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Bio: Martin Saveski is a postdoctoral scholar at the Management Science and Engineering department at Stanford University. He completed his Ph.D. from MIT in September 2020. Martin’s broad research area is Computational Social Science. He uses Causal Inference and Social Network Analyses to study pressing social problems online, such as political polarization and toxicity. He has also made methodological contributions in the areas of causal inference in networks, and recommender systems. Previously, he has interned at Facebook, LinkedIn, Amazon, and Yahoo. His work has been covered by major media outlets, including the New York Times, NPR, MIT Tech Review, and others.

Talk Title: Engaging Politically Diverse Audiences on Social Media

Talk Abstract: In this talk, I will present our study of how political polarization is reflected in the language used by media outlets to promote their content online and what we can do to reduce it. We tracked the Twitter posts of several media outlets over the course of more than three years (566K tweets), and the engagement with these tweets from other users (104M retweets). We then used this data to model the relationship between the tweet text and the political diversity of the audience. We built a tool that integrates our models and helps journalists craft tweets that are engaging to a politically diverse audience, guided by the model predictions. To test the real-world impact of the tool, we partnered with the award-winning PBS documentary series Frontline and ran a series of advertising experiments on Twitter testing how tens of thousands of users respond to the tweets. We found that in seven out of the ten experiments, the tweets selected by our model were indeed engaging to a more politically diverse audience, illustrating the effectiveness of our tool. I will close by discussing the methodological challenges and opportunities in using advertisements to test interventions on social media platforms.