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321

Title *

Attraction depending on the level of abstraction of the character descriptionsuse asterix (*) to get italics

Authors *

Hiyori Kuge, Kai Otsubo, Kaede Hattori, Mai Urakawa, Yuki YamadaPlease use the format "First name initials family name" as in "Marie S. Curie, Niels H. D. Bohr, Albert Einstein, John R. R. Tolkien, Donna T. Strickland"

Year *

2024

Picture

Abstract *

<p>With the recent expansion of social networking platforms, we find ourselves presented with burgeoning opportunities to evaluate other people by reading their profiles without direct interactions. Therefore, it is important to examine the determinants of interpersonal attraction in such unilateral communication. The uncertainty reduction theory posits that as an individual’s uncertainty diminishes, they will be evaluated as more attractive. While past studies have predominantly examined the relationship between uncertainty and attraction by focusing on the effect of the amount of accessible information, little attention has been paid to the qualitative aspects of information (i.e., how information is described). In light of this, our present study investigates the effect of expression abstractness within profiles on the attraction attributed to the focal individual. Given that concrete expressions contain richer information than abstract counterparts, we hypothesize that (1) a person described in more (positive) concrete terms will be perceived as more attractive, and (2) the level of uncertainty will mediate this effect. To empirically test these hypotheses, we will conduct an online experiment with 1,000 native adult Japanese speakers.</p>

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Keywords (optional)

uncertainty reduction theory, attributional confidence, familiarity, interpersonal attraction

Methods that require specific expertise (optional)

NonePlease indicate the methods that may require specialised expertise during the peer review process (use a comma to separate various required expertises).

Thematic fields *

Social sciences

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Submission date

2022-10-10 09:08:03

Recommender

Chris Chambers

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