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

从来没有像今天这样，每个人都可以获得如此多的信息。不幸的是，伴随着如此多的信息而来的是大量的错误信息，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确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。 该项目追踪信息如何在社交媒体渠道上传播，以及思想，观点和信仰如何在传播过程中发生变化。 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，以了解社会媒体中信息传播的基本原理。该项目开发了一种新的方法来研究信息传播，汇集了几种不同的信息流动机制。 这些都被用来分析信息如何在社交媒体中传播。 这项研究有两个主要目标：首先，它将发现和预测舆论趋势，并确定用户对社会广泛关注的话题的两极分化（例如，气候变化或新冠肺炎疫情）。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的重要因素。研究人员可以获得大量的数据，使他们能够建立和测试大规模的信息传播模型。 该项目的成果包括能够理解社交媒体中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NSF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.