Today we’re joined by Johannes Eichstaedt, an Assistant Professor of Psychology at Stanford University.
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Johannes joined us at the outset of the coronavirus pandemic to discuss his use of Facebook and Twitter data to measure the psychological states of large populations and individuals. In our conversation, we explore how Johannes applies his physics background to a career as a computational social scientist, the differences in communication on social media vs the real world, and what language indicators point to changes in mental health.
We also discuss some of the major patterns in the data that emerged over the first few months of lockdown, including mental health, social norms, and political patterns. We also explore how Johannes built the process, and the techniques he’s using to collect, sift through, and understand the data.
Celebrating 400 Interviews!
You are about to listen to episode 400 of the TWIML AI Podcast, and we’re so happy to be sending it your way. While 400 is an amazing milestone, what’s even more interesting to us is how we grow and evolve over the next 100 episodes to better serve all of you. We’ve already introduced a ton of new programs this year, from video interviews to watch parties to premium courses, and this is just the beginning.
We want to hear your feedback on this occasion. Head to the comments section below and share your story on how the podcast has impacted you, your thoughts on how we can improve, and your tips on who we should interview!
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Connect with Johannes!
- Video: The Psychological Impact of COVID-19
- Video: Measuring the Health of Population through Social Media
- Paper: Estimating geographic subjective well-being from Twitter: A comparison of dictionary and data-driven language methods
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“More On That Later” by Lee Rosevere licensed under CC By 4.0