scholarly journals Scaling Up Research on Drug Abuse and Addiction Through Social Media Big Data (Preprint)

2016 ◽  
Author(s):  
Sunny Jung Kim ◽  
Lisa A Marsch ◽  
Jeffrey T Hancock ◽  
Amarendra K Das
Keyword(s):  
Social Media ◽  
Substance Use ◽  
Big Data ◽  
Drug Abuse ◽  
Drug Use ◽  
Prescription Drugs ◽  
Critical Review ◽  
Prescription Drug Abuse ◽  
Problematic Drug ◽  
Use Of Social Media

BACKGROUND Substance use–related communication for drug use promotion and its prevention is widely prevalent on social media. Social media big data involve naturally occurring communication phenomena that are observable through social media platforms, which can be used in computational or scalable solutions to generate data-driven inferences. Despite the promising potential to utilize social media big data to monitor and treat substance use problems, the characteristics, mechanisms, and outcomes of substance use–related communications on social media are largely unknown. Understanding these aspects can help researchers effectively leverage social media big data and platforms for observation and health communication outreach for people with substance use problems. OBJECTIVE The objective of this critical review was to determine how social media big data can be used to understand communication and behavioral patterns of problematic use of prescription drugs. We elaborate on theoretical applications, ethical challenges and methodological considerations when using social media big data for research on drug abuse and addiction. Based on a critical review process, we propose a typology with key initiatives to address the knowledge gap in the use of social media for research on prescription drug abuse and addiction. METHODS First, we provided a narrative summary of the literature on drug use–related communication on social media. We also examined ethical considerations in the research processes of (1) social media big data mining, (2) subgroup or follow-up investigation, and (3) dissemination of social media data-driven findings. To develop a critical review-based typology, we searched the PubMed database and the entire e-collection theme of “infodemiology and infoveillance” in the Journal of Medical Internet Research / JMIR Publications. Studies that met our inclusion criteria (eg, use of social media data concerning non-medical use of prescription drugs, data informatics-driven findings) were reviewed for knowledge synthesis. User characteristics, communication characteristics, mechanisms and predictors of such communications, and the psychological and behavioral outcomes of social media use for problematic drug use–related communications are the dimensions of our typology. In addition to ethical practices and considerations, we also reviewed the methodological and computational approaches used in each study to develop our typology. RESULTS We developed a typology to better understand non-medical, problematic use of prescription drugs through the lens of social media big data. Highly relevant studies that met our inclusion criteria were reviewed for knowledge synthesis. The characteristics of users who shared problematic substance use–related communications on social media were reported by general group terms, such as adolescents, Twitter users, and Instagram users. All reviewed studies examined the communication characteristics, such as linguistic properties, and social networks of problematic drug use–related communications on social media. The mechanisms and predictors of such social media communications were not directly examined or empirically identified in the reviewed studies. The psychological or behavioral consequence (eg, increased behavioral intention for mimicking risky health behaviors) of engaging with and being exposed to social media communications regarding problematic drug use was another area of research that has been understudied. CONCLUSIONS We offer theoretical applications, ethical considerations, and empirical evidence within the scope of social media communication and prescription drug abuse and addiction. Our critical review suggests that social media big data can be a tremendous resource to understand, monitor and intervene on drug abuse and addiction problems.

scholarly journals Exploring Substance Use Tweets of Youth in the United States: Mixed Methods Study

10.2196/16191 ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. e16191 ◽  
Author(s):  
Robin C Stevens ◽  
Bridgette M Brawner ◽  
Elissa Kranzler ◽  
Salvatore Giorgi ◽  
Elizabeth Lazarus ◽  
...  
Keyword(s):  
Social Media ◽  
Substance Use ◽  
Mixed Methods ◽  
Drug Use ◽  
Prescription Drugs ◽  
The United States ◽  
Health Concern ◽  
Polysubstance Use ◽  
Keywords And Phrases ◽  
And Behaviors

Background Substance use by youth remains a significant public health concern. Social media provides the opportunity to discuss and display substance use–related beliefs and behaviors, suggesting that the act of posting drug-related content, or viewing posted content, may influence substance use in youth. This aligns with empirically supported theories, which posit that behavior is influenced by perceptions of normative behavior. Nevertheless, few studies have explored the content of posts by youth related to substance use. Objective This study aimed to identify the beliefs and behaviors of youth related to substance use by characterizing the content of youths’ drug-related tweets. Using a sequential explanatory mixed methods approach, we sampled drug-relevant tweets and qualitatively examined their content. Methods We used natural language processing to determine the frequency of drug-related words in public tweets (from 2011 to 2015) among youth Twitter users geolocated to Pennsylvania. We limited our sample by age (13-24 years), yielding approximately 23 million tweets from 20,112 users. We developed a list of drug-related keywords and phrases and selected a random sample of tweets with the most commonly used keywords to identify themes (n=249). Results We identified two broad classes of emergent themes: functional themes and relational themes. Functional themes included posts that explicated a function of drugs in one’s life, with subthemes indicative of pride, longing, coping, and reminiscing as they relate to drug use and effects. Relational themes emphasized a relational nature of substance use, capturing substance use as a part of social relationships, with subthemes indicative of drug-related identity and companionship. We also identified topical areas in tweets related to drug use, including reference to polysubstance use, pop culture, and antidrug content. Across the tweets, the themes of pride (63/249, 25.3%) and longing (39/249, 15.7%) were the most popular. Most tweets that expressed pride (46/63, 73%) were explicitly related to marijuana. Nearly half of the tweets on coping (17/36, 47%) were related to prescription drugs. Very few of the tweets contained antidrug content (9/249, 3.6%). Conclusions Data integration indicates that drugs are typically discussed in a positive manner, with content largely reflective of functional and relational patterns of use. The dissemination of this information, coupled with the relative absence of antidrug content, may influence youth such that they perceive drug use as normative and justified. Strategies to address the underlying causes of drug use (eg, coping with stressors) and engage antidrug messaging on social media may reduce normative perceptions and associated behaviors among youth. The findings of this study warrant research to further examine the effects of this content on beliefs and behaviors and to identify ways to leverage social media to decrease substance use in this population.

The Use of Social Media for Urban Planning

2019 ◽  
pp. 1049-1070
Author(s):  
Fabian Neuhaus
Keyword(s):  
Social Media ◽  
Big Data ◽  
Urban Areas ◽  
Large Scale ◽  
Urban Environments ◽  
Data Systems ◽  
User Management ◽  
Social Nature ◽  
User Data ◽  
Use Of Social Media

User data created in the digital context has increasingly been of interest to analysis and spatial analysis in particular. Large scale computer user management systems such as digital ticketing and social networking are creating vast amount of data. Such data systems can contain information generated by potentially millions of individuals. This kind of data has been termed big data. The analysis of big data can in its spatial but also in a temporal and social nature be of much interest for analysis in the context of cities and urban areas. This chapter discusses this potential along with a selection of sample work and an in-depth case study. Hereby the focus is mainly on the use and employment of insight gained from social media data, especially the Twitter platform, in regards to cities and urban environments. The first part of the chapter discusses a range of examples that make use of big data and the mapping of digital social network data. The second part discusses the way the data is collected and processed. An important section is dedicated to the aspects of ethical considerations. A summary and an outlook are discussed at the end.

TWEET DATA EXTRACTOR FOR CREATING A TWITTER TRAFFIC MAP MASHUP

2015 ◽  
Vol 75 (10) ◽  
Author(s):  
Amirul Afif Jasmi ◽  
Mohamad Hafis Izran Ishak ◽  
Nurul Hawani Idris
Keyword(s):  
Social Media ◽  
Big Data ◽  
Traffic Control ◽  
Transportation Planning ◽  
General Public ◽  
New Technologies ◽  
The Road ◽  
Current Information ◽  
Road Users ◽  
Use Of Social Media

Over recent years, there has been a growth of interest in the use of social media including Facebook and Twitter by the authorities to share and updates current information to the general public. The technology has been used for a variety of purposes including traffic control and transportation planning. There is a concern that the use of new technologies, including social media will lead to data abundance that requires effective operational resources to interpret the big data. This paper proposes a tweet data extractor to extract the traffic tweet by the authority and visualise the reports and mash up on top of online map, namely Twitter map. Visualisation of traffic tweet on a map could assist a user to effectively interpret the text based Twitter report by a location based map viewer. Hence, it could ease the process of planning itinerary by the road users. 

How Predictors of Prescription Drug Abuse Vary by Age

2008 ◽  
Vol 38 (4) ◽  
pp. 1027-1043 ◽  
Author(s):  
Jeremy Arkes ◽  
Martin Y. Iguchi
Keyword(s):  
Drug Abuse ◽  
Prescription Drug ◽  
Prescription Drugs ◽  
Age Groups ◽  
Prescription Drug Abuse ◽  
Drug Abusers ◽  
Age Group ◽  
The Past ◽  
Nonmedical Use ◽  
Race Ethnicity

Previous studies that have identified the predictors of prescription drug abuse have either focused on a specific age group or pooled all age groups together into one sample. This approach constrains the predictors to have the same effect across age groups. In this study, we use the 2001 to 2003 National Survey on Drug Use and Health to estimate separate models across five age groups for the past year nonmedical use of prescription drugs. The results indicate that several factors (e.g., gender, race/ethnicity marital status, other substance use) have quite different correlations with prescription drug abuse across age groups. This suggests that more accurate profiles of prescription drug abusers can be obtained by estimating separate models for different age groups.

The use of social media by higher education institutions

2020 ◽  
Vol 65 (2) ◽  
pp. 30-42
Author(s):  
Jacek Maślankowski ◽  
Łukasz Brzezicki
Keyword(s):  
Higher Education ◽  
Social Media ◽  
Big Data ◽  
Competitive Advantage ◽  
Full Advantage ◽  
Higher Education Institutions ◽  
Marketing Activities ◽  
Use Of Social Media ◽  
Marketing Methods

Higher education institutions have been using, to an increasing extent, various marketing methods and tools, which are becoming a decisive factor in building their competitive advantage and achieving success. In order to initiate and maintain long-term relationships with their communities and to conduct other marketing activities, higher education institutions have been increasingly often using social media, which has enabled them to actively create their image. The aim of this study is to utilize big data methods and tools to measure the scale of the use of social media by the higher education sector. The research carried out in the first quarter of 2019 demonstrates that large higher education institutions, i.e. those with over 1696 students (according to the adopted classification), use social media to communicate current news to a larger extent than the smaller ones. A significantly smaller percentage of mediumsized higher education institutions (223-1695 students) and small ones (up to 222 students) have accounts in social media, thus failing to take full advantage of the potential of these media. Higher education institutions use social media mainly to promote events they organise.

scholarly journals Social Media Text Mining Framework for Drug Abuse: Development and Validation Study With an Opioid Crisis Case Analysis

10.2196/18350 ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. e18350 ◽  
Author(s):  
Tareq Nasralah ◽  
Omar El-Gayar ◽  
Yong Wang
Keyword(s):  
Social Media ◽  
Drug Abuse ◽  
Data Quality ◽  
Prescription Drugs ◽  
Case Analysis ◽  
Media Content ◽  
Opioid Epidemic ◽  
Social Media Data ◽  
Social Media Text ◽  
Media Data

Background Social media are considered promising and viable sources of data for gaining insights into various disease conditions and patients’ attitudes, behaviors, and medications. They can be used to recognize communication and behavioral themes of problematic use of prescription drugs. However, mining and analyzing social media data have challenges and limitations related to topic deduction and data quality. As a result, we need a structured approach to analyze social media content related to drug abuse in a manner that can mitigate the challenges and limitations surrounding the use of such data. Objective This study aimed to develop and evaluate a framework for mining and analyzing social media content related to drug abuse. The framework is designed to mitigate challenges and limitations related to topic deduction and data quality in social media data analytics for drug abuse. Methods The proposed framework started with defining different terms related to the keywords, categories, and characteristics of the topic of interest. We then used the Crimson Hexagon platform to collect data based on a search query informed by a drug abuse ontology developed using the identified terms. We subsequently preprocessed the data and examined the quality using an evaluation matrix. Finally, a suitable data analysis approach could be used to analyze the collected data. Results The framework was evaluated using the opioid epidemic as a drug abuse case analysis. We demonstrated the applicability of the proposed framework to identify public concerns toward the opioid epidemic and the most discussed topics on social media related to opioids. The results from the case analysis showed that the framework could improve the discovery and identification of topics in social media domains characterized by a plethora of highly diverse terms and lack of a commonly available dictionary or language by the community, such as in the case of opioid and drug abuse. Conclusions The proposed framework addressed the challenges related to topic detection and data quality. We demonstrated the applicability of the proposed framework to identify the common concerns toward the opioid epidemic and the most discussed topics on social media related to opioids.

scholarly journals Sentimen Analisis Terhadap Komentar Konsumen Terhadap Produk Penjualan Toko Online Menggunakan Metode K-Means

2020 ◽  
Vol 19 (2) ◽  
pp. 207-213
Author(s):  
Andris Faesal ◽  
Aziz Muslim ◽  
Aditya Hastami Ruger ◽  
Kusrini Kusrini
Keyword(s):  
Social Media ◽  
Big Data ◽  
Programming Language ◽  
Raw Data ◽  
R Programming Language ◽  
R Programming ◽  
Use Of Social Media ◽  
Media Sentiment ◽  
Existing Data

In this big data era, the use of social media often makes posts in his social media accounts in the form of opinions on events and things around him. One of them is making a post that gives an opinion on the events and items around it. One of them is making a post that gives an opinion on an item that has just been purchased, so that the effect is on other users who are connected to it. The more people who know it, then indirectly people will get to know the item. For that from the description of the problem above, this study raises an idea to make an analysis of social media sentiment which aims to provide a decision of consumer opinion on social media on sales products. As for the several stages of the method for the research, namely from the collection of data carried out by collecting existing data in tweets from social media Twitter using the R programming language. The data produces raw or raw data associated with sales items. With the K-means method as inputting, after each group is known from the K-Means output

scholarly journals Problematic social media use is associated with the evaluation of both risk and ambiguity during decision making

2021 ◽  
Author(s):  
Dar Meshi ◽  
David Freestone ◽  
Ceylan Özdem-Mertens
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Substance Use ◽  
High Risk ◽  
Substance Use Disorders ◽  
Media Use ◽  
Social Media Use ◽  
Social Media Platforms ◽  
Problematic Social Media Use ◽  
Use Of Social Media

AbstractBackground and aimsPeople can engage in excessive, maladaptive use of social media platforms. This problematic social media use mirrors substance use disorders with regard to symptoms and certain behavioral situations. For example, individuals with substance use disorders demonstrate aberrations in risk evaluations during decision making, and initial research on problematic social media use has revealed similar findings. However, these results concerning problematic social media use have been clouded by tasks that involve learning and that lack a clear demarcation between risky and ambiguous decision making. Therefore, we set out to specifically determine the relationship between problematic social media use and decision making under both risk and ambiguity, in the absence of learning.MethodsWe assessed each participant's (N = 90) self-reported level of problematic social media use. We then had them perform the wheel of fortune task, which has participants make choices between a sure option or either a risky or ambiguous gamble. In this way, the task isolates decisions made under risk and ambiguity, and avoids trial-to-trial learning. Results: We found that the greater an individual's problematic social media use, the more often that individual choses high-risk gambles or ambiguous gambles, regardless of the degree of ambiguity.Discussion and conclusionsOur findings indicate that greater problematic social media use is related to a greater affinity for high-risk situations and overall ambiguity. These findings have implications for the field, specifically clarifying and extending the extant literature, as well as providing future avenues for research.

scholarly journals Early Detection of Prescription Drug Abuse Using Doctor Shopping Monitoring From Claims Databases: Illustration From the Experience of the French Addictovigilance Network

2021 ◽  
Vol 12 ◽  
Author(s):  
Thomas Soeiro ◽  
Clémence Lacroix ◽  
Vincent Pradel ◽  
Maryse Lapeyre-Mestre ◽  
Joëlle Micallef
Keyword(s):  
Drug Abuse ◽  
Early Detection ◽  
Prescription Drug ◽  
Prescription Drugs ◽  
Best Practice ◽  
Illicit Drugs ◽  
Opioid Analgesics ◽  
Outcome Data ◽  
Prescription Drug Abuse ◽  
Doctor Shopping

Opioid analgesics and maintenance treatments, benzodiazepines and z-drugs, and other sedatives and stimulants are increasingly being abused to induce psychoactive effects or alter the effects of other drugs, eventually leading to dependence. Awareness of prescription drug abuse has been increasing in the last two decades, and organizations such as the International Narcotics Control Board has predicted that, worldwide, prescription drug abuse may exceed the use of illicit drugs. Assessment of prescription drug abuse tackles an issue that is hidden by nature, which therefore requires a specific monitoring. The current best practice is to use multiple detection systems to assess prescription drug abuse by various populations in a timely, sensitive, and specific manner. In the early 2000's, we designed a method to detect and quantify doctor shopping for prescription drugs from the French National Health Data System, which is one of the world's largest claims database, and a first-class data source for pharmacoepidemiological studies. Doctor shopping is a well-known behavior that involves overlapping prescriptions from multiple prescribers for the same drug, to obtain higher doses than those prescribed by each prescriber on an individual basis. In addition, doctor shopping may play an important role in supplying the black market. The paper aims to review how doctor shopping monitoring can improve the early detection of prescription drug abuse within a multidimensional monitoring. The paper provides an in-depth overview of two decades of development and validation of the method as a complementary component of the multidimensional monitoring conducted by the French Addictovigilance Network. The process accounted for the relevant determinants of prescription drug abuse, such as pharmacological data (e.g., formulations and doses), chronological and geographical data (e.g., impact of measures and comparison between regions), and epidemiological and outcome data (e.g., profiles of patients and trajectories of care) for several pharmacological classes (e.g., opioids, benzodiazepines, antidepressants, and methylphenidate).

Sign in / Sign up

Export Citation Format

Share Document