OBJECTIVES: Intentional and unintentional exposures to electronic nicotine delivery system (ENDS) e-liquids can cause illness and death. In this study, we describe acute nicotine toxicity due to e-liquid exposure (ANTEE) information found on Twitter and contextualize ANTEE experiences to clarify conditions associated with exposure. METHODS: We obtained 20,180 ANTEE-relevant tweets from 2013-2018. We excluded retweets, suspected bots, non-English tweets, tweets not originating in the US, and advertisements. We coded relevant tweets qualitatively using domains for e-liquid exposure tweets and e-liquid-related non-exposure tweets (ie, posts reflecting hypothetical exposure, information about e-liquids). RESULTS: Content analyses were based on 1656 e-liquid exposure tweets and 1210 non-exposure tweets. More than half of exposure tweets (61.3%) were classified as accidental exposures; subjects were predominately young people, assumed to be under age 18 (40.5%), and self (27.7%). The most common exposure route was ingestion (61.1%). Of exposure tweets, 13.9% described health effects and 12.7% described seeking assistance. Most non-exposure tweets were classified as likely or hypothetical exposure (49.9%) or presentation of advice, information, or warnings (40.5%). CONCLUSIONS: Tweets can serve as a novel and complementary data source for learning more about e-liquid exposures.
OBJECTIVES: Intentional and unintentional exposures to electronic nicotine delivery system (ENDS) e-liquids can cause illness and death. In this study, we describe acute nicotine toxicity due to e-liquid exposure (ANTEE) information found on Twitter and contextualize ANTEE experiences to clarify conditions associated with exposure. METHODS: We obtained 20,180 ANTEE-relevant tweets from 2013-2018. We excluded retweets, suspected bots, non-English tweets, tweets not originating in the US, and advertisements. We coded relevant tweets qualitatively using domains for e-liquid exposure tweets and e-liquid-related non-exposure tweets (ie, posts reflecting hypothetical exposure, information about e-liquids). RESULTS: Content analyses were based on 1656 e-liquid exposure tweets and 1210 non-exposure tweets. More than half of exposure tweets (61.3%) were classified as accidental exposures; subjects were predominately young people, assumed to be under age 18 (40.5%), and self (27.7%). The most common exposure route was ingestion (61.1%). Of exposure tweets, 13.9% described health effects and 12.7% described seeking assistance. Most non-exposure tweets were classified as likely or hypothetical exposure (49.9%) or presentation of advice, information, or warnings (40.5%). CONCLUSIONS: Tweets can serve as a novel and complementary data source for learning more about e-liquid exposures.
Entities:
Keywords:
Twitter; e-cigarette; e-liquid exposure; electronic nicotine delivery systems; social media
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