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In this paper the authors use Twitter data and a recently developed algorithm at the University of California Santa Barbara to extract Origin-Destination pairs in the Greater Los Angeles metropolitan area known as the Southern California Association of Governments (SCAG) region. This algorithm contains two steps: individual-based trajectory detection and place-based trip aggregation. In essence, if a person tweeted in different TAZs within 4 hours, it is considered to be one OD-trip. The extracted OD-trips were aggregated into 30 minute intervals. Then, the authors compare these trips with a traditional travel demand model (SCAG, 2012, 4-step model). Substantial spatial heterogeneity is found and a variety of social factors including the tweeting demographics. In this paper the authors illustrate the results from a spatially autoregressive regression model and a three-class latent class regression model that convert tweet derived trips to four-step trips accounting for zonal and trip-maker heterogeneity. In these regression models the authors use measures of business density and diversity, and population density as added explanatory/control variables, so that a unit contribution of a tweet trip can be adjusted by land-use effects and the trip producing zones in the twitter data can be explained in a more complete way. Preliminary results are encouraging and show the usefulness of harvested large-scale mobility data from location-based social media. The results also show the added value of latent class regression models in this experiment. The paper concludes with a review of next steps.