Airports don’t bring out the best in people.
Ten years ago, Ashwin Rajadesingan was traveling and had that thought. Today, he is an assistant professor at the University of Texas at Austin, but back in 2014, Rajadesingan was a student seeking his master’s degree in computer science in the School of Computing and Augmented Intelligence, part of the Ira A. Fulton Schools of Engineering at Arizona State University.
While flying to a technical conference, the computational social scientist observed that his fellow travelers were tired, sometimes cranky and frustrated by flight delays. And they were … pretty sarcastic about it.
“When people are angry or downcast, they’re more likely to use sarcasm,” Rajadesingan says. “But online customer service agents can struggle to detect this. We were watching airline representatives respond to sarcastic social media posts in sometimes unintentionally hilarious ways.”
When he returned to ASU, Rajadesingan began to explore how computer systems could be trained to detect sarcasm and assist corporate customer service agents who work all over the world and must respond quickly. He turned to his faculty mentor, Huan Liu, an ASU Regents Professor and a seminal figure in the development of artificial intelligence, or AI, specializing in data mining and machine learning.
Under Liu’s direction, Rajadesingan and then-doctoral student, Reza Zafarani, co-authored the paper, “Sarcasm Detection on Twitter: A Behavioral Modeling Approach.”
Now, the work has received the WSDM 2025 Test of Time Award. The accolade, given by the Association for Computing Machinery, or ACM, honors research that has maintained its scientific significance after 10 years, recognizing scholarship that has endured.
This March, Liu and Zafarani traveled to Hamburg, Germany, to accept the award at the 18th ACM International Conference on Web Search and Data Mining.
Read the full story on Full Circle.