{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/132825"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/132825","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Measuring pro-social message in job postings using machine learning","abstract":"When searching for jobs, job applicants are not only motivated by monetary compensation alone, the meaning and social effects of the work also matter. Pro-social motivation, the desire to have a positive impact on other people or social collectives also play an important role in job searching. On the other hand, organizations also have many incentives to promote pro-social jobs during the recruiting processes and accordingly design pro-social characteristics in job postings. Using latest machine learning techniques, we could possibly quantify pro-social characteristics in massive amount of job postings and potentially predict pro-social messages advertised in online job postings. In this thesis, we take up the challenge of developing novel measures of pro-social that satisfactorily address the problems identified with existing measures of pro-social. We proposed implementations of two different machine learning approaches to quantitatively measure pro-social messages from over five million online job postings documentation and effectively predict pro-social jobs, with 79% and 94% prediction accuracy yield from methodology I and methodology II respectively. Based on those approaches, we evaluate the model performance and measure correlation of industries' use of pro-social messages in job postings to compare the effectiveness of two models on several metrics.","abstract_html":"When searching for jobs, job applicants are not only motivated by monetary compensation alone, the meaning and social effects of the work also matter. Pro-social motivation, the desire to have a positive impact on other people or social collectives also play an important role in job searching. On the other hand, organizations also have many incentives to promote pro-social jobs during the recruiting processes and accordingly design pro-social characteristics in job postings. Using latest machine learning techniques, we could possibly quantify pro-social characteristics in massive amount of job postings and potentially predict pro-social messages advertised in online job postings. In this thesis, we take up the challenge of developing novel measures of pro-social that satisfactorily address the problems identified with existing measures of pro-social. We proposed implementations of two different machine learning approaches to quantitatively measure pro-social messages from over five million online job postings documentation and effectively predict pro-social jobs, with 79% and 94% prediction accuracy yield from methodology I and methodology II respectively. Based on those approaches, we evaluate the model performance and measure correlation of industries' use of pro-social messages in job postings to compare the effectiveness of two models on several metrics.","abstract_has_math":false,"creators":["Hong, Zhuoqiao."],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Engineering and Management Program","school":null,"contributors":[],"advisors":["Nathan Wilmers."],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020","date_published":"2020","updated_at":"2026-07-22T22:21:06Z","subjects":["Engineering and Management Program.","System Design and Management Program."],"languages":["eng"],"rights":["MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/132825","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Nathan Wilmers."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. 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Pro-social motivation, the desire to have a positive impact on other people or social collectives also play an important role in job searching. On the other hand, organizations also have many incentives to promote pro-social jobs during the recruiting processes and accordingly design pro-social characteristics in job postings. Using latest machine learning techniques, we could possibly quantify pro-social characteristics in massive amount of job postings and potentially predict pro-social messages advertised in online job postings. In this thesis, we take up the challenge of developing novel measures of pro-social that satisfactorily address the problems identified with existing measures of pro-social. We proposed implementations of two different machine learning approaches to quantitatively measure pro-social messages from over five million online job postings documentation and effectively predict pro-social jobs, with 79% and 94% prediction accuracy yield from methodology I and methodology II respectively. Based on those approaches, we evaluate the model performance and measure correlation of industries' use of pro-social messages in job postings to compare the effectiveness of two models on several metrics."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M. in Engineering and Management"]},{"key":"dc:title","label":"Title","values":["Measuring pro-social message in job postings using machine learning"]}]}],"canonical_facts":{"dc:contributor.advisor":["Nathan Wilmers."],"dc:contributor.department":["Massachusetts Institute of Technology. Engineering and Management Program","SysDes"],"dc:contributor.other":["Massachusetts Institute of Technology. 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Using latest machine learning techniques, we could possibly quantify pro-social characteristics in massive amount of job postings and potentially predict pro-social messages advertised in online job postings. In this thesis, we take up the challenge of developing novel measures of pro-social that satisfactorily address the problems identified with existing measures of pro-social. We proposed implementations of two different machine learning approaches to quantitatively measure pro-social messages from over five million online job postings documentation and effectively predict pro-social jobs, with 79% and 94% prediction accuracy yield from methodology I and methodology II respectively. Based on those approaches, we evaluate the model performance and measure correlation of industries' use of pro-social messages in job postings to compare the effectiveness of two models on several metrics."],"dc:description.degree":["S.M. in Engineering and Management"],"dc:identifier.uri":["https://hdl.handle.net/1721.1/132825"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Engineering and Management Program.","System Design and Management Program."],"dc:title":["Measuring pro-social message in job postings using machine learning"],"dc:type":["Thesis"],"thesis:degree_name":["Master"]},"updated_at":"2026-07-22T22:21:06Z"}