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This study aims to understand the impact of underlying theme in the communication on social media on funding received by early-stage start-ups.

The study is based on empirical testing of data of 849 start-ups and more than 130K tweets. Machine learning (ML) model has been used for text classification of 130K+ tweets. Causal mediation analysis with bootstrapping is carried out for hypothesis testing.

Tweets addressing quality-related uncertainty are a predictor of amount of funds raised. Audience response acts as a mediator between tweets focusing on relational orientation and amount of funds raised.

The authors advance signaling theory by theorizing and investigating the importance of signal content. Endogenous signal of quality directly influences the start-ups outcomes, while exogenous signal helps disseminate information and influence the success.

Entrepreneurs should put in concerted effort to reduce uncertainty about the start-ups. Value creation is a central concept for start-ups; however, communicating value should be the dominant part of social media strategy.

Computer-based language processing techniques have amplified the research focused on content. To the best of the authors’ knowledge, this is the first comprehensive study that explores underlying themes of communication of start-ups and their impact on acquiring funds.