It does not matter whether it is a job interview with Tech Giants, Wall Street firms, or a small startup; all candidates want to demonstrate their best selves or even present themselves better than they really are. Meanwhile, recruiters want to know the candidates' authentic selves and detect soft skills that prove an expert candidate would be a great fit in any company. Recruiters worldwide usually struggle to find employees with the highest level of these skills. Digital footprints can assist recruiters in this process by providing candidates' unique set of online activities, while social media delivers one of the largest digital footprints to track people. In this study, for the first time, we show that a wide range of behavioral competencies consisting of 16 in-demand soft skills can be automatically predicted from Instagram profiles based on the following lists and other quantitative features using machine learning algorithms. We also provide predictions on Big Five personality traits. Models were built based on a sample of 400 Iranian volunteer users who answered an online questionnaire and provided their Instagram usernames which allowed us to crawl the public profiles. We applied several machine learning algorithms to the uniformed data. Deep learning models mostly outperformed by demonstrating 70% and 69% average Accuracy in two-level and three-level classifications respectively. Creating a large pool of people with the highest level of soft skills, and making more accurate evaluations of job candidates is possible with the application of AI on social media user-generated data.
翻译:无论是与Tech Giants、华尔街公司、华尔街公司、还是小的创业者进行工作面试,这无关紧要;所有候选人都希望展示自己最优秀的自我,甚至表现得比实际要好。与此同时,招聘者希望了解候选人真实的自我,并发现能够证明专家候选人在任何公司中非常适合的软技能。世界各地的招聘者通常都在努力寻找具有这些技能最高水平的员工。数字足迹可以帮助招聘者,为候选人提供一套独特的在线活动,而社交媒体则提供最大的数字足迹之一,以跟踪人们。在本研究中,我们第一次展示了由16种需要的软技能组成的广泛行为能力,这些能力可以根据Instagram简介自动预测候选人的真实自我,并用机器学习算法和其他定量特征,证明专家候选人的大小。根据400个伊朗自愿用户用户的抽样,他们回答在线问卷,并提供了他们的Instagram用户名,从而使我们能够了解公众的概况。我们用几套机器学习算法对统一的软媒体级别数据进行了数级的计算。深层次的模型大多为超过70 %,以展示了社会级别上最高等级的数据。