The Internet Doesn’t Have to Be Bad for Democracy

Opinion | This Is What Will Ruin Public Opinion Polling for Good

Instead of navigating the obstacles to conduct polls with human respondents, pollsters are running A.I. simulations instead. Why?

How to Run Surveys: A Guide to Creating Your Own Identifying Variation and Revealing the Invisible

Surveys are an essential approach for eliciting otherwise invisible factors such as perceptions, knowledge and beliefs, attitudes, and reasoning. These factors are critical determinants of social, economic, and political outcomes. Surveys are not merely a research tool. They are also not only a way of collecting data. Instead, they involve creating the process that will generate the data. This allows the researcher to create their own identifying and controlled variation. Thanks to the rise of mobile technologies and platforms, surveys offer valuable opportunities either to study broadly representative samples or to focus on specific groups. This article offers guidance on the complete survey process, from the design of the questions and experiments to the recruitment of respondents and the collection of data to the analysis of survey responses. It covers issues related to the sampling process, selection and attrition, attention and carelessness, survey question design and measurement, response biases, and survey experiments.

Do AI and bogus respondents threaten polling’s future?

Courtney Kennedy, vice president of methods and innovation, answers some common questions about the current polling landscape in the U.S.

Polling Has an AI Respondent Problem

Democracy doesn't know what's coming.

Improving social media for democratic discourse

Social media have expanded opportunities for communication and political participation, but today's dominant platforms are optimized primarily for engagement and advertising revenue, contributing...

Chatbots and the Ballot Box: Evaluating Accuracy, Sourcing, and Language Gaps in AI Answers to Election Questions - Institute for Strategic Dialogue

ISD assesses how reliably six leading AI models provide accurate, complete information on US election procedures and voter access.

Redesigning algorithms to intervene on social norm misperceptions during a national election

For the first time in history, civic discourse commonly occurs in digital environments in which algorithms influence exposure to social information1,2. It is increasingly important to understand whether and how these algorithms affect political discourse3–5. Here we built custom feed-ranking algorithms with full control over their features, and randomly assigned 2,000 participants to use them for 8 weeks (before and after the 2024 US presidential election). We tested whether an engagement-based algorithm (used on major social media platforms6,7) amplifies intergroup, moralized and emotional (IME) information in ways that skew perceptions of social norms around political dialogue5,8, and whether it increased engagement with IME content and perceptions of partisan animosity (compared with a reverse-chronological feed9,10). We also developed and tested a ‘diversified extremity’ algorithm to reduce the influence of extreme users11–13 to improve the accuracy of social norm perception14–16 and reduce perceptions of partisan animosity. We found that engagement-based feeds amplified IME and toxic content relative to reverse-chronological feeds, with the largest increases in moral outrage and political content. Engagement-based feeds also reduced prescriptive norm perception accuracy (albeit in an unexpected direction) and increased perceived partisan animosity. However, they did not significantly alter users’ own engagement behaviours. The diversified extremity algorithm reduced IME and toxic content exposure, improved prescriptive norm accuracy, yet maintained comparable platform enjoyment—suggesting that reducing the influence of extreme users can curb algorithmic distortions without diminishing user experience.

Generative AI Meets Open-Ended Survey Responses: Research Participant Use of AI and Homogenization

The growing popularity of generative artificial intelligence (AI) tools presents new challenges for data quality in online surveys and experiments. This study examines participants’ use of large language models to answer open-ended survey questions and describes empirical tendencies in human versus large language model (LLM)-generated text responses. In an original survey of research participants recruited from a popular online platform for sourcing social science research subjects, 34 percent reported using LLMs to help them answer open-ended survey questions. Simulations comparing human-written responses from three pre-ChatGPT studies with LLM-generated text reveal that LLM responses are more homogeneous and positive, particularly when they describe social groups in sensitive questions. These homogenization patterns may mask important underlying social variation in attitudes and beliefs among human subjects, raising concerns about data validity. Our findings shed light on the scope and potential consequences of participants’ LLM use in online research.

Political Quizzes | The Paper Pilot

The Paper Pilot's digital garden of thoughts on philosophy, politics, and sociology

The social media discourse of engaged partisans is toxic even when politics are irrelevant

Abstract. Prevailing theories of partisan incivility on social media suggest that it derives from disagreement about political issues or from status compet

The social media discourse of engaged partisans is toxic even when politics are irrelevant

Abstract. Prevailing theories of partisan incivility on social media suggest that it derives from disagreement about political issues or from status compet

What’s in a name? A forward view of anonymity online

Demos is Britain’s leading cross-party think-tank. We produce original research, publish innovative thinkers and host thought-provoking events.

The Computational Democracy Project

Tools for deliberative democracy.

David Dill: Why Online Voting Is a Danger to Democracy

​A Stanford computer scientist says Internet voting would be "a complete disaster."