Around the world, would-be authoritarian leaders have convinced their supporters to vote away the democracies they claim to cherish. How is this possible? We argue that simply fearing that opposing partisans support democratic backsliding can lead individuals to support it themselves. Would-be authoritarians may then be able to start a self-reinforcing dynamic of democratic backsliding by fostering these fears, which then generate exaggerated fears on the other. Using observational and experimental studies (N=4,400), we present four findings consistent with this account: Republicans and Democrats (1) overestimate opposing partisan willingness to break democratic norms; (2) will support their party breaking democratic norms themselves to the extent that they overestimate willingness by the other side; (3) that experimentally correcting this overestimation reduces support for breaking norms, and (4) increases the likelihood of voting for candidates that uphold democratic norms. Our findings suggest that we can foster democratic stability even in a highly polarized society using interventions that simply correct misperceptions about opposing partisans’ commitment to democratic norms
Online discourse faces challenges in facilitating substantive and productive political conversations. Recent technologies have explored the potential of generative AI to promote civil discourse, encourage the development of mutual understanding in a discussion, produce feedback that enables people to converge in their views, and provide usable citizen input on policy questions posed to the public by governments and civil society. In this paper, we present a framework to help policymakers, technologists, and the public assess potential opportunities and risks when incorporating generative AI into online platforms for discussion and deliberation in order to strengthen democratic practices and help democratic governments make more effective and responsive policy decisions.
Evaluating service delivery needs in data-poor environments presents a particularly difficult problem for policymakers. The places where the need for social services are most acute are often the very same places where assessing policy interventions is the most challenging. This paper uses Twitter data to gain insights into service delivery needs in a data-poor environment. Specifically, it examines the development priorities of citizens in the north- western region of Pakistan between 2007 and 2020, using natural language processing techniques (NLP) and sentiment analysis of 9.5 million tweets generated by 20,000 unique Twitter users. The analysis reveals that service delivery priorities in this context are centered on access to education, healthcare, food, and clean water. The findings provide baseline data for future on-the-ground research and development initiatives. In addition, the methodology used in this paper demonstrates both current resources and areas in need of future work in the use of NLP techniques in analyzing social media data in other contexts.
No AccessPolicy Research Working Papers29 Mar 2021Using Twitter to Evaluate the Perception of Service Delivery in Data-Poor EnvironmentsAuthors/Editors: Alia Braley, Samuel P. Fraiberger, Emcet O. TaşAlia Braley, Samuel P. Fraiberger, Emcet O. Taşhttps://doi.org/10.1596/1813-9450-9575SectionsAboutPDF (1.5 MB) ToolsAdd to favoritesDownload CitationsTrack Citations ShareFacebookTwitterLinked In Abstract: Evaluating service delivery needs in data-poor environments presents a particularly difficult problem for policymakers. The places where the need for social services are most acute are often the very same places where assessing policy interventions is the most challenging. This paper uses Twitter data to gain insights into service delivery needs in a data-poor environment. Specifically, it examines the development priorities of citizens in the north- western region of Pakistan between 2007 and 2020, using natural language processing techniques (NLP) and sentiment analysis of 9.5 million tweets generated by 20,000 unique Twitter users. The analysis reveals that service delivery priorities in this context are centered on access to education, healthcare, food, and clean water. The findings provide baseline data for future on-the-ground research and development initiatives. In addition, the methodology used in this paper demonstrates both current resources and areas in need of future work in the use of NLP techniques in analyzing social media data in other contexts. Previous bookNext book FiguresreferencesRecommendeddetails View Published: March 2021 Copyright & Permissions KeywordsSOCIAL MEDIATWITTERSERVICE DELIVERYNATURAL LANGUAGE PROCESSINGSENTIMENT ANALYSISACCESS TO EDUCATION PDF DownloadLoading ...
Scholars warn that partisan divisions in the mass public threaten the health of American democracy. We conducted a megastudy (n = 32,059 participants) testing 25 treatments designed by academics and practitioners to reduce Americans’ partisan animosity and antidemocratic attitudes. We find that many treatments reduced partisan animosity, most strongly by highlighting relatable sympathetic individuals with different political beliefs or by emphasizing common identities shared by rival partisans. We also identify several treatments that reduced support for undemocratic practices – most strongly by correcting misperceptions of rival partisans’ views or highlighting the threat of democratic collapse – which shows that antidemocratic attitudes are not intractable. Taken together, the study’s findings identify promising general strategies for reducing partisan division and improving democratic attitudes, shedding theoretical light on challenges facing American democracy.
Abstract Around the world, citizens are voting away the democracies they claim to cherish. Why are they voting against their own values? In this article, we provide evidence that this behavior is driven in part by fear that their opponents might dismantle democracy first. In an observational study (N=1,973), we find that US partisans who most fear the other party’s willingness to subvert democracy are also those most willing to support subverting democracy themselves. In an experimental study (N=2,543), we use an intervention to reduce these often exaggerated fears. With these fears reduced, partisans become more committed to upholding democratic norms. They also become more willing to vote against candidates of their own party who break these norms. The findings suggest that we can foster democratic stability by strengthening trust in opposing partisans’ commitment to democracy.
