scholarly journals Social Media and the Surge: Emergency physician Twitter use in the Covid-19 pandemic as a potential predictor of impending surge (Preprint)

2021 ◽  
Author(s):  
Colton Margus ◽  
Natasha Brown ◽  
Michelle R. Safferman ◽  
Alexander Hart ◽  
Attila Hertelendy ◽  
...  
Keyword(s):  
Social Media ◽  
Emergency Physician ◽  
Latent Dirichlet Allocation ◽  
Clinical Care ◽  
Application Programming Interface ◽  
Professional Organizations ◽  
Emergency Physicians ◽  
Hospital Bed ◽  
Healthcare Crisis ◽  
Application Programming

BACKGROUND Early conversations on social media by emergency physicians offer a window into the ongoing response to the SARS-CoV-2 pandemic. OBJECTIVE This retrospective observational study of emergency physician Twitter use details how the healthcare crisis has influenced emergency physician discourse online, and may have use as a harbinger of ensuing surge. METHODS Followers of the three main emergency physician professional organizations were identified using Twitter’s application programming interface. They and their followers were included in the study if identifying explicitly as United States-based emergency physicians. Statuses or ‘tweets’ were obtained between January 4th, 2020, when the new disease was first reported, and December 14th, 2020, when vaccinations first began. Original tweets underwent sentiment analysis using the previously validated Valence Aware Dictionary and sEntiment Reasoner (VADER) tool as well as topic modeling using Latent Dirichlet Allocation unsupervised machine learning. Sentiment and topic trends were then correlated with daily change in new Covid-19 cases and inpatient bed utilization. RESULTS 3,463 emergency physicians produced 334,747 unique English tweets during the study period. 910 (26.3%) stated that they were in training, and 446 (51.7%) of those who provided a gender identified as a man. Overall tweet volume went from a pre-March mean of 481.9 ±72.7 daily tweets to 1,065.5 ±257.3 daily thereafter. Parameter and topic number tuning led to 20 tweet topics, with a topic coherence of 0.49. Except for a week in June and four days in November, discourse was dominated by the healthcare system (45,570, 13.6%). Discussion of pandemic response, epidemiology, and clinical care were jointly found to correlate with Covid-19 hospital bed utilization (Pearson’s r = 0.41), as was the occurrence of ‘covid’,‘coronavirus’, or ‘pandemic’ in tweet text (0.47). Momentum in Covid-19 tweets, as demonstrated by a sustained crossing of 7 and 28-day moving averages, was found to have occurred 45.0 ±12.7 days before peak Covid-19 hospital bed utilization across the country and four most contributory states. CONCLUSIONS Covid-19 Twitter discussion among emergency physicians correlates with and may precede rising hospital burden. This study therefore begins to depict the extent to which the ongoing pandemic has affected the field of emergency medicine discourse online, and suggests a potential avenue for understanding predictors of surge.

Is JUUL marketing to kids on social media? Analysis of JUUL Twitter Followers� Age (Preprint)

2018 ◽  
Author(s):  
Annice Kim ◽  
Robert Chew ◽  
Michael Wenger ◽  
Margaret Cress ◽  
Thomas Bukowski ◽  
...  
Keyword(s):  
Social Media ◽  
Application Programming Interface ◽  
Media Analysis ◽  
Prediction Algorithm ◽  
Social Media Networks ◽  
Verification Methods ◽  
Twitter Account ◽  
Application Programming ◽  
Age Verification ◽  
Age Category

BACKGROUND JUUL is an electronic nicotine delivery system (ENDS) resembling a USB device that has become rapidly popular among youth. Recent studies suggest that social media may be contributing to its popularity. JUUL company claims their products are targeted for adult current smokers but recent surveillance suggests youth may be exposed to JUUL products online. To date, there has been little attention on restricting youth exposure to age restricted products on social media. OBJECTIVE The objective of this study was to utilize a computational age prediction algorithm to determine the extent to which underage youth are being exposed to JUUL’s marketing practices on Twitter. METHODS We examined all of @JUULvapor’s Twitter followers in April 2018. For followers with a public account, we obtained their metadata and last 200 tweets using the Twitter application programming interface. We ran a series of classification models to predict whether the account following @JUULvapor was an underage youth or an adult. RESULTS Out of 9,077 individuals following @JUULvapor Twitter account, a three-age category model predicted that 44.9% are 13 to 17 years old (N=4,078), 43.6% are 18 to 24 years old (N=3,957), and 11.5% are 25 years old or older (N=1,042); and a two-age category model predicted that 80.6% (N=7,313) are under 21 years old. CONCLUSIONS Despite a disclaimer that followers must be of legal age to purchase tobacco products, the majority of JUUL followers on Twitter are under age. This suggests that ENDS brands and social media networks need to implement more stringent age-verification methods to protect youth from age-restricted content.

scholarly journals Ontology Meter for Twitter Fake Accounts Detection

2021 ◽  
Vol 14 (1) ◽  
pp. 410-419
Author(s):  
Mohammed Jabardi ◽  
◽  
Asaad Hadi ◽  
Keyword(s):  
Social Media ◽  
Service Providers ◽  
Decision Rules ◽  
Application Programming Interface ◽  
Detection Accuracy ◽  
Online Fraud ◽  
Social Media Platforms ◽  
Consumer Data ◽  
Application Programming ◽  
And Control

One of the most popular social media platforms, Twitter is used by millions of people to share information, broadcast tweets, and follow other users. Twitter is an open application programming interface and thus vulnerable to attack from fake accounts, which are primarily created for advertisement and marketing, defamation of an individual, consumer data acquisition, increase fake blog or website traffic, share disinformation, online fraud, and control. Fake accounts are harmful to both users and service providers, and thus recognizing and filtering out such content on social media is essential. This study presents a new approach to detect fake Twitter accounts using ontology and Semantic Web Rule Language (SWRL) rules. SWRL rules-based reasoner is utilized under predefined rules to infer whether the profile is trust or fake. This approach achieves a high detection accuracy of 97%. Furthermore, ontology classifier is an interpretable model that offers straightforward and human-interpretable decision rules.

Tracking via Geotagged Social Media Data

2020 ◽  
Author(s):  
Anne Hardy
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Single Point ◽  
Application Programming Interface ◽  
Continuous Location ◽  
Social Media Data ◽  
Large Numbers ◽  
Application Programming ◽  
Media Data ◽  
Programming Interface

Over the past twenty years, social media has changed the ways in which we plan, travel and reflect on our travels. Tourists use social media while travelling to stay in touch with friends and family, enhance their social status (Guo et al., 2015); and assist others with decision making (Xiang and Gretzel, 2010; Yoo and Gretzel, 2010). They also use it to report back to their friends and family where they are. This can be done using a geotag function that provides a location for where a post is made. While little is known about why tourists choose to geotag their social media posts, Chung and Lee (2016) suggest that geotags may be used in an altruistic manner by tourists, in order to provide information, and because they elicit a sense of anticipated reward. What is known, however, is that the function offers researchers the ability to understand where tourists travel. There are two types of geotagged social media data. The first of these is discussed in this chapter and may be defined as single point geo-referenced data – geotagged social media posts whose release is chosen by the user. This includes data gathered from social media apps such as Facebook, Instagram, Twitter and WeiChat. The method of obtaining this data involves the collation of large numbers of discrete geotagged updates or photographs. Data can be collated via an application programming interface (API) provided by the app developer to researchers, by automated data scraping via computer programs, perhaps written in Python, or manually by researchers. The second type of data is continuous location-based data from applications that are designed to track movement constantly, such as Strava or MyFitnessPal. Tracking methods using this continuous location-based data are discussed in detail in the following chapter.

Age Knows No Bounds: A Latent Content Analysis of Social Media Comments Toward Older Adults’ Engagement in Sports Activities

2021 ◽  
pp. 073346482110046
Author(s):  
Allan B. de Guzman ◽  
John Christopher B. Mesana ◽  
Jonas Airon M. Roman
Keyword(s):  
Older Adults ◽  
Social Media ◽  
Content Analysis ◽  
Application Programming Interface ◽  
Well Being ◽  
Future Directions ◽  
Playing Field ◽  
Data Application ◽  
Application Programming ◽  
Latent Content

With the growing statistics of older adults across societies, sustaining their health and well-being through active participation in sports cannot be neglected nor overlooked. This qualitative study purports to characterize the ontology of social media comments relative to older person’s engagement in sports via latent content analysis. Specifically, a set of YouTube comments ( n = 7,546), extracted from select videos featuring older adults in sports ( n = 62), through YouTube Data Application Programming Interface (API) Version 3, was subjected to inductive analytic procedures of content analysis. Interestingly, this study afforded the emergence of a playing field model emanating from the dualistic perspectives of aging as engagement and aged as engaged that represent how YouTube users view older adult’s continual involvement in sports. Limitations and future directions of this study are also discussed in this article.

scholarly journals Topic Modeling Analysis of Social Enterprises: Twitter Evidence

2020 ◽  
Vol 12 (8) ◽  
pp. 3419 ◽  
Author(s):  
Shr-Wei Kao ◽  
Pin Luarn
Keyword(s):  
Social Media ◽  
Topic Modeling ◽  
Application Programming Interface ◽  
Social Enterprises ◽  
Four Dimensions ◽  
Modeling Analysis ◽  
Application Programming ◽  
Rich Information ◽  
Programming Interface

Social media is a major channel used for communication by professional and social groups. The text posted on social media contains extremely rich information. To capture the development of social enterprises (SEs), this paper examines the tweets posted on Twitter and searches the hashtags on the Twitter Application Programming Interface (API) that SEs deem to be the most important. The results suggest that these tweets can be divided into three content groups (strategy, impact and business). This paper expands this into four dimensions (strategy, impact, business and people) and six indicators (social, opportunity, change, enterprise, network and team) and establishes a conceptual framework of SEs. This paper aims to enhance the understanding of the pertinent issues recently affecting SEs and extract findings that can act as a reference for follow-up studies.

scholarly journals Public Perception of SARS-CoV-2 Vaccinations on Social Media: Questionnaire and Sentiment Analysis

2021 ◽  
Vol 18 (24) ◽  
pp. 13028
Author(s):  
Charlotte Roe ◽  
Madison Lowe ◽  
Benjamin Williams ◽  
Clare Miller
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Public Perception ◽  
Application Programming Interface ◽  
Vaccine Hesitancy ◽  
Analysis Model ◽  
Search Terms ◽  
Machine Learning Approach ◽  
Application Programming

Vaccine hesitancy is an ongoing concern, presenting a major threat to global health. SARS-CoV-2 COVID-19 vaccinations are no exception as misinformation began to circulate on social media early in their development. Twitter’s Application Programming Interface (API) for Python was used to collect 137,781 tweets between 1 July 2021 and 21 July 2021 using 43 search terms relating to COVID-19 vaccines. Tweets were analysed for sentiment using Microsoft Azure (a machine learning approach) and the VADER sentiment analysis model (a lexicon-based approach), where the Natural Language Processing Toolkit (NLTK) assessed whether tweets represented positive, negative or neutral opinions. The majority of tweets were found to be negative in sentiment (53,899), followed by positive (53,071) and neutral (30,811). The negative tweets displayed a higher intensity of sentiment than positive tweets. A questionnaire was distributed and analysis found that individuals with full vaccination histories were less concerned about receiving and were more likely to accept the vaccine. Overall, we determined that this sentiment-based approach is useful to establish levels of vaccine hesitancy in the general public and, alongside the questionnaire, suggests strategies to combat specific concerns and misinformation.

scholarly journals Implementing an Application Programming Interface for PROMIS Measures at Three Medical Centers

2021 ◽  
Vol 12 (05) ◽  
pp. 979-983
Author(s):  
Michael Bass ◽  
Christian Oncken ◽  
Allison W. McIntyre ◽  
Chris Dasilva ◽  
Joshua Spuhl ◽  
...  
Keyword(s):  
Patient Reported Outcomes ◽  
Clinical Care ◽  
Application Programming Interface ◽  
Measurement Information ◽  
Software Integration ◽  
Clinical Workflow ◽  
Medical Centers ◽  
Patient Reported ◽  
Application Programming ◽  
Programming Interface

Abstract Background There is an increasing body of literature advocating for the collection of patient-reported outcomes (PROs) in clinical care. Unfortunately, there are many barriers to integrating PRO measures, particularly computer adaptive tests (CATs), within electronic health records (EHRs), thereby limiting access to advances in PRO measures in clinical care settings. Objective To address this obstacle, we created and evaluated a software integration of an Application Programming Interface (API) service for administering and scoring Patient-Reported Outcomes Measurement Information System (PROMIS) measures with the EHR system. Methods We created a RESTful API and evaluated the technical feasibility and impact on clinical workflow at three academic medical centers. Results Collaborative teams (i.e., clinical, information technology [IT] and administrative staff) performed these integration efforts addressing issues such as software integration as well as impact on clinical workflow. All centers considered their implementation successful based on the high rate of completed PROMIS assessments (between January 2016 and January 2021) and minimal workflow disruptions. Conclusion These case studies demonstrate not only the feasibility but also the pathway for the integration of PROMIS CATs into the EHR and routine clinical care. All sites utilized diverse teams with support and commitment from institutional leadership, initial implementation in a single clinic, a process for monitoring and optimization, and use of custom software to minimize staff burden and error.

scholarly journals The Volume and Tone of Twitter Posts About Cannabis Use During Pregnancy: A Scoping Review Protocol

2021 ◽  
Author(s):  
Liam Cresswell ◽  
Lisette Espín-Noboa ◽  
Malia Su-Qin Murphy ◽  
Serine Ramlawi ◽  
Mark C. Walker ◽  
...  
Keyword(s):  
Social Media ◽  
Scoping Review ◽  
English Language ◽  
Health Effect ◽  
Academic Research ◽  
Application Programming Interface ◽  
Cannabis Use ◽  
Scoping Reviews ◽  
Review Protocol ◽  
Application Programming

Introduction: Cannabis use has increased in Canada since its legalization in 2018, including among pregnant women who may be motivated to use cannabis to reduce symptoms of nausea and vomiting. However, a growing body of research suggests that cannabis use during pregnancy may harm the developing fetus. Patients increasingly seek medical advice from online sources, but these platforms are often used to spread anecdotal descriptions or misinformation. Given the possible disconnect between online messaging and evidence-based research about the effects of cannabis use during pregnancy, there is a potential for advice taken from social media to cause harm. We propose a scoping review of Twitter to quantify the volume and tone of English-language posts related to cannabis use in pregnancy from January 2012 to July 2021.Methods and Analysis: Using Arksey and O’Malley’s framework for scoping reviews, we will collect publicly available posts from Twitter that mention cannabis use during pregnancy and employ the Twitter Application Programming Interface (API) for Academic Research to extract data from tweets, including public metrics such as the number of likes, retweets and quotes, as well as health effect mentions, sentiment, location and users interests. These data will be used to quantify how cannabis use during pregnancy is discussed on Twitter and to build a qualitative profile of supportive and opposing posters.Ethics and Dissemination: Research ethics approval is not required for publicly accessible Twitter data. We will disseminate this review’s findings through traditional channels, including preprint and peer-reviewed publications and presentations at academic conferences. In addition, we will share our findings through professional and institutional social media accounts and web pages associated with the research team.

scholarly journals Reddit discussions about buprenorphine associated precipitated withdrawal in the era of fentanyl

2021 ◽  
Author(s):  
Anthony Spadaro ◽  
Abeed Sarker ◽  
Whitney Hogg-Bremmer ◽  
Jennifer S Love ◽  
Nicole O'Donnell ◽  
...  
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Application Programming Interface ◽  
The United States ◽  
Opioid Use Disorder ◽  
Opioid Use ◽  
Induction Strategy ◽  
Application Programming ◽  
The Impact ◽  
Media Data

Background: Buprenorphine is an evidence-based treatment for Opioid Use Disorder (OUD). Standard buprenorphine induction requires a period of opioid abstinence to minimize risk of precipitated opioid withdrawal (POW). Our objective was to study the impact of the increasing presence of fentanyl and its analogs in the opioid supply of the United States, on buprenorphine induction and POW, using social media data from Reddit. Methods: This is a data-driven, mixed methods study of opioid-related forums, called subreddits, on Reddit to analyze posts related to fentanyl, POW, and buprenorphine induction. The posts were collected from seven subreddits using an application programming interface for Reddit. We applied natural language processing to identify subsets of salient posts relevant to buprenorphine induction, and performed manual, qualitative, thematic analyses of them. Results: 267,136 posts were retrieved from seven subreddits. Fentanyl mentions increased from 3 in 2013 to 3870 in 2020, and POW mentions increased from 2 (2012) to 332 (2020). Manual review of 384 POW-mentioning posts and 106 'Bernese method' (a microdosing induction strategy) mentioning posts revealed common themes and peoples' experiences. Specifically, presence of fentanyl caused POWs despite long abstinence durations, and alternative induction via microdosing were frequently recommended in peer-to-peer discussions. Conclusions: This study found that increased social media chatter on Reddit about POW correlated with fentanyl mentions. A subset of posts described microdosing as a self-management strategy to avoid POW. Reddit posts suggest that people are utilizing these strategies to initiate buprenorphine due to challenges arising from fentanyl prevalence in the opioid supply.

scholarly journals Implementasi Metode Klasifikasi Naive Bayes Pada Sistem Analisis Opini Pengguna Twitter Berbasis Web

2021 ◽  
Vol 10 (1) ◽  
pp. 46-54
Author(s):  
Apif Supriadi ◽  
Fatmasari
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Application Programming Interface ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Public Sentiment ◽  
Application Programming ◽  
Analysis System ◽  
Programming Interface

Abstract— Development of social media which is the result of technological development is an inseparable part of people's lives. Social media is a place where ordinary people express their feelings and opinions about something that concerns them. Inknowing the direction of public sentiment, surveys are usually done online or offline, this sentiment analysis system will facilitate and speed up the process of knowing the direction of public sentiment, in the case of research. This uses data from Twitter social media called tweets or tweets, web-based sentiment analysis system that will classify tweets into 3 (three) types of sentiments, namely positive, neutral and negative, then make a percentage to make it easier to see the direction of public sentiment. In classifying this system uses the Naive Bayes Classifier method and displays it in a web interface with the PHP programming language and uses the Application Programming Interface (API) to get data from Twitter. Intisari — Saat ini perkembangan media sosial yang merupakan hasil dari perkembangan teknologi menjadi bagian tak terpisahkan dari kehidupan masyarakat. Media sosial menjadi tempat masyarakat biasa mengutarakan berbagai perasaan dan opininya tentang suatu hal yang jadi perhatian mereka, dalam mengetahui arah sentimen masyarakat biasanya dilakukan survei baik secara online atau offline, sistem analisis sentimen ini akan memudahkan dan mempercepat proses mengetahui arah sentimen publik, dalam kasus penelitian ini menggunakan data dari media sosial Twitter yang disebut dengan tweets atau cuitan, sistem analisis sentimen berbasis web yang akan mengklasifikasikan cuitan kedalam 3 (tiga) jenis sentimen yaitu positif, netral dan negatif lalu melakukan persentasenya agar mempermudah melihat arah sentimen publik. Dalam melakukan klasifikasinya sistem ini menggunakan metode Naive Bayes Classifier dan menampilkannya dalam antarmuka web dengan bahasa pemrograman PHP dan menggunakan Application Programming Interface (API) dalam mendapatkan data dari Twitter.

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