Table 1 Paraphrased and de-identified example Tweets describing overdose from users geolocated Tribal lands between 2014 and 2015
State | Primary account | Secondary account | Substance | SI | Signal (%) | Paraphrased examples | |
|---|---|---|---|---|---|---|---|
OK | 18 (31.0%) | 8 (13.8%) | 11 (18.9%) | 11 (18.9%) | 26 (45%) | Woke up early because my friend overdosed. Why!? | You overdosed in school. Get out of this conversation |
Wouldn’t it be great if I just overdosed on Xanax | I’d rather overdose than live | ||||||
NC | 10 (17.2%) | 3 (5.1%) | 8 (13.8%) | 8 (13.8%) | 13 (22%) | I hope I overdose, bye all | Way too many community overdoses from K2/Spice in the last month |
CA | 4 (7.0%) | 1 (1.7%) | 3 (5.1%) | 3 (5.1%) | 5 (8.6%) | Hand me the pills to OD | Our teens are dying from methamphetamine |
NE | 2 (3.5%) | – | 1 (1.7%) | – | 2 (3.5%) | I’ve OD and died in the past | Took too much [laundry] softener this time. OD’d |