Using Social Media Big Data to Combat Prescription Drug Crisis

Researchers at Dartmouth, Stanford University, and IBM Research, conducted a critical review of existing literature to determine whether social media big data can be used to understand communication and behavioral patterns related to prescription drug abuse. Their study found that with proper research methods and attention to privacy and ethical issues, social media big data can reveal important information concerning drug abuse, such as user-reported side effects, drug cravings, emotional states, and risky behaviors.

Their work, "Scaling Up Prescription Drug Abuse and Addiction Research Through Social Media Big Data," is reported in the Journal of Medical Internet Research .

Prescription drug addiction is a well-known nationwide problem. Many people who are unable to get help for their addiction seek out peer support groups on Facebook or other social media platforms to share stories about their experiences and also provide social peer-based support.

Lead author, Sunny Jung Kim, PhD, an e-health communication scholar in the departments of biomedical data science and psychiatry at Dartmouth's Geisel School of Medicine, says that because we are prolific consumers of social media, which is not limited to geography - globally, people spend more than two hours every day on social media platforms generating vast amounts of big data about our personal communications and activities - we can use these platforms to enhance public health communication strategies to help people on a large scale.

"Harnessing social media platforms and data can provide insight into important novel discoveries of collective public health risk behavior, a better understanding of peoples' struggles with addiction, and their process of recovery," Kim says. "I started this project because there were few studies about why people use social networking sites to share unsolicited, highly personal information about their drug use, nor about the psychological effects or consequences of this type of user-generated communication."

Co-author Jeffrey Hancock, PhD, a professor in the department of communication and the director of computational social science at Stanford University, says, "Given the importance of this problem for the U.S. population, it's imperative that we understand how social media is playing a role and how it can be part of the solution."

Based on their findings, the researchers designed an evidence-based, multi-level framework to inform future social media-based substance use prevention and recovery intervention programs.

"Our review and typology suggests that social media big data and platforms can be a tremendous resource for monitoring and intervening on behalf of people with drug addiction and abuse problems," Kim says.

Kim SJ, Marsch LA, Hancock JT, Das AK.
Scaling Up Research on Drug Abuse and Addiction Through Social Media Big Data.
J Med Internet Res 2017;19(10):e353. doi: 10.2196/jmir.6426.

Most Popular Now

Unlocking the 10 Year Health Plan

The government's plan for the NHS is a huge document. Jane Stephenson, chief executive of SPARK TSL, argues the key to unlocking its digital ambitions is to consider what it...

Alcidion Grows Top Talent in the UK, wit…

Alcidion has today announced the addition of three new appointments to their UK-based team, with one internal promotion and two external recruits. Dr Paul Deffley has been announced as the...

AI can Find Cancer Pathologists Miss

Men assessed as healthy after a pathologist analyses their tissue sample may still have an early form of prostate cancer. Using AI, researchers at Uppsala University have been able to...

New Training Year Starts at Siemens Heal…

In September, 197 school graduates will start their vocational training or dual studies in Germany at Siemens Healthineers. 117 apprentices and 80 dual students will begin their careers at Siemens...

AI, Full Automation could Expand Artific…

Automated insulin delivery (AID) systems such as the UVA Health-developed artificial pancreas could help more type 1 diabetes patients if the devices become fully automated, according to a new review...

How AI could Speed the Development of RN…

Using artificial intelligence (AI), MIT researchers have come up with a new way to design nanoparticles that can more efficiently deliver RNA vaccines and other types of RNA therapies. After training...

MIT Researchers Use Generative AI to Des…

With help from artificial intelligence, MIT researchers have designed novel antibiotics that can combat two hard-to-treat infections: drug-resistant Neisseria gonorrhoeae and multi-drug-resistant Staphylococcus aureus (MRSA). Using generative AI algorithms, the research...

AI Hybrid Strategy Improves Mammogram In…

A hybrid reading strategy for screening mammography, developed by Dutch researchers and deployed retrospectively to more than 40,000 exams, reduced radiologist workload by 38% without changing recall or cancer detection...

Penn Developed AI Tools and Datasets Hel…

Doctors treating kidney disease have long depended on trial-and-error to find the best therapies for individual patients. Now, new artificial intelligence (AI) tools developed by researchers in the Perelman School...

Are You Eligible for a Clinical Trial? C…

A new study in the academic journal Machine Learning: Health discovers that ChatGPT can accelerate patient screening for clinical trials, showing promise in reducing delays and improving trial success rates. Researchers...

Global Study Reveals How Patients View M…

How physicians feel about artificial intelligence (AI) in medicine has been studied many times. But what do patients think? A team led by researchers at the Technical University of Munich...

New AI Tool Addresses Accuracy and Fairn…

A team of researchers at the Icahn School of Medicine at Mount Sinai has developed a new method to identify and reduce biases in datasets used to train machine-learning algorithms...