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

AI Catches One-Third of Interval Breast …

An AI algorithm for breast cancer screening has potential to enhance the performance of digital breast tomosynthesis (DBT), reducing interval cancers by up to one-third, according to a study published...

Great plan: Now We need to Get Real abou…

The government's big plan for the 10 Year Health Plan for the NHS laid out a big role for delivery. However, the Highland Marketing advisory board felt the missing implementation...

Researchers Create 'Virtual Scienti…

There may be a new artificial intelligence-driven tool to turbocharge scientific discovery: virtual labs. Modeled after a well-established Stanford School of Medicine research group, the virtual lab is complete with an...

From WebMD to AI Chatbots: How Innovatio…

A new research article published in the Journal of Participatory Medicine unveils how successive waves of digital technology innovation have empowered patients, fostering a more collaborative and responsive health care...

New AI Tool Accelerates mRNA-Based Treat…

A new artificial intelligence (AI) model can improve the process of drug and vaccine discovery by predicting how efficiently specific mRNA sequences will produce proteins, both generally and in various...

AI also Assesses Dutch Mammograms Better…

AI is detecting tumors more often and earlier in the Dutch breast cancer screening program. Those tumors can then be treated at an earlier stage. This has been demonstrated by...

RSNA AI Challenge Models can Independent…

Algorithms submitted for an AI Challenge hosted by the Radiological Society of North America (RSNA) have shown excellent performance for detecting breast cancers on mammography images, increasing screening sensitivity while...

AI could Help Emergency Rooms Predict Ad…

Artificial intelligence (AI) can help emergency department (ED) teams better anticipate which patients will need hospital admission, hours earlier than is currently possible, according to a multi-hospital study by the...

Head-to-Head Against AI, Pharmacy Studen…

Students pursuing a Doctor of Pharmacy degree routinely take - and pass - rigorous exams to prove competency in several areas. Can ChatGPT accurately answer the same questions? A new...

NHS Active 10 Walking Tracker Users are …

Users of the NHS Active 10 app, designed to encourage people to become more active, immediately increased their amount of brisk and non-brisk walking upon using the app, according to...

New AI Tool Illuminates "Dark Side…

Proteins sustain life as we know it, serving many important structural and functional roles throughout the body. But these large molecules have cast a long shadow over a smaller subclass...

Deep Learning-Based Model Enables Fast a…

Stroke is the second leading cause of death globally. Ischemic stroke, strongly linked to atherosclerotic plaques, requires accurate plaque and vessel wall segmentation and quantification for definitive diagnosis. However, conventional...