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While open-source software has become ubiquitous, its sustainability is in question: without a constant supply of contributor effort, open-source projects are at risk. While prior work has extensively studied the motivations of open-source contributors in general, relatively little is known about how people choose which project to contribute to, beyond personal interest. This question is especially relevant in transparent social coding environments like GitHub, where visible cues on personal pro"le and repository pages, known as signals, are known to impact impression formation and decision making. In this paper, we report on a mixed-methods empirical study of the signals that influence the contributors’ decision to join a GitHub project. We first interviewed 15 GitHub contributors about their project evaluation processes and identified the important signals they used, including the structure of the README and the amount of recent activity. Then, we proceeded quantitatively to test out the impact of each signal based on the data of 9,977 GitHub projects. We reveal that many important pieces of information lack easily observable signals, and that some signals may be both attractive and unattractive. Our findings have direct implications for open-source maintainers and the design of social coding environments, e.g., features to be added to facilitate better project searching experience