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Self-descriptions of LinkedIn Users in Different Social Situations

Konferensbidrag (offentliggjort, men ej förlagsutgivet)
Författare Danilo Garcia
Catrin Rappe
Sverker Sikström
Publicerad i Ninth Self Biennial International Conference. Melbourne, Australia. 25-28 September
Publiceringsår 2017
Publicerad vid Psykologiska institutionen
Språk en
Ämnesord LinkedIn; Latent Semantic Analysis; Identity; Self-descriptions
Ämneskategorier Psykologi


Background: As humans we use language to introduce ourselves to other people and provide them with specific information that we want them to use to build their impression of us upon. The situation, however, influences individuals to focus on specific self-schemata when presenting themselves. Up until recently, however, most research has been conducted using predefined words or qualitative approaches to investigate self-presentations. Here, we used computational methods to investigate how individuals’ self-descriptions in a fictive recruitment situation differ from those made in a friendship situation. Method: Participants (N = 451) were recruited from LinkedIn and randomly assigned to generate 10 words they would use to describe themselves for an employer (recruitment-condition) or a friend (friendship-condition). The word-frequency-rate was compared between conditions and to their co-occurrence in natural language (Google’s n-gram database). The words were quantified using the Latent Semantic Analysis algorithm and then compared between conditions. Results: Participants in the recruitment-condition, compared to the friendship-condition, used flexible and loyal more often, while kind and affectionate was used more often by participants in the friendship-condition. Both conditions contained different and similar words that co-occurred more often when compared to natural language. The self-descriptions’ semantic representation was significantly different between conditions. Conclusions: Individuals highlight their competence and diminish their warmth and vice versa depending on the social situations studied here. Moreover, computational methods present a novel and valid approach to assess individuals’ self-presentations in recruitment situations.

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