One of the most concerning notions for science communicators, fact-checkers, and advocates of truth, is the backfire effect; this is when a correction leads to an individual increasing their belief in the very misconception the correction is aiming to rectify.
Agent-based models present an ideal tool for interrogating the dynamics of communication and exchange. Such models allow individual aspects of human interaction to be isolated and controlled in a way that sheds new insight into complex behavioral phenomena. This approach is particularly valuable in settings beset by confounding factors and mixed empirical evidence.
Research at the intersection of machine learning and the social sciences has provided critical new insights into social behavior. At the same time, a variety of issues have been identified with the machine learning models used to analyze social data.
The internet has become a popular resource to learn about health and to investigate one's own health condition. However, given the large amount of inaccurate information online, people can easily become misinformed. Individuals have always obtained information from outside the formal health care system, so how has the internet changed people's engagement with health information?
Using both survey- and platform-based measures of support, we study how polarization manifests for 4,313 of President Donald Trump’s tweets since he was inaugurated in 2017. We find high levels of polarization in response to Trump’s tweets.
Individuals acquire increasingly more of their political information from social media, and ever more of that online time is spent in interpersonal, peer-to-peer communication and conversation. Yet, many of these conversations can be either acrimoniously unpleasant or pleasantly uninformative. Why do we seek out and engage in these interactions? Who do people choose to argue with, and what brings them back to repeated exchanges?
The spread of fake news on social media became a public concern in the United States after the 2016 presidential election. We examined exposure to and sharing of fake news by registered voters on Twitter and found that engagement with fake news sources was extremely concentrated.
OPINION — At the end of the movie “The Candidate,” Robert Redford’s character wins a Senate seat, and then immediately pulls aside his most trusted adviser and asks, “What do we now?” After the divisive election of 2018, we imagine that many newly elected members of Congress are pondering the same question.
There is a growing consensus that online platforms have a systematic influence on the democratic process. However, research beyond social media is limited. In this paper, we report the results of a mixed-methods algorithm audit of partisan audience bias and personalization within Google Search.
People influence each other when they interact to solve problems. Such social influence introduces both benefits (higher average solution quality due to exploitation of existing answers through social learning) and costs (lower maximum solution quality due to a reduction in individual exploration for novel answers) relative to independent problem solving.