Collaborative Research: HNDS-R: Dynamics and Mechanisms of Information Spread via Social Media

从来没有像今天这样，每个人的指尖都能获得如此多的信息。不幸的是，随着如此多的信息而来的是很多错误的信息，这些信息可以传播给人类，并被他们当作真相。了解信息如何流动及其对人类行为的影响，对于决定如何保护社会免受错误信息、宣传和“假新闻”的影响非常重要。该项目追踪信息是如何在社交媒体渠道上传播的，以及思想、观点和信仰是如何随着传播而变化的。进行这项研究需要结合计算社会科学、计算机科学、社会学和统计学的概念，以了解社交媒体中信息传播的基本原理。该项目开发了一种研究信息传播的新方法，将信息流的几种不同机制结合在一起。这些数据被用来分析信息是如何在社交媒体上传播的。这项研究有两个主要目标：首先，它将发现和预测舆论趋势，并确定用户在社会广泛关注的话题上的两极分化（例如，气候变化或Covid-19大流行）。其次，它将跟踪信息传播，以了解其在形成舆论趋势方面的作用，并确定对其传播和采用重要的因素。研究人员可以访问大量数据，使他们能够建立和测试大规模的信息扩散模型。该项目的成果包括能够理解社交媒体信息流的新计算机算法，以及信息传播和扩散科学研究的新途径。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

There has never been so much information available at everyone’s fingertips than there is today. Unfortunately, with so much information comes a lot of misinformation that can be spread to human populations and adopted by them as the truth. Understanding how information flows and its impact on human behavior is important for determining how to protect society from the effects of misinformation, propaganda, and “fake news.” This project traces how information spreads on social media channels and how ideas, opinions, and beliefs change as they spread. Conducting this research requires combining concepts from computational social sciences, computer science, sociology, and statistics to understand the fundamentals of information spread in social mediaThis project develops a new approach to the study of information diffusion that brings together several different mechanisms for information flow. Together these are used to analyze how information spreads in social media. The research has two main goals: First, it will spot and predict opinion trends and identify users’ polarization on topics of broad interest to society (e.g., climate change or the Covid-19 pandemic). Second, it will track information propagation to understand its role in shaping opinion trends and identify the factors that are important for its spread and adoption. The researchers have access to a large amount of data that permits them to build and test large-scale models of information diffusion. The outcomes of this project include new computer algorithms that are capable of understanding information flow in social media and new avenues for research in the science of information spread and diffusion.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.