Computational Analysis Of Behavior In Employment Interviews And Video Resumes

Used in nearly every organization, employment interviews are a ubiquitous process where job applicants are evaluated by an employer for an open position. Consisting of an interpersonal interaction between at least one interviewer and a job applicant, they are used to assess interviewee knowledge, skills, abilities, and behavior in order to select the most suitable person for the job at hand. Because they require face-to-face interaction between at least two protagonists, they are inherently social, and all that recruiters have as a basis to forge their opinion is the applicant's behavior during the interview (in addition to his resume); in such settings, first impressions are known to play an important role. First impressions can be defined as snap judgments of others made based on a low amount of information. Interestingly, social psychology research has shown that humans are quite accurate at making inferences about others, even if the information is minimal. Social psychologists long studied job interviews, with the aim of understanding the relationships between behavior, interview outcomes, and job performance. Until recently, psychology studies relied on the use of time-intensive manual annotations by human observers. However, the advent of inexpensive audio and video sensors in the last decade, in conjunction with improved perceptual processing methods, has enabled the automatic and accurate extraction of behavioral cues, facilitating the conduct of social psychology studies. The use of automatically extracted nonverbal cues in combination with machine learning inference techniques has led to promising computational methods for the automatic prediction of individual and group social variables such as personality, emergent leadership, or dominance. In this thesis, we addressed the problem of automatically predicting hirability impressions from interview recordings by investigating three main aspects. First, we explored the use of state-of-the-art computational methods for the automatic extraction of nonverbal cues. As a rationale for selecting the behavioral features to be extracted, we reviewed the psychology literature for nonverbal cues which were shown to play a role in job interviews. While the main focus of this thesis is nonverbal behavior, we also investigated the use of verbal content and standard questionnaire outputs. Also, we did not limit ourselves to the use of existing techniques: we developed a multimodal nodding detection method based on previous findings in psychology stating that head gestures are conditioned on the speaking status of the person under analysis, and results showed that considering the speaking status improved the accuracy. Second, we investigated the use of supervised machine learning techniques for the prediction of hirability impressions in a regression task, and up to 36% of the variance could be explained, demonstrating that the automatic inference of hirability is a promising task. Finally, we analyzed