This is your Twitter on drugs

Abstract

Twitter can be a rich source of information when one wants to monitor trends related to a given topic. In this paper, we look at how tweets can augment a public health program that studies emerging patterns of illicit drug use. We describe the architecture necessary to collect vast numbers of tweets over time based on a large number of search terms and the challenges that come with finding relevant information in the collected tweets. We then show several examples of early analysis we have done on this data, examining temporal and geospatial trends.

From the total survey error framework to an error framework for digital traces of humans

“…He and Rothschild examine different methods of obtaining relevant political tweets, establishing that bias exists in keywordbased data collection (He and Rothschild, 2016) affecting both the users included in the sample as well as the sentiment of the resultant tweets. Predicting Influenza rates from search queries (Yuan et al, 2013) Understanding the use and effect of psychiatric drugs through Twitter (Buntain and Golbeck, 2015) Attribute Using attributes such as location or community affiliation to subset entities such as users who may have the relevant attribute in their biography…”

Machine Learning and Natural Language Processing for Geolocation-Centric Monitoring and Characterization of Opioid-Related Social Media Chatter

“…Studies have suggested that social media posts mentioning opioids and other abuse-prone substances contain detectable signals of abuse or misuse, [20][21][22] with some users openly sharing such information, which they may not share with their physicians or through any other means. 13,17,23,24 Manual analyses established the potential of social media for drug abuse research, but automated, data-centric processing pipelines are required to fully realize social media's research potential. However, the characteristics of social media data present numerous challenges to automatic processing from the perspective of natural language processing and machine learning, including the presence of misspellings, colloquial expressions, data imbalance, and noise.…”

The Efficacy of PRISTINE: Revealing Concealed Opioid Crisis Trends via Reddit Examination

“…First, Twitter has been the subject of numerous studies. For instance, Buntain et al [9] studied emerging patterns of illicit drug use by analyzing tweets. They geolocated the tweets and categorized them based on tracking the frequency of drug names over time.…”