During the 2019 Hong Kong Anti-extradition Law (Anti-ELAB) Movement, Twitter has become an extended frontline for opinion leaders and protestors seeking external attention and recognition. This research attempts to explicate how the information about the Anti-ELAB Movement was transmitted on Twitter, how and to what extent these messages mobilize people to respond, and what properties in tweets triggered more subsequent actions. The determinants of mobilization are identified by applying social network algorithms and sentiment analysis to locate the chain and tone of conversations. To address the confounders in texts, Topical Inverse Regression Text-Matching (TIRM) ensures the texts are comparable in causal inference modeling. The findings indicate that external allies played a more significant role in mobilizing individuals than local Hongkongers, both in terms of disseminating information about the Anti-ELAB movement and inspiring new discussions among Twitter users.