Sol Messing is a Research Associate Professor at New York University, with the Center for Social Media and Politics. Prior to joining NYU, Messing founded data science research teams at Pew Research Center, Acronym, and Twitter. He has industry experience working on recommender systems, complex experimentation, feature engineering/discovery, algorithm audits, and differential privacy.
Messing’s published work spans digital media and politics, advertising and elections, and Congressional communication. His most widely cited work shows how social signals in social media are likely the most powerful drivers of what we consume online, and how the networks we form on social media have a stronger ideological relationship to what we consume, compared with ranking algorithms (i.e., “filter bubbles”) or individual preferences. His research on Congress consists of numerous works on inter-party criticism and conflict among members on social media, as well as published work and a co-authored book, The Impression of Influence (Princeton University Press, 2014) about the impacts of credit-claiming on the voting public. He has also published work on the consequences of election forecasts and digital alterations to candidate images in advertising. Messing’s most recent work quantifies the impact of an entire digital Presidential advertising campaign in the 2020 election, in one of the largest field experiments ever conducted.
Messing received his PhD in Communication from Stanford University in 2013, earning a Masters of Science in Statistics. He serves on the advisory board of Journal of Quantitative Description, served as Assistant Editor of Political Communication, and founded theJournal of International Policy Solutions.
In response to the Biden-Harris Administration's public request for information on mitigating the risks of AI, we submitted comments highlighting the importance of transparent standards for identifying and labeling AI generated content online.
In response to the European Commission's Digital Services Act, we submitted comments highlighting the importance of data access for independent research and suggested standards for data access mechanisms.