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Twitter after high-profile events: how language impacts message engagement

Posted January 22, 2019

For those connected to social media, it is not uncommon to turn to Twitter during high-profile events. In fact, a majority of Twitter users rely on the platform for news consumption. Shocking events, such as mass shootings and natural disasters, can create a lack of understanding among the greater population about why things happen. This causal uncertainty is often explored by people on Twitter as well.

Using real-world Twitter and experimental data based on high-profile events, researchers at the University of Nevada, Reno; Tulane University, and the University of Texas at Austin, found that heightened causal uncertainty increases individuals’ liking and sharing of messages that consist of more abstract (versus concrete) language.

Abstract language favored on social media in the aftermath of high-profile events associated with causal uncertainty. Image credit: MikeRenpening | Free image via Pixabay

Abstract language favored on social media in the aftermath of high-profile events associated with causal uncertainty. Image credit: MikeRenpening | Free image via Pixabay

“Think about people’s reaction after the Sandy Hook shooting,” Jae-Eun Namkoong, principal investigator and assistant professor of marketing at the University of Nevada, Reno’s College of Business, said. “People were shocked. They wanted to know not just about what happened, but also about why it happened. And, since understanding something like a mass shooting is a complex process, it can be impossible to pin down just one particular cause to blame.”

The research, “Responding to Causal Uncertainty in the Twitterverse: When Abstract Language and Social Prominence Increase Message Engagement,” published in the “Journal of Interactive Marketing,” examines what types of language are more positively received during times of heightened causal uncertainty and the role of the message source, in the context of social media communication.

“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,” Namkoong said. “This is also reflected in the U.S. government’s investment in studying how to better utilize social media for managing crises.”

What Namkoong and her colleagues found was that, in communicating events associated with high causal uncertainty, abstract messages were liked and shared more often on Twitter, and that this effect was especially pronounced when the messages were from socially influential sources. This research is a part of a larger body of work that investigates the relationship between causal uncertainty and abstract thinking. In their earlier work, the authors had discovered that abstract thinking makes people focus on fewer, more essential causes, which, in turn, reduces causal uncertainty. They had also found that causal uncertainty makes people want to think more abstractly.

“This research goes beyond what we found before and really highlights the role of leaders and how they address events associated with causal uncertainty, such as national tragedies and business disasters,” Namkoong said.

“During times of causal uncertainty, messaging – particularly from leaders and influencers – needs to be simple,” Marlone Henderson, associate professor of psychology at the University of Texas at Austin, said. “But simple, doesn’t mean uninformative and vague. Our research shows that abstract, general statements that are on-point and include key takeaways about why events happened are the kind of messages on social media that receive positive public responses.”

The research is the first of its kind to examine how one’s preference for abstract cognitive process is manifested in social interactions. The first two studies provided correlational evidence by observing consumers’ real social media activities. Namkoong and her colleagues focused on nine high-profile events in spring/summer of 2016 including the Orlando nightclub shooting, Brexit, and the Nice terrorist attack to name a few. All events were unexpected, which is a key factor that induces causal uncertainty.

“Another good indicator of high causal uncertainty is the extent to which people seek answers to the question ‘why,’ the simplest word people use to express causal uncertainty,” Joon Ro, assistant professor of marketing at Tulane University, and the researcher who collected the Twitter data, said. “For example, we saw the Google search term ‘why’ (e.g., ‘why Brexit’) spike after each event’s origination.”

In addition to the nine high causal uncertainty evets, researchers collected Twitter data in response to Hurricane Matthew, the biggest hurricane of 2016, as a control event. Like many of the events in the main data, the hurricane was negative; but unlike those events, the hurricane was completely forecasted, and hence, causal uncertainty was likely to be low. Language abstractness had no impact on message engagement in the control data.

These findings were complemented with experimental studies, designed around a real-world event, the Washington, D.C. Dec. 18, 2017 Amtrak train derailment. Researchers manipulated low or high causal uncertainty surrounding the train derailment by asking participants to elaborate on what they did or did not understand, respectively, about the incident in terms of why it happened. They were then shown either an abstract or concrete tweet about the incident. In another experiment, participants were also told that the tweet originated from someone with either high social influence (e.g., manager or leader) or low social influence (e.g., regular group member). These experiments confirmed what was discovered in the real Twitter data: people found abstract messages from others more appealing, an effect that was especially impactful when the message source had high social influence.

“Using both real-world and experimental data, we offer 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. “Specifically, we demonstrated across a wide range of events that individuals in situations of heightened causal uncertainty found abstract messages from others more appealing, suggesting that the goal to reduce causal uncertainty through abstract processing is pursued through social interactions.”

Namkoong and her colleagues believe this research has a number of relevant business implications.

“Businesses have to communicate with consumers and shareholders about events, many of which are associated with high causal uncertainty,” Namkoong said. “Such events may include business transgressions, disasters, accidents, unexpected performance outputs, and so on. The fact that a growing number of managers, including CEOs, are active on social media makes our findings all the more important to consider.”

Ro emphasized the effect sizes.

“The effect sizes we found with our Twitter data were substantial—increasing the abstractness of a tweet by one standard deviation increased the average number of ‘likes’ tweets receive by 10 percent,” Ro said. “This number jumped even higher when the tweet originated from an influential source.”

Source: University of Nevada, Reno

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