Nonverbal behavior coding is typically conducted by ‘‘hand’’. To remedy this time and resource intensive undertaking, we illustrate how nonverbal social sensing, defined as the automated recording and extracting of nonverbal behavior via ubiquitous social sensing platforms, can be achieved. More precisely, we show how and what kind of nonverbal cues can be extracted and to what extent automated extracted nonverbal cues can be validly obtained with an illustrative research example. In a job interview, the applicant’s vocal and visual nonverbal immediacy behavior was automatically sensed and extracted. Results show that the applicant’s nonverbal behavior can be validly extracted. Moreover, both visual and vocal applicant nonverbal behavior predict recruiter hiring decision, which is in line with previous findings on manually coded applicant nonverbal behavior. Finally, applicant average turn duration, tempo variation, and gazing best predict recruiter hiring decision. Results and implications of such a nonverbal social sensing for future research are discussed.