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LinkedIn users are ‘time traveling’ by adding AI keywords to past jobs on their profiles

There’s a new trend online called “LinkedIn time travel,” and it has nothing to do with a DeLorean. The practice refers to users of the job networking site editing their former jobs, mostly to make themselves appear more qualified to hiring managers. According to a report from the National Bureau of Economic Research and Revelio Labs, which examined tens of millions of profiles, the trend seems…

LinkedIn users are ‘time traveling’ by adding AI keywords to past jobs on their profiles

A novel phenomenon on LinkedIn is "time traveling," where users alter their past job listings to appear more attractive to potential employers. This trend, studied by the National Bureau of Economic Research and Revelio Labs, reveals that 19.7% of LinkedIn users modify job titles or descriptions years after leaving the role, typically around four years after departing.

Young job seekers under 30 are the most active, editing 38.1% of their profiles. However, trends vary by job type: AI leadership roles, design, and qualitative research frequently undergo changes, with 56.6% of these profiles edited. MBA holders lead in this practice at 29.3%. The research suggests that time traveling goes beyond typical résumé updates, as it involves falsely adding responsibilities to a job they no longer held.

Gideon Slocum Moore, a researcher involved in the study, explains that this manipulation involves artificially enhancing one's skills to match the current job market demands, even if those skills were never part of the original role. The trend is also linked to a decline in DEI-related keywords, reflecting shifting business attitudes since the Trump presidency, which targeted DEI programs.

While some professions, like wellness practitioners, show minimal engagement in time traveling, the overall trend indicates a concerted effort among job seekers to adapt and stand out in a competitive job market.

Written by urgent.news from Fast Company's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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