Evidence map›Paper›PMID 36532978›Full record

ArticleFrontiers in psychology2022

What drives me to use TikTok: A latent profile analysis of users' motives.

Li Gu, Xun Gao, Yong Li

Abstract read
In one paragraph

Article in Frontiers in psychology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
  4. Does TikTok Addiction exist? A qualitative study.Health psychology research · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Li GuSchool of Innovation Design, Guangzhou Academy of Fine Arts, Guangzhou, China.
Xun GaoSchool of Innovation Design, Guangzhou Academy of Fine Arts, Guangzhou, China.
Yong LiSchool of Innovation Design, Guangzhou Academy of Fine Arts, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With TikTok rising in social media, the heterogeneity of users creates diversity in motives for using it. The issue of which profiles of TikTok use motives can be identified warrants greater understanding. Conducting latent profile analyses for a sample of 384 TikTok users, we identified four profiles, namely overall low motives, overall medium motives, overall high motives, and escapist addiction and novelty motives profiles. The former three profiles reflect different levels of motivations across four motives (socially rewarding self-presentation, trendiness, escapist addiction, and novelty). It is worth noting that TikTok users in the escapist addiction and novelty motives profile are mainly motivated by escapist addiction and novelty, but not socially rewarding self-presentation or trendiness motives. Through multivariate analysis of variance (MANOVA) and multinomial logistic regression, we further explore the differences in TikTok use between the profiles and the extent to which users' background characteristics and TikTok use predict their profile membership. The four profiles differ significantly in terms of the scores of almost all TikTok use motives. The frequency of TikTok use and video posting are the most notable predictors of profile membership. These results make theoretical contributions to the extant literature on social media use profiles by exploring the number and types of latent profiles of TikTok use motives, which also inform opportunities for enhancing user experience and imply tailored content recommendations to both achieve maximized gratifications and maintain mental wellbeing based on user profiles.

Indexed as

escapist addictionlatent profile analysismotivesTikTok useuses and gratifications theory

Identifiers

PMID36532978
PMCPMC9752051

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.