Scientists have developed a new computer model that can decode how people use emoticons to express themselves on social media platforms like Facebook.
The computer model — called robust label ranking (ROAR) — may help users and businesses navigate the increasingly complicated way people are expressing how they feel on social media, researchers said.
While Facebook once featured only one official emoticon reaction — the like button — the social media site added five more buttons — love, haha, wow, sad and angry — last year.
“We want to understand the user’s reactions behind these clicks on the emoticons by modeling the problem as the ranking problem – given a Facebook post, can an algorithm predict the right ordering among six emoticons in terms of votes?” said said Jason Zhang, a research assistant at Pennsylvania State University in the US.
“But, what we found out was that existing solutions predict the user’s emotions and their rankings poorly in some times,” said Zhang.
Zhang added that merely counting clicks fails to acknowledge that some emoticons are less likely to be clicked than others, which is called the imbalance issue.
For example, users tend to click the like button the most because it signals a positive interaction and it is also the default emoticon on Facebook.
“When we post something on Facebook, our friends tend to click the positive reactions, usually love, haha, or, simply, like, but they’ll seldom click angry,” said Zhang.
For social media managers and advertisers, who spend billions buying Facebook advertisements each year, this imbalance may skew their analysis on how their content is actually performing on Facebook, said Dongwon Lee, associate professor at Penn State.
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