Nav: Home

Love actually: Computer model may decode Facebook emoticons

February 06, 2018

While the trusty "like" button is still the most popular way to signal approval for Facebook posts, a computer model may help users and businesses navigate the increasingly complicated way people are expressing how they feel on social media, according to Penn State researchers.

In a study, researchers developed a social emotion mining computer model that one day could be used to better predict people's emotional reactions to Facebook posts, said Jason Zhang, a research assistant in Penn State's College of Information Sciences and Technology. 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 -- in early 2016.

"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 Zhang. "But, what we found out was that existing solutions predict the user's emotions and their rankings poorly in some times."

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. "And this causes the severe imbalance issue."

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 of information sciences and technology. The new model -- which they call robust label ranking, or ROAR -- could lead to better analytic packages for social media analysts and researchers.

"A lot of the commercial advertisements on Facebook are driven by likes," said Lee. "Eventually, if we can predict these emoticons more accurately using six emoticons, we can build a better model that can discern more precise distribution of emotions in the social platforms with only one emoticon -- like -- such as on Facebook before 2016. This is a step in the direction of creating a model that could tell, for instance, that a Facebook posting made in 2015 with a million likes in fact consists only 80 percent likes and 20 percent angry. If such a precise understanding on social emotions is possible, that may impact how you advertise."

The researchers, who will present their findings at the Thirty-Second AAAI Conference on Artificial Intelligence today (Feb. 6) in New Orleans, used an AI technique called "supervised machine learning" to evaluate their newly-developed solution, Lee added. In this study, the researchers trained the model using four Facebook post data sets including public posts from ordinary users, the New York Times, the Wall Street Journal and the Washington Post, and showed that their solution significantly outperformed existing solutions. All four sets of data were analyzed after Facebook introduced the six emoticons in 2016.

The researchers suggest future research may explore the multiple meanings for liking a post.

"Coming up with right taxonomy for the meanings of like is another step in the research," said Lee. "When you click on the like button, you could really be signaling several emotions -- maybe you agree with it, or you're adding your support, or you just like it."
The National Science Foundation and Samsung supported this work.

Penn State

Related Social Media Articles:

Can seeing the Facebook logo make you crave social media?
A new study examined how social media cues such as the Facebook logo may affect frequent and less frequent social media users differently, sparking spontaneous hedonic reactions that make it difficult to resist social media cravings.
People could be genetically predisposed to social media use
Chance York (Kent State University) used a behavior genetics framework and twin study data from the 2013 Midlife in the United States survey, York examined how both environmental and genetic factors contribute to social media use by applying an analytical model called Defries-Fulker Regression.
New survey reveals almost 6 in 10 teens take a break from social media
A new survey reveals that 58 percent of American teens report taking significant breaks from social media, and that many of these breaks are voluntary.
Who are you on social media? New research examines norms of online personas
According to the Pew Research center, the majority of adults on the internet have more than one social networking profile on sites like Facebook, Twitter, and LinkedIn.
Social media tools can reinforce stigma and stereotypes
Researchers have developed new software to analyze social media comments, and used this tool in a recent study to better understand attitudes that can cause emotional pain, stigmatize people and reinforce stereotypes.
Floods and hurricanes predicted with social media
Social media can warn us about extreme weather events before they happen -- such as hurricanes, storms and floods - according to new research by the University of Warwick.
Why is some social media content interpreted as bragging?
People who post personal content on social networking sites such as Facebook and try to present themselves in a positive light may be perceived as bragging, and therefore be less attractive to others, according to a new study published in Cyberpsychology, Behavior, and Social Networking.
Your (social media) votes matter
Tim Weninger, assistant professor of computer science and engineering at the University of Notre Dame, conducted two large-scale experiments on Reddit and the results provide insight into how a single up/down vote can influence what content users see on the site.
Multi-social millennials more likely depressed than social(media)ly conservative peers
Compared with the total time spent on social media, use of multiple platforms is more strongly associated with depression and anxiety among young adults, the University of Pittsburgh Center for Research on Media, Technology and Health found in a national survey.
Computers can take social media data and make marketing personas
Computers may be able to group consumers into marketing segments in real time just by observing how they respond to online videos and other social media data, according to a team of researchers.

Related Social Media Reading:

Best Science Podcasts 2019

We have hand picked the best science podcasts for 2019. Sit back and enjoy new science podcasts updated daily from your favorite science news services and scientists.
Now Playing: TED Radio Hour

Jumpstarting Creativity
Our greatest breakthroughs and triumphs have one thing in common: creativity. But how do you ignite it? And how do you rekindle it? This hour, TED speakers explore ideas on jumpstarting creativity. Guests include economist Tim Harford, producer Helen Marriage, artificial intelligence researcher Steve Engels, and behavioral scientist Marily Oppezzo.
Now Playing: Science for the People

#524 The Human Network
What does a network of humans look like and how does it work? How does information spread? How do decisions and opinions spread? What gets distorted as it moves through the network and why? This week we dig into the ins and outs of human networks with Matthew Jackson, Professor of Economics at Stanford University and author of the book "The Human Network: How Your Social Position Determines Your Power, Beliefs, and Behaviours".