论文标题

对Instagram配置文件的AI支持的探索预测了软技能和个性特征,以增强雇用决定

AI-enabled exploration of Instagram profiles predicts soft skills and personality traits to empower hiring decisions

论文作者

Harirchian, Mercedeh, Amin, Fereshteh, Rouhani, Saeed, Aligholipour, Aref, Lord, Vahid Amiri

论文摘要

无论是对科技巨头,华尔街公司还是小型创业公司的工作面试都没关系;所有候选人都希望展示自己的最佳自我,甚至比实际表现更好。同时,招聘人员想知道候选人的真实自我,并发现软技能,证明专家候选人将非常适合任何公司。全球招聘人员通常很难找到这些技能最高水平的员工。数字足迹可以通过提供候选人独特的在线活动来帮助招聘人员,而社交媒体则提供了跟踪人们的最大数字足迹之一。在这项研究中,我们首次表明,可以根据以下列表和其他机器学习算法从Instagram配置文件中自动预测由Instagram配置文件自动预测的各种行为能力。我们还提供有关五大人格特征的预测。模型是根据400位伊朗志愿用户的样本建立的,这些用户回答了在线问卷并提供了他们的Instagram用户名,使我们能够抓取公共资料。我们将几种机器学习算法应用于统一的数据。深度学习模型的表现分别在两级和三级分类中表现出70%和69%的平均精度。通过在社交媒体用户生成的数据中应用AI,可以将大量具有最高软技能的人库以及对求职者进行更准确的评估。

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.

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