EurekAlert: Researcher repurposes social networking models to predict COVID spread

EurekAlert: Researcher repurposes social networking models to predict COVID spread. “Since the COVID-19 epidemic began, there has been plenty of opportunity to observe how a vast array of truths, half-truths, and falsehoods can flare up and spread like wildfire across social media, swirl around, and just as quickly get buried and forgotten. It could serve as a fascinating case study for CSL and computer science professor Tarek Abdelzaher, who for years has studied how information propagates through social media. But he and his students Chaoqi Yang and Ruijie Wang have taken a big step further. They recognized that the dissemination of information through a population of online users is closely analogous to the transmission of a virus through a population of flesh-and-blood human beings, and that realization has inspired them to repurpose their information propagation models to predict COVID-19 spread. Furthermore, they have made the findings available to the public […]

Physics: Explaining Bursts of Attention on Social Media

Physics: Explaining Bursts of Attention on Social Media. “Social media are like a giant megaphone for public opinion: they can sway elections, crush a business, or incite mass action on hot-button issues like vaccination and climate change. Researchers studying how a topic grabs ‘collective attention’ have noticed a common feature in social media data: occasional short and seemingly random bursts of high-volume activity. These poorly understood ‘spikes’ are an intrinsic aspect of attention dynamics, says Manlio De Domenico, a network theorist at the Bruno Kessler Foundation (FBK) in Trento, Italy.”