"I Been Taking Adderall Mixing it With Lean, Hope I Don't Wake Up Out My Sleep": Harnessing Twitter to Understand Nonmedical Prescription Stimulant Use among Black Women and Men Subscribers
Webster, J.-L.; Lakamana, S.; Ge, Y.; Sarker, A.
Show abstract
Black women and men outpace other races for stimulant-involved overdose mortality despite lower lifetime use. Growth in mortality from prescription stimulant medications is increasing in tandem with prescribing patterns for these medications. We used Twitter to explore nonmedical prescription stimulant use (NMPSU) among Black women and men using emotion and sentiment analysis, and topic modeling. We applied the NRC Lexicon and VADER dictionary, and LDA topic modeling to examine feelings and themes in conversations about NMPSU by gender. We paid attention to the ability of natural language processing techniques to detect differences in emotion and sentiment among Black Twitter subscribers given increased mortality from stimulants. We found that, although emotion and sentiment outcomes match the directionality of emotions and sentiment observed (i.e., Black Twitter subscribers use more positive language in tweets), this belies limitations of NRC and VADER dictionaries to distinguish feelings for Black people. Even still, LDA topic models showcased the relevance of hip-hop, dependence on NMPSU, and recreational use as consequential to Black Twitter subscribers discussions. However, gender shaped the relevance of these topics for each group. Greater attention needs to be paid to how Black women and men use social media to discuss important topics like drug use. Natural language processing methods and social media research should include larger proportions of Black, Hispanic/Latinx, and American Indian populations in development of emotion and sentiment lexicons, otherwise outcomes regarding NMPSU will not be generalizable to populations writ large due to cultural differences in communication about drug use online.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Which social media platforms facilitate monitoring the opioid crisis? 93%
- Virtual and remote opioid poisoning education and naloxone distribution programs: a scoping review 90%
- Exploring perspectives on digital smoking cessation just-in-time adaptive interventions: A focus group study with adult smokers and smoking cessation professionals 90%
Similar papers in this journal
- Twitter Discourse on Nicotine as Potential Prophylactic or Therapeutic for COVID-19 92%
- Social-spatial network structures among young urban and suburban persons who inject drugs in a large metropolitan area 91%
- A qualitative study exploring the impact of the COVID-19 pandemic on People Who Inject Drugs (PWID) and drug service provision in the UK: PWID and service provider perspectives 91%
Similar papers in this journal
- Use of the Internet and digital devices among people with severe mental ill health during the COVID-19 pandemic restrictions 92%
- Priorities for Future Research about Screen Use and Adolescent Mental Health: A Participatory Prioritization Study 92%
- Passive sensing data predicts stress in university students: A supervised machine learning method for digital phenotyping 91%
Similar papers in this journal
- Fentanyl, Heroin, and Methamphetamine-Based Counterfeit Pills Sold at Tourist-Oriented Pharmacies in Mexico: An Ethnographic and Drug Checking Study 91%
- Associations between ZIP code-level alcohol outlet density and binge drinking among people who inject drugs in 22 US Metropolitan Areas 91%
- Associations between vaping and Covid-19: cross-sectional findings from the HEBECO study 91%
Similar papers in this journal
- Social media discourse and internet search queries on cannabis as a medicine: A systematic review 94%
- Can Psychedelic Use Benefit Meditation Practice? Examining Individual, Psychedelic, and Meditation-Related Factors 92%
- Sex and Gender Influences on Problematic Cannabis Use and Cannabis Use Disorder: A Scoping Review Protocol 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.