Matching Recruiters and Jobseekers on Twitter

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OriginalspracheEnglisch
Titel des Sammelwerks2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
Herausgeber/-innenMartin Atzmuller, Michele Coscia, Rokia Missaoui
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers Inc.
Seiten266-269
Seitenumfang4
ISBN (elektronisch)978-1-7281-1056-1
ISBN (Print)978-1-7281-1057-8
PublikationsstatusVeröffentlicht - 2020
Veranstaltung12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020 - Virtual, Online, Niederlande
Dauer: 7 Dez. 202010 Dez. 2020

Abstract

An efficient job recommendation framework needs to recommend an appropriate jobseeker to a recruiter and vice-versa. Prior studies have mostly considered datasets from commercial job portals such as LinkedIn or CareerBuilder. However, these datasets are proprietary and not publicly available. Moreover, these portals charge their clients for offering customized services. Hence, we explore whether publicly available Twitter data can be a viable alternative to commercial job portals. We have extracted 0.76 million job-related tweets. We have manually annotated tweet-pairs from recruiters and jobseekers in the domain of computer science jobs. Next, we have employed Siamese architecture and considered multiple artificial neural network models with different word embeddings. We have achieved around 97% accuracy for some of our models. Our study demonstrates the potential of the Twitter platform for job recommendations.

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Matching Recruiters and Jobseekers on Twitter. / Khatua, Aparup; Nejdl, Wolfgang.
2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). Hrsg. / Martin Atzmuller; Michele Coscia; Rokia Missaoui. Institute of Electrical and Electronics Engineers Inc., 2020. S. 266-269 9381392.

Publikation: Beitrag in Buch/Bericht/Sammelwerk/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

Khatua, A & Nejdl, W 2020, Matching Recruiters and Jobseekers on Twitter. in M Atzmuller, M Coscia & R Missaoui (Hrsg.), 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)., 9381392, Institute of Electrical and Electronics Engineers Inc., S. 266-269, 12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020, Virtual, Online, Niederlande, 7 Dez. 2020. https://doi.org/10.1109/ASONAM49781.2020.9381392
Khatua, A., & Nejdl, W. (2020). Matching Recruiters and Jobseekers on Twitter. In M. Atzmuller, M. Coscia, & R. Missaoui (Hrsg.), 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (S. 266-269). Artikel 9381392 Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASONAM49781.2020.9381392
Khatua A, Nejdl W. Matching Recruiters and Jobseekers on Twitter. in Atzmuller M, Coscia M, Missaoui R, Hrsg., 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). Institute of Electrical and Electronics Engineers Inc. 2020. S. 266-269. 9381392 doi: 10.1109/ASONAM49781.2020.9381392
Khatua, Aparup ; Nejdl, Wolfgang. / Matching Recruiters and Jobseekers on Twitter. 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). Hrsg. / Martin Atzmuller ; Michele Coscia ; Rokia Missaoui. Institute of Electrical and Electronics Engineers Inc., 2020. S. 266-269
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title = "Matching Recruiters and Jobseekers on Twitter",
abstract = "An efficient job recommendation framework needs to recommend an appropriate jobseeker to a recruiter and vice-versa. Prior studies have mostly considered datasets from commercial job portals such as LinkedIn or CareerBuilder. However, these datasets are proprietary and not publicly available. Moreover, these portals charge their clients for offering customized services. Hence, we explore whether publicly available Twitter data can be a viable alternative to commercial job portals. We have extracted 0.76 million job-related tweets. We have manually annotated tweet-pairs from recruiters and jobseekers in the domain of computer science jobs. Next, we have employed Siamese architecture and considered multiple artificial neural network models with different word embeddings. We have achieved around 97% accuracy for some of our models. Our study demonstrates the potential of the Twitter platform for job recommendations.",
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Download

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