A collaborative team of researchers led by Dale Zhou, who did his Ph.D. at the University of Pennsylvania's Perelman School of Medicine, and Dani Bassett in Penn's School of Engineering and Applied Science looked at how nearly half a million people around the world use Wikipedia's knowledge networks.
A collaborative team of researchers led by Dale Zhou, who did his Ph.D. at the University of Pennsylvania's Perelman School of Medicine, and Dani Bassett in Penn's School of Engineering and Applied Science looked at how nearly half a million people around the world use Wikipedia's knowledge networks. They found stark differences in browsing habits between countries offering insights into cultural differences and potential drivers of curiosity and learning.
"We observed that countries that had greater inequality, in terms of gender and access to education, had people who were browsing with more intent -- seeking closely related information, whereas the people in countries that had more equality were browsing expansively, with more diversity in topics -- jumping from topic to topic and collecting loosely connected information," Bassett says.
Working with Martin Gerlach of the Wikimedia Foundation, who had data of more than two million human browsers,"allowed us to apply our existing methods, and develop new methods, to capture styles of curiosity emerging across 14 different languages of Wikipedia and 50 different countries or territories," Lydon-Staley says.The researchers cite three main hypotheses driving the associations between information-seeking approaches and equality.
"The dancer is someone who moves along a track of information but, unlike the busybody, they make leaps between ideas in a creative, choreographed way," says Zurn."They don't jump randomly; they connect different domains to create something new." "This opens up new research avenues, including the role of biological processes in shaping how we seek information," says Shubhankar Patankar, another author on the paper and a doctoral student in Penn Engineering. He is also keen to understand the implications of the work for AI."Imparting notions of curiosity to AI systems learning from interactions is an increasingly important area of research," Patankar says.
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