Signs & Symptoms of Dextromethorphan Exposure from YouTube

Abstract

Detailed data on the recreational use of drugs are difficult to obtain through traditional means, especially for substances like Dextromethorphan (DXM) which are available over-the-counter for medicinal purposes. In this study, we show that information provided by commenters on YouTube is useful for uncovering the toxicologic effects of DXM. Using methods of computational linguistics, we were able to recreate many of the clinically described signs and symptoms of DXM ingestion at various doses, using information extracted from YouTube comments. Our study shows how social networks can enhance our understanding of recreational drug effects.

Nursing identity and patient-centredness in scholarly health services research: a computational text analysis of PubMed abstracts 1986–2013

“…As of 15 April there were only 1,034 journal papers listed in the database PubMed with the terms “computational linguistics,” “natural language processing,” or “text analytics” in the title or abstract, 655 of which were published since 2008. These suggest that, in health, applications have been diverse and include: 1) exploration of theories in the light of social media [ 55 ]; 2) analyses of clinical texts for the purposes of classification [ 56 ], symptom description [ 57 ] and diagnosis [ 58 ], the analysis and prediction of patient outcomes [ 59 , 60 ] and evaluation of the extent of utilisation of evidence in health practice [ 61 ]; 3) the analysis of healthcare experiences and behaviours using popular media such as YouTube [ 62 ]; and 4) clinical decision support [ 63 ]. This study takes a socio-cultural perspective, focussing on the extent to which patient-centred nursing is under-represented in the discipline of health services as represented in the PubMed database and as such may be described as a form of “evidence surveillance.” In this study, software associated with natural language processing is used to explore a theoretical construct — nursing professional identity as it relates to patient-centredness in the PubMed corpus.…”

Patterns and Perceptions of Dextromethorphan Use in Adult Members of an Online Dextromethorphan Community

“…For example, Chary et al (2014) recently investigated DXM use information such as doses and symptoms through comments on DXM-related YouTube videos, indicating the utility of investigating specific user groups outside traditional data sources such as poison center calls or physician encounters.…”

“…Four distinct, dose-dependent stages of DXM intoxication have been identified, ranging from increased alertness and generalized euphoria in Stage 1 to complete dissociation, hallucinations, delusions, and ataxia in Stage 4 (Romanelli and Smith 2009). Significantly, the language of this stage-based demarcation is mirrored by user descriptions in laboratory settings (Reissig et al 2012) and in commenters on DXM-related YouTube videos (Chary et al 2014). Psychological effects may also include feelings of comfort, improved assessment of oneself, and difficulty in communicating (Degkwitz 1964), with side-effects such as sweating, fatigue, nausea, vomiting, apathy, and insomnia (Ziaee et al 2005).…”

“…The growing need for awareness by medical practitioners of the symptoms of DXM overdose has also been noted, particularly due to the risk of associated psychiatric and central nervous system consequences (Alam, Nelson, and Bastiampillai 2013;Haroz and Greenberg 2006). Increase in recreational DXM use has likely been facilitated by legal access to the drug as well as by information available on the Internet through forums and outlets such as YouTube (Chary et al 2014). Despite the United States Drug Enforcement Administration listing DXM as one of their drugs of concern (Drug Enforcement Administration 2011), few details are known regarding typical use patterns and preferences in non-laboratory settings, or of typical perceptions of DXM among recreational DXM users.…”

Social media analytics for YouTube comments: potential and limitations

“…Examples include community detection based upon connections between users or resources (Jürgens, 2012), and automatic categorisation (Bouman, Drossaert, & Pieterse, 2012), with sentiment polarity being the most common example (Thelwall, Buckley, & Paltoglou, 2011). Using natural language processing techniques, it may also be possible to extract highly specific information, such as the symptoms of illegal drug use at specific dosage levels (Chary, Park, McKenzie, Sun, Manini, & Genes, 2014). Theoretical analyses may also use an analytic approach (e.g., Dynel, 2014).…”