Tools, Potential, and Pitfalls of Social Media Screening: Social Profiling in the Era of AI-Assisted Recruiting

Yeqing Kong Georgia Institute of Technology ; Huiling Ding North Carolina State University

Abstract

Employers are increasingly turning to innovative artificial intelligence recruiting technologies to evaluate candidates’ online presence and make hiring decisions. Such social media screening, or social profiling, is an emerging approach to assessing candidates’ social influence, personalities, and workplace behaviors through their publicly shared data on social networking sites. This article introduces the processes, benefits, and risks of social profiling in employment decision making. The authors provide important guidance for job applicants, technical and professional communication instructors, and hiring professionals on how to strategically respond to the opportunities and challenges of automated social profiling technologies.

Journal
Journal of Business and Technical Communication
Published
2024-01-01
DOI
10.1177/10506519231199478
Open Access
Closed
Topics

Citation Context

Cited by in this index (6)

  1. Journal of Business and Technical Communication
  2. College Composition and Communication
  3. Journal of Business and Technical Communication
  4. Journal of Business and Technical Communication
  5. Technical Communication Quarterly
Show all 6 →
  1. Rhetoric Society Quarterly

Cites in this index (3)

  1. Computers and Composition
  2. Computers and Composition
  3. Computers and Composition
Also cites 40 works outside this index ↓
  1. 10.1177/2329490616642247
  2. 10.1089/cyber.2018.0670
  3. 10.1111/jcc4.12072
  4. 10.1080/15332845.2012.668651
  5. 10.1111/1748-8583.12033
  6. 10.1007/978-3-319-29989-1_2
  7. 10.1002/joec.12074
  8. 10.2139/ssrn.2366564
  9. 10.1177/0093650208321782
  10. 10.1007/s11623-006-0140-3
  11. 10.1007/978-1-4020-6914-7_2
  12. 10.1007/978-1-4020-9498-9_14
  13. 10.1108/IJCHM-01-2021-0073
  14. 10.7551/mitpress/8435.001.0001
  15. 10.1093/jla/laz001
  16. 10.1145/3472714.3473697
  17. 10.1371/journal.pone.0201703
  18. 10.1108/ER-04-2015-0072
  19. 10.1007/978-3-319-29989-1_1
  20. 10.1145/2566486.2568045
  21. 10.1007/978-3-031-02145-9
  22. 10.1016/j.cobeha.2017.05.009
  23. 10.1088/1742-6596/1140/1/012043
  24. 10.1109/CAC.2017.8244176
  25. 10.1111/ijsa.12067
  26. 10.18574/nyu/9781479833641.001.0001
  27. 10.1016/j.techsoc.2021.101647
  28. 10.1609/hcomp.v7i1.5281
  29. 10.1108/JEIM-11-2020-0436
  30. 10.1016/j.cogsys.2023.01.006
  31. 10.1080/08832323.2013.848832
  32. 10.1145/3380851.3416774
  33. 10.1177/0149206313503018
  34. 10.1007/978-3-319-29989-1_14
  35. 10.1016/j.clsr.2010.11.009
  36. 10.1111/ijsa.12277
  37. 10.1007/s10672-014-9250-5
  38. 10.1108/PR-12-2019-0680
  39. 10.1108/SHR-07-2018-0051
  40. 10.1007/s10672-021-09372-4
CrossRef global citation count: 20 View in citation network →