The Social Science and Social Media Collaborative
Have established models of social and political processes lost their predictive power?
Recent events, such as incorrect predictions of the 2016 election outcome and the spread of misinformation, present an opportunity to challenge old models with new sources of data.
The abundance of data from social media presents an opportunity to understand social and political trends better.
But first, researchers must address issues concerning the use of this data. Is it representative? Are users honest about their thoughts? Is the collection and processing of the data unbiased and accurate?
This study incorporates three parallel projects to address these issues and harness the opportunity to use new data to gain a better understanding of social and political phenomena.
Each of the three projects have specific substantive focus areas, but are linked through the use of data science methods, big data resources, and the use of high performance computing.
S3MC member Pamela Davis-Kean is the new Associate Director for Humanities and Social Sciences at the Michigan Institute for Data Science
Josh Pasek will give a talk on Friday February 1, 2019, titled “What Can Tweets Tell Us About Public Opinions? Uncovering the Data Generating Process by Linking Twitter Data with Surveys” that is available for live streaming.
Dr. Bode, member of the S3MC, is named 2019 Provost’s Distinguished Associate Professor at Georgetown University.
On November 27, 2018, S3Mc’s Ceren Budak participated in a panel on fake news organized by the U-M Dissonance Event Series.
Josh Pasek, Stuart Soroka and Mike Traugott of S3MC contributed to a recent study that is conducted in collaboration with SurveyMonkey and The Washington Post. They found that those who love Trump and those who hate him are paying the most attention to him.
A new paper examining the use of Twitter data has been published in Public Opinion Quarterly. The study uses knowledge of the processes generating Twitter data to develop and test hypotheses for when social media and survey data might align, and thus when social media processes may reflect survey measures.