managing crises.”

Overall, researchers found that causal uncertainty increased Twitter user’s liking and sharing of messages that consist of more abstract language. In fact, increasing the abstractness of a tweet by one standard deviation increased its number of likes by 10 percent, researchers found. This effect was especially pronounced when the messages were from socially influential sources.

These findings were complemented with experimental studies on the 2017 Amtrak train derailment in Washington, D.C. Researchers manipulated low or high causal uncertainty surrounding the train derailment by asking participants to elaborate on what they did or did not understand about why or how it happened. They were then shown either an abstract or concrete tweet about the incident.

In another experiment, participants were told whether the tweet originated from someone with high social influence (manager or leader) or low social influence (regular group member). These experiments confirmed what was discovered in the real Twitter data: people found abstract messages from others more appealing, especially when the message source had high social influence.

“Our study offers unique insights into the type of language favored and shared on social media in situations of heightened causal uncertainty and the moderating role of the social prominence of a message source,” Henderson said. “I think what we learn from the Twitter data can help us understand how people react to brief, snippets of news— such as a text message, a news soundbite or a passing conversation—and how leaders, most importantly, can respond and interact effectively to reduce causal uncertainty.”

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