A tool to assist researchers in tracking mis/disinformation and providing rapid response
In recent years there has been an abundance of mis/disinformation around US elections on social media sites like Twitter. Research collectives such as the Election Integrity Partnership investigated this information in real time to analyze and disseminate important details across election stakeholders. However, not all researchers have the quantitative skill sets necessary to access this data at scale. Our project improves the research process of qualitative researchers by creating a Python Jupyter Notebook that allows researchers to gain insight from datasets relevant to the US election.
This project is sponsored by the University of Washington’s Center for an Informed Public (UW CIP). Mike Caulfield is the main point of contact for this sponsored project, who is a Research Scientist for the UW CIP and is working with the Election Integrity Partnership (EIP). Alongside him, there is Emily Porter, who is a PhD student with an expertise in Information Science working with the UW CIP.
We developed a comprehensive Python Jupyter Notebook that includes features to assist qualitative researchers in their research process. These features are sentiment analysis, search, standardize timezones, popular tweets, and filters (retweets, date, and time).
Find positive, negative and neutral tweets related to keyword/phrase/hashtag
Find tweets related to a term and/or exclude term(s) using vectorization
Convert timezones in the dataset (e.g., GMT to PST)
Filter to find tweets by date and time
Filter to find if a tweet was retweeted or not
Count the number of times a tweet was retweeted
Product Manager
UX/Front-End Dev.
Data Scientist
Data Scientist/Back-End Dev.
As of May 21st, 2022, the project has been transferred to the UW CIP for further development. For any questions, please contact Mike Caulfield at mica42@uw.edu.
