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Leveraging the crawled geotagged and times-tamped tweets of Twitter users, this study develops a methodological framework to predict massively unreported travel mode choices of Twitter users who have left geotagged and timestamped tweets. The prediction framework is based on the similarity between a user without reported mode choice and the users with known travel modes. To appropriately represent a Twitter user’s data, we employ a discretized spatial-temporal probabilistic distribution to characterize the user. A novel convolution-based similarity measure is then proposed to effectively capture the interdependencies of both spatially and temporally adjacent data points. A graph inference model is further established to explore the predictability of people’s travel mode choice. To validate the prediction framework, we use the Proposition 1 incident in Austin, TX in 2016 as a case study and leverage relevant data crawled from Twitter. The prediction results validate the effectiveness of both the convolution-based similarity measure and the prediction framework. This work demonstrates the feasibility of using social media data to predict people’s mobility choices.