Content and trend analysis of user-generated nicotine sickness tweets: A retrospective infoveillance study.

ABSTRACT:

Introduction

Exposure to pro-tobacco and electronic nicotine delivery system (ENDS) social media content can lead to overconsumption, increasing the likelihood of nicotine poisoning. This study aims to examine trends and characteristics of nicotine sickness content on Twitter between 2018-2020.

Methods

Tweets were collected retrospectively from the Twitter Academic Research Application Programming Interface (API) stream filtered for keywords: 'nic sick', 'nicsick', 'vape sick', 'vapesick' between 2018-2020. Collected tweets were manually annotated to identify suspected user-generated reports of nicotine sickness and related themes using an inductive coding approach. The Augmented Dickey-Fuller (ADF) test was used to assess stationarity in the monthly variation of the volume

SUBMITTER: Purushothaman V 

PROVIDER: S-EPMC8919180 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature