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Warning: It’s a scam!! Towards understanding the Employment Scams using Knowledge Graphs

Published: 04 January 2023 Publication History

Abstract

Employment scams, such as scapegoat positions, clickbait and non-existing jobs, etc., are among the top five scams registered over online platforms.1 Generally, scam complaints contain heterogeneous information (money, location, employment type, organization, email, and phone number), which can provide critical insights for appropriate interventions to avoid scams. Despite substantial efforts to analyze employment scams, integrating relevant scam-related information in structured form remains unexplored. In this work, we extract this information and construct a large-scale Employment Scam Knowledge Graph consisting of 0.1M entities and 0.2M relationships. Our findings include discovering different modes of employment scams, entities, and relationships among entities to alert job seekers. We plan to extend this work by utilizing a knowledge graph to identify and avoid potential scams in the future.

References

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Dieter Fensel, Umutcan Şimşek, Kevin Angele, Elwin Huaman, Elias Kärle, Oleksandra Panasiuk, Ioan Toma, Jürgen Umbrich, and Alexander Wahler. 2020. Introduction: what is a knowledge graph?In Knowledge Graphs. Springer, 1–10.
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Deanna Grant-Smith, Alicia Feldman, and Cassandra Cross. 2022. Key trends in employment scams in Australia: What are the gaps in knowledge about recruitment fraud. QUT Centre for Justice Briefing Papers 21 (2022).
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Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia d’Amato, Gerard De Melo, Claudio Gutierrez, Sabrina Kirrane, José Emilio Labra Gayo, Roberto Navigli, Sebastian Neumaier, 2021. Knowledge graphs. ACM Computing Surveys (CSUR) 54, 4 (2021), 1–37.
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Syed Mahbub, Eric Pardede, and ASM Kayes. 2022. Online Recruitment Fraud Detection: A Study on Contextual Features in Australian Job Industries. IEEE Access 10(2022), 82776–82787.
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Alexandrea J Ravenelle, Erica Janko, and Ken Cai Kowalski. 2022. Good jobs, scam jobs: Detecting, normalizing, and internalizing online job scams during the COVID-19 pandemic. New Media & Society 24, 7 (2022), 1591–1610.
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Thomas Rebele, Fabian Suchanek, Johannes Hoffart, Joanna Biega, Erdal Kuzey, and Gerhard Weikum. 2016. YAGO: A multilingual knowledge base from wikipedia, wordnet, and geonames. In International semantic web conference. Springer, 177–185.
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Xavier Schmitt, Sylvain Kubler, Jérémy Robert, Mike Papadakis, and Yves LeTraon. 2019. A replicable comparison study of NER software: StanfordNLP, NLTK, OpenNLP, SpaCy, Gate. In 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS). IEEE, 338–343.
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Jim Webber and Ian Robinson. 2018. A programmatic introduction to neo4j. Addison-Wesley Professional.

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  1. Warning: It’s a scam!! Towards understanding the Employment Scams using Knowledge Graphs

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    CODS-COMAD '23: Proceedings of the 6th Joint International Conference on Data Science & Management of Data (10th ACM IKDD CODS and 28th COMAD)
    January 2023
    357 pages
    ISBN:9781450397971
    DOI:10.1145/3570991
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 04 January 2023

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    Author Tags

    1. Employment Scams
    2. Fraudulent
    3. Imposters
    4. Information Extraction
    5. Knowledge Graphs
    6. complaints

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    • Extended-abstract
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    CODS-COMAD 2023

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