AI Article Synopsis
- We utilized quantitative semantics to analyze clusters of words in LinkedIn users' self-descriptions aimed at employers and friends.
- Some word clusters effectively distinguished between traits associated with work versus friendship (like "flexible" for work and "caring" for friends).
- Additionally, we identified differences in self-descriptions based on education levels, with high-educated users described as "analytical" while low-educated users used terms like "messy."
Article Abstract
We used quantitative semantics to find clusters of words in LinkedIn users' self-descriptions to an employer or a friend. Some of these clusters discriminated between worker and friend conditions (e.g., flexible vs. caring) and between LinkedIn users with high and low education (e.g., analytical vs. messy).
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| http://dx.doi.org/10.1002/pchj.210 | DOI Listing |
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Article Synopsis
- We utilized quantitative semantics to analyze clusters of words in LinkedIn users' self-descriptions aimed at employers and friends.
- Some word clusters effectively distinguished between traits associated with work versus friendship (like "flexible" for work and "caring" for friends).
- Additionally, we identified differences in self-descriptions based on education levels, with high-educated users described as "analytical" while low-educated users used terms like "messy."
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