TY - JOUR
T1 - Unraveling dental caries misinformation
T2 - Identifying predictive factors for engagement on Instagram
AU - Menezes, Tamires Sá
AU - Jucá, Ana Maria
AU - Jorge, Olívia Santana
AU - Lotto, Matheus
AU - Ayala Aguirre, Patricia Estefania
AU - Cruvinel, Thiago
N1 - Publisher Copyright:
© The Author(s) 2024.
PY - 2024/1/1
Y1 - 2024/1/1
N2 - Objective: This study aimed to identify predictive factors for engagement with dental caries-related posts on Instagram. Methods: Using CrowdTangle, 10,000 English-language posts were retrieved based on total interactions. From these, 2063 posts were evaluated using predetermined inclusion criteria, and a final sample of 500 posts was selected. Two independent investigators classified the posts according to dichotomized criteria: author profile (regular or commercial users), sentiment (positive or neutral/negative), motivation (financial or nonfinancial), format (link or photo), aim of content (prevention or treatment), and facticity (information or misinformation). Predictive factors for total interaction and overperforming scores were determined using multiple logistic regression models. Results: Misinformation accounted for 44% of posts. Most posts were shared by regular users (67.8%), used a photo format (61%), and expressed a positive sentiment (73.8%). Prevention-related posts were significantly related to financial motivation, while treatment-related posts were linked to time of publication, neutral/negative sentiment, and photo format. Older posts were positively associated with misinformation (odds ratio (OR) = 1.45). Positive sentiment (OR = 1.71) and regular user profiles (OR = 1.93) were associated with higher total interactions. In contrast, business profiles (OR = 2.39) and posts with neutral/negative sentiment (OR = 1.79) were associated with overperforming scores. Conclusion: Despite a significant amount of misinformation, only sentiment and author profiles were predictive factors for total interaction and overperforming scores in Instagram posts about dental caries.
AB - Objective: This study aimed to identify predictive factors for engagement with dental caries-related posts on Instagram. Methods: Using CrowdTangle, 10,000 English-language posts were retrieved based on total interactions. From these, 2063 posts were evaluated using predetermined inclusion criteria, and a final sample of 500 posts was selected. Two independent investigators classified the posts according to dichotomized criteria: author profile (regular or commercial users), sentiment (positive or neutral/negative), motivation (financial or nonfinancial), format (link or photo), aim of content (prevention or treatment), and facticity (information or misinformation). Predictive factors for total interaction and overperforming scores were determined using multiple logistic regression models. Results: Misinformation accounted for 44% of posts. Most posts were shared by regular users (67.8%), used a photo format (61%), and expressed a positive sentiment (73.8%). Prevention-related posts were significantly related to financial motivation, while treatment-related posts were linked to time of publication, neutral/negative sentiment, and photo format. Older posts were positively associated with misinformation (odds ratio (OR) = 1.45). Positive sentiment (OR = 1.71) and regular user profiles (OR = 1.93) were associated with higher total interactions. In contrast, business profiles (OR = 2.39) and posts with neutral/negative sentiment (OR = 1.79) were associated with overperforming scores. Conclusion: Despite a significant amount of misinformation, only sentiment and author profiles were predictive factors for total interaction and overperforming scores in Instagram posts about dental caries.
KW - eHealth
KW - infodemiology
KW - Instagram
KW - internet
KW - misinformation
KW - oral health
KW - social media
UR - https://www.scopus.com/pages/publications/85212052138
U2 - 10.1177/20552076241299642
DO - 10.1177/20552076241299642
M3 - Artículo
AN - SCOPUS:85212052138
SN - 2055-2076
VL - 10
JO - Digital Health
JF - Digital Health
ER -