LibGuides: Misinformation, Disinformation, and Information Literacy: Research Tools

Research Centers, Organizations, and Useful Websites

Data and Society
Data and Society Research Institute is an independent nonprofit research organization that studies the social implications of data, automation, and AI.
Annenberg Public Policy Center
While well-known for FactCheck.org, a website that directly addresses misinformation of political figures and other influential entities, the APPC research covers a wide variety of topics related to the communication of key issues in health, science, and climate, all hot areas of misinformation.
Pew Research Center
Pew Research Center is a nonpartisan American think tank that conducts public opinion polling, demographic research, and data-driven social science studies. It is particularly valuable for mis-/disinformation research through its comprehensive surveys on Americans' news consumption habits, trust in media sources, and beliefs about false information online.
NORC
NORC at the University of Chicago is an independent research institution that conducts large-scale social science surveys and data collection, offering valuable resources for misinformation research through its rigorous polling methodologies, public opinion studies on media trust and misinformation sources, and longitudinal datasets that track how Americans' beliefs and information consumption patterns change over time.

Inter-University Consortium for Political and Social Research (ICPSR)
Datasets and statistics related to social science and demographics. Since Rice is an ICPSR member institution, Rice students, faculty, and staff can register for a personal password and download information directly from ICPSR using a rice.edu email address.
PubMed @ Rice
Citations from MEDLINE and other life science journals from the 1950s onward; also includes links to free full-text articles and resources like the PubMed Central digital archive.
JSTOR
Online, full text access to scholarly journals from first publication date to a "moving wall" of 2-5 years from present--i.e., the most recent 2-5 years' worth of issues will not be available at any given time. The archived collection includes journals in humanities, social sciences, and natural sciences. The content is searchable by author, title, full-text, discipline, year, and article type.

The Industry Documents Library (IDL)

Hosted at the library of the University of California San Francisco (UCSF), the IDL is a huge collection of internal documents from various industries manufacturing products that are directly impacting public health. These documents are released by the companies as a result of litigation. Analyzing the documents help us understand how these industries are shaping the discourse of health in society, and their roles in marketing, legislation, and policymaking. In particular, it helps people find how some of the common health claims that could be misleading were manufactured by the industry's various marketing strategies. This is a great resource for researchers who want to study the role large companies were playing in making and spreading health mis-/disinformation. Check out their YouTube Channel to learn more.

COVID-19 Fake News Infodemic Research Dataset (CoVID19-FNIR Dataset)
The CoVID-19 FNIR Dataset is a collection of fact-checked, expert-verified news stories about the COVID-19 pandemic. It includes fake news scraped from the Internet and from verified Twitter accounts. The data came from the U.S., India, and Europe between February and June 2020. The dataset is accessible through IEEE DataPort, with a free account.

Do not use AI for critical analysis. AI can help brainstorm or summarize, but the core analysis and interpretation must be your own.
Fact-check everything. LLMs can "hallucinate" and generate incorrect information or fake citations. Always verify.
Protect your data. Do not input sensitive, confidential, or proprietary data into public AI tools.
Avoid Plagiarism. Do not copy and paste AI-generated text directly. Check Rice policies on use and citation.
Understand the Tool's Limitations. Be aware of the biases in the AI's training data, as they can be reflected in the output.
Use as a Starting Point. Treat AI-generated text as a first draft to be revised, edited, and personalized.
Be Transparent. Be prepared to disclose your use of AI in your methodology or acknowledgments as required.