Entrepreneurship involves an immense network of activities, linked via collaborations and information propagation. Information dissemination is extremely important for entrepreneurs. Finding influential users with high levels of interaction and connectivity in social media and involving them in information spread helps disseminating the information quickly. Thus, facilitating key entrepreneurial actors to find and collaborate with each other. Identifying and ranking entrepreneurial top influential people is still in infancy. This paper proposes an ERank framework for topic-specific influence theories that are specialized with respect to Twitter. Firstly, it extracts four dimensions to characterize influencers, including user popularity, activity, reliability, and tweet quality. Afterwards, it uses linear combinations of these dimensions to assign influence score to each user. Experimental results on a real-life dataset containing 233,018 Arabic tweets show that ERank successfully ranks 8 out of 10 entrepreneurial influencers. Unlike other existing approaches, ERank doesn’t require any labelled data and has lower computational cost. To ensure the effectiveness and efficiency of ERank, three validation techniques were used (1) to compare the detected influencers with the real-world influencers, (2) to investigate the spread of information of the detected influencers, and (3) to compare the quality of ERank results with other ranking methods.