Difficulty in regulating social media content of age-restricted products: Comparing JUUL’s official Twitter timeline versus vaping-related hashtags. (Preprint)

2021 ◽  
Author(s):  
Danny Valdez ◽  
Jennifer B Unger
Keyword(s):  
Social Media ◽  
Illicit Drugs ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Application Programming Interface ◽  
Wide Audience ◽  
Media Regulation ◽  
Standard Application ◽  
Social Media Platforms ◽  
Social Media Presence

BACKGROUND In 2018, JUUL Labs Inc, a popular e-cigarette manufacturer, announced it would substantially limit its social media presence in compliance with the Food and Drug Administration’s (FDA) call to curb underage e-cigarette use. However, shortly after the announcement, a series of JUUL-related hashtags emerged on various social media platforms, calling the effectiveness of the FDA’s regulations into question. OBJECTIVE The purpose of this study is to show that hashtags remain a common venue to market age-restricted products on social media. METHODS We used Twitter’s standard Application Programming Interface (API) to download the 3200 most-recent tweets originating from JUUL Labs Inc.’s official Twitter Account (@JUULVapor), and a series of tweets containing one, or more, of the following hashtags (#ecig, #vape, #JUUL). We ran two Latent Dirichlet Allocation (LDA) topic models comparing @JUULVapor’s content versus our hashtag corpus. We qualitatively deliberated topic meanings and substantiated our interpretations with tweets from either corpus. RESULTS The topic model generated for @JUULVapor’s timeline seemingly alluded to compliance with the FDA’s call to prohibit marketing of age-restricted products on social media. However, the topic model generated for the hashtag corpus contained several references to flavors, vaping paraphernalia, and illicit drugs which may be appealing to younger audiences. CONCLUSIONS Our findings underscore the complicated nature of social media regulation. Although JUUL Labs Inc. seemingly complied with the FDA to limit its social media presence, JUUL and other e-cigarette manufacturers are still discussed openly in social media spaces. Much discourse about JUUL and e-cigarettes is spread via hashtags, which allow messages to reach a wide audience quickly. This suggests social media regulations on manufacturers are, by themselves, in effective. Stricter protocols are needed to regulate discourse about age-restricted products on social media.

scholarly journals Solution to Detect, Classify, and Report Illicit Online Marketing and Sales of Controlled Substances via Twitter: Using Machine Learning and Web Forensics to Combat Digital Opioid Access (Preprint)

2018 ◽  
Author(s):  
Tim Mackey ◽  
Janani Kalyanam ◽  
Josh Klugman ◽  
Ella Kuzmenko ◽  
Rashmi Gupta
Keyword(s):  
Machine Learning ◽  
Illicit Drugs ◽  
Topic Model ◽  
Application Programming Interface ◽  
Online Marketing ◽  
Prescription Opioid ◽  
Law Enforcement Agencies ◽  
Controlled Substances ◽  
The Us ◽  
Social Media Platforms

BACKGROUND On December 6 and 7, 2017, the US Department of Health and Human Services (HHS) hosted its first Code-a-Thon event aimed at leveraging technology and data-driven solutions to help combat the opioid epidemic. The authors—an interdisciplinary team from academia, the private sector, and the US Centers for Disease Control and Prevention—participated in the Code-a-Thon as part of the prevention track. OBJECTIVE The aim of this study was to develop and deploy a methodology using machine learning to accurately detect the marketing and sale of opioids by illicit online sellers via Twitter as part of participation at the HHS Opioid Code-a-Thon event. METHODS Tweets were collected from the Twitter public application programming interface stream filtered for common prescription opioid keywords in conjunction with participation in the Code-a-Thon from November 15, 2017 to December 5, 2017. An unsupervised machine learning–based approach was developed and used during the Code-a-Thon competition (24 hours) to obtain a summary of the content of the tweets to isolate those clusters associated with illegal online marketing and sale using a biterm topic model (BTM). After isolating relevant tweets, hyperlinks associated with these tweets were reviewed to assess the characteristics of illegal online sellers. RESULTS We collected and analyzed 213,041 tweets over the course of the Code-a-Thon containing keywords codeine, percocet, vicodin, oxycontin, oxycodone, fentanyl, and hydrocodone. Using BTM, 0.32% (692/213,041) tweets were identified as being associated with illegal online marketing and sale of prescription opioids. After removing duplicates and dead links, we identified 34 unique “live” tweets, with 44% (15/34) directing consumers to illicit online pharmacies, 32% (11/34) linked to individual drug sellers, and 21% (7/34) used by marketing affiliates. In addition to offering the “no prescription” sale of opioids, many of these vendors also sold other controlled substances and illicit drugs. CONCLUSIONS The results of this study are in line with prior studies that have identified social media platforms, including Twitter, as a potential conduit for supply and sale of illicit opioids. To translate these results into action, authors also developed a prototype wireframe for the purposes of detecting, classifying, and reporting illicit online pharmacy tweets selling controlled substances illegally to the US Food and Drug Administration and the US Drug Enforcement Agency. Further development of solutions based on these methods has the potential to proactively alert regulators and law enforcement agencies of illegal opioid sales, while also making the online environment safer for the public.

Vaccine-Related Content, Dynamics, and Conspiracy Theories in External Links Shared in Twitter COVID-19 Conversations: A Computational Analysis (Preprint)

2021 ◽  
Author(s):  
Iain Cruickshank ◽  
Tamar Ginossar ◽  
Jason Sulskis ◽  
Elena Zheleva ◽  
Tanya Berger-Wolf
Keyword(s):  
Social Media ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Communication Strategy ◽  
Vaccine Hesitancy ◽  
Traditional Media ◽  
Conspiracy Theories ◽  
Wide Range ◽  
Social Media Platforms ◽  
Youtube Videos

BACKGROUND The onset of the COVID-19 pandemic and the consequent “infodemic” that ensued highlighted the role that social media play in increasing vaccine hesitancy. Despite the efforts to curtail the spread of misinformation, the anti-vaccination movement continues to use Twitter and other social media platforms to advance its messages. Although users typically engage with different social media platforms, research on vaccination discourse typically focused on single platforms. Understanding the content and dynamics of external content shared on vaccine-related conversations on Twitter during the COVID-19 pandemic can shed light on the use of different sources, including traditional media and social media by the anti-vaccination movement. In particular, examining how YouTube videos are shared within vaccination-related tweets is important in understanding the spread of anti-vaccination narratives. OBJECTIVE informed by agenda-setting theory, this study aimed to use machine-learning to understand the content and dynamics of external websites shared in vaccines-related tweets posted in COVID-19 conversations on Twitter. METHODS We screened around 5 million tweets posted to COVID-19 related conversations to include tweets that discussed vaccination. We then identified external content, including the most tweeted web domains and URLs within these tweets and the number of days they were shared. The topics and dynamics of tweeted YouTube videos were further analyzed by using Latent Dirichlet Allocation to topic-model the transcripts of the YouTube videos, and by independent coders. RESULTS of 841,896 vaccination-related tweets identified, 128,408 (22.1%) included external content. A wide range of external websites were shared. The 20 most tweeted websites constituted 10.9% of the shared websites and were typically shared for only 2-3 days within a one-month period. Traditional media constituted the majority of these 20 most tweeted URLs. Content of YouTube links shared had both the greatest number of unique URLs for any given URL domain and was the most tweeted domain over time. The majority (n=15) of the 20 most tweeted videos opposed vaccinations and featured conspiracy theories. Analysis of the transcripts of 1,280 YouTube videos shared indicated high frequency of conspiracy theories. CONCLUSIONS Our study reveals that sharing URLs over Twitter is a common communication strategy. Whereas shared URLs overall demonstrated a strong presence of legacy media organizations, YouTube videos were used to spread anti-vaccination messages. Produced by individuals or by foreign governments, these videos emerged as a major driver for sharing vaccine-related conspiracy theories. Future interventions should take into account cross-platform use to counteract this misinformation.

scholarly journals Museum Tourism 2.0: Experiences and Satisfaction with Shopping at the National Gallery in London

2019 ◽  
Vol 11 (24) ◽  
pp. 7108
Author(s):  
Jun Shao ◽  
Qinlin Ying ◽  
Shujin Shu ◽  
Alastair M. Morrison ◽  
Elizabeth Booth
Keyword(s):  
Social Media ◽  
English Language ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Online Reviews ◽  
National Gallery ◽  
Travel Planning ◽  
Shopping Experience ◽  
Social Media Platforms ◽  
The Individual

The tourist shopping experience is the sum of the satisfaction or dissatisfaction from the individual attributes of purchased products and services. With the popularity of the Internet and travel review websites, more people choose to upload their tour experiences on their favorite social media platforms, which can influence another’s travel planning and choices. However, there have been few investigations of social media reviews of tourist shopping experiences and especially of satisfaction with museum tourism shopping. This research analyzed the user-generated reviews of the National Gallery (NG) in London written in the English language on TripAdvisor to learn more about tourist shopping experience in museums. The Latent Dirichlet Allocation (LDA) topic model was used to discover the underlying themes of online reviews and keywords related to these shopping experiences. Sentiment analysis based on a purpose-developed dictionary was conducted to explore the dissatisfying aspects of tourist shopping experiences. The results provide a framework for museums to improve shopping experiences and enhance their future development.

Customer Experience: Extracting Topics From Tweets

2021 ◽  
pp. 147078532110475
Author(s):  
Manit Mishra
Keyword(s):  
Social Media ◽  
Customer Service ◽  
Latent Dirichlet Allocation ◽  
Online Shopping ◽  
Customer Experience ◽  
Process Failure ◽  
Shopping Experience ◽  
Social Media Platforms ◽  
Media Platform ◽  
Moments Of Truth

The ubiquity of social media platforms facilitates free flow of online chatter related to customer experience. Twitter is a prominent social media platform for sharing experiences, and e-retail firms are rapidly emerging as the preferred shopping destination. This study explores customers’ online shopping experience tweets. Customers tweet about their online shopping experience based on moments of truth shaped by encounters across different touchpoints. We aggregate 25,173 such tweets related to six e-retailers tweeted over a 5-year period. Grounded on agency theory, we extract the topics underlying these customer experience tweets using unsupervised latent Dirichlet allocation. The output reveals five topics which manifest into customer experience tweets related to online shopping—ordering, customer service interaction, entertainment, service outcome failure, and service process failure. Topics extracted are validated through inter-rater agreement with human experts. The study, thus, derives topics from tweets about e-retail customer experience and thereby facilitates prioritization of decision-making pertaining to critical service encounter touchpoints.

scholarly journals Social Media Research Network: Social Media Toolkit

2021 ◽  
Author(s):  
Akash Shroff ◽  
Chantelle A Roulston ◽  
Marian Ruiz ◽  
Sharon Chen
Keyword(s):  
Social Media ◽  
Science Communication ◽  
Research Network ◽  
Research Groups ◽  
The Social ◽  
Large Audience ◽  
Social Media Platforms ◽  
Media Research ◽  
Social Media Research ◽  
Social Media Presence

The Social Media Research Network was co-founded by Chantelle Roulston and Akash Shroff in August 2021 with the support of Dr. Jessica Schleider and the Lab for Scalable Mental Health (LSMH). Since 2018, LSMH has been recruiting adolescents and parents using social media—primarily Facebook and Instagram. As of September 2021, our social media presence has reached 1.4 million people across the world. More than 35,000 individuals have interacted with our posts and messages and more than 6,000 youth, young adults, and parents have completed our single-session interventions. We wanted to share our current success and improve our processes by forming a collaboration of psychology/adolescent development research labs.The SMRN Social Media Toolkit is designed to consolidate social media experiences and suggestions from various labs into a useful document for others to use. This is by no means an exhaustive list of social media platforms and suggestions. We have limited the toolkit to include the use of Facebook and Instagram, owned and trademarked by Meta Platforms, Inc.. Instagram and Facebook encompass a very large audience (diverse in age, location, and race/ethnicity). The platforms have a lot of overlap and have been successful in research efforts for the authors. This toolkit outlines broad concepts of branding, post design, and post management. It also provides details, suggestions, and tips on how to create an account, gain a following, increase engagement, and more on both Facebook and Instagram. . Lastly, it details the process of using paid Facebook and Instagram advertisements for research purposes (i.e., recruiting participants).The ultimate goal of SMRN is to increase collaboration across research groups so that we can leverage the entire network’s social media presence to improve recruitment, science communication, and outreach efforts for all research groups involved. We hope this document will serve as a preliminary guide for the research groups within the network.

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.

scholarly journals Concerns Expressed by Chinese Social Media Users During the COVID-19 Pandemic: Content Analysis of Sina Weibo Microblogging Data (Preprint)

2020 ◽  
Author(s):  
Junze Wang ◽  
Ying Zhou ◽  
Wei Zhang ◽  
Richard Evans ◽  
Chengyan Zhu
Keyword(s):  
Social Media ◽  
Latent Dirichlet Allocation ◽  
Vaccine Development ◽  
User Behavior ◽  
Chinese Government ◽  
Web Crawler ◽  
Health Crisis ◽  
User Behavior Analysis ◽  
Sina Weibo ◽  
Social Media Platforms

BACKGROUND The COVID-19 pandemic has created a global health crisis that is affecting economies and societies worldwide. During times of uncertainty and unexpected change, people have turned to social media platforms as communication tools and primary information sources. Platforms such as Twitter and Sina Weibo have allowed communities to share discussion and emotional support; they also play important roles for individuals, governments, and organizations in exchanging information and expressing opinions. However, research that studies the main concerns expressed by social media users during the pandemic is limited. OBJECTIVE The aim of this study was to examine the main concerns raised and discussed by citizens on Sina Weibo, the largest social media platform in China, during the COVID-19 pandemic. METHODS We used a web crawler tool and a set of predefined search terms (<i>New Coronavirus Pneumonia</i>, <i>New Coronavirus</i>, and <i>COVID-19</i>) to investigate concerns raised by Sina Weibo users. Textual information and metadata (number of likes, comments, retweets, publishing time, and publishing location) of microblog posts published between December 1, 2019, and July 32, 2020, were collected. After segmenting the words of the collected text, we used a topic modeling technique, latent Dirichlet allocation (LDA), to identify the most common topics posted by users. We analyzed the emotional tendencies of the topics, calculated the proportional distribution of the topics, performed user behavior analysis on the topics using data collected from the number of likes, comments, and retweets, and studied the changes in user concerns and differences in participation between citizens living in different regions of mainland China. RESULTS Based on the 203,191 eligible microblog posts collected, we identified 17 topics and grouped them into 8 themes. These topics were pandemic statistics, domestic epidemic, epidemics in other countries worldwide, COVID-19 treatments, medical resources, economic shock, quarantine and investigation, patients’ outcry for help, work and production resumption, psychological influence, joint prevention and control, material donation, epidemics in neighboring countries, vaccine development, fueling and saluting antiepidemic action, detection, and study resumption. The mean sentiment was positive for 11 topics and negative for 6 topics. The topic with the highest mean of retweets was domestic epidemic, while the topic with the highest mean of likes was quarantine and investigation. CONCLUSIONS Concerns expressed by social media users are highly correlated with the evolution of the global pandemic. During the COVID-19 pandemic, social media has provided a platform for Chinese government departments and organizations to better understand public concerns and demands. Similarly, social media has provided channels to disseminate information about epidemic prevention and has influenced public attitudes and behaviors. Government departments, especially those related to health, can create appropriate policies in a timely manner through monitoring social media platforms to guide public opinion and behavior during epidemics.

Transitive Topic Modeling with Conversational Structure Context: Discovering Topics that are Most Popular in Online Discussions

2020 ◽  
Vol 14 (02) ◽  
pp. 273-293
Author(s):  
Yingcheng Sun ◽  
Richard Kolacinski ◽  
Kenneth Loparo
Keyword(s):  
Social Media ◽  
Topic Modeling ◽  
Topic Model ◽  
Online Discussions ◽  
Challenging Problem ◽  
Topic Extraction ◽  
Limited Success ◽  
Social Media Platforms ◽  
Improved Performance ◽  
Conversational Structure

With the explosive growth of online discussions published everyday on social media platforms, comprehension and discovery of the most popular topics have become a challenging problem. Conventional topic models have had limited success in online discussions because the corpus is extremely sparse and noisy. To overcome their limitations, we use the discussion thread tree structure and propose a “popularity” metric to quantify the number of replies to a comment to extend the frequency of word occurrences, and the “transitivity” concept to characterize topic dependency among nodes in a nested discussion thread. We build a Conversational Structure Aware Topic Model (CSATM) based on popularity and transitivity to infer topics and their assignments to comments. Experiments on real forum datasets are used to demonstrate improved performance for topic extraction with six different measurements of coherence and impressive accuracy for topic assignments.

This Is How We Do It: A Qualitative Approach to National Sport Organizations’ Social-Media Implementation

2017 ◽  
Vol 10 (2) ◽  
pp. 196-217 ◽  
Author(s):  
Michael L. Naraine ◽  
Milena M. Parent
Keyword(s):  
Social Media ◽  
Qualitative Approach ◽  
Sport Organizations ◽  
Factors Affecting ◽  
Perceived Utility ◽  
Strategic Value ◽  
Semistructured Interviews ◽  
Social Media Platforms ◽  
Business To Consumer ◽  
Social Media Presence

This study’s purpose was to uncover national sport organizations’ (NSOs) perceptions of social media to understand how social media are situated and implemented. Specifically, the study sought to understand the perceived utility of social media, the rationale for the content produced and disseminated, and the factors affecting social-media implementation. Through semistructured interviews with Canadian NSOs, results were grouped into 3 themes: the value of social media (i.e., benefits, potential, and credibility), social-media use (i.e., content, types of social-media platforms, and rationale/motivations), and the challenges associated with social media (i.e., capacity, language issues, stakeholders engagement or lack thereof, and resistance). NSOs implement social media solely for business-to-consumer purposes. Social media act as a “double-edged sword”: NSOs believe that a good social-media presence requires sufficient resources but remain unconvinced of the “true” strategic value of social media.

Identifying target audience on enterprise social network

2019 ◽  
Vol 119 (1) ◽  
pp. 111-128 ◽  
Author(s):  
Jianhong Luo ◽  
Xuwei Pan ◽  
Shixiong Wang ◽  
Yujing Huang
Keyword(s):  
Social Media ◽  
Social Network ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Information Service ◽  
Target Audience ◽  
Business Practices ◽  
Content Type ◽  
Enterprise Social Media ◽  
Enterprise Social Network

Purpose Delivering messages and information to potentially interested users is one of the distinguishing applications of online enterprise social network (ESN). The purpose of this paper is to provide insights to better understand the repost preferences of users and provide personalized information service in enterprise social media marketing. Design/methodology/approach It is accomplished by constructing a target audience identification framework. Repost preference latent Dirichlet allocation (RPLDA) topic model topic model is proposed to understand the mass user online repost preferences toward different contents. A topic-oriented preference metric is proposed to measure the preference degree of individual users. And the function of reposting forecasting is formulated to identify target audience. Findings The empirical research shows the following: a total of 20 percent of the repost users in ESN represent the key active users who are particularly interested in the latent topic of messages in ESN and fits Pareto distribution; and the target audience identification framework can successfully identify different target key users for messages with different latent topics. Practical implications The findings should motivate marketing managers to improve enterprise brand by identifying key target audience in ESN and marketing in a way that truthfully reflects personalized preferences. Originality/value This study runs counter to most current business practices, which tend to use simple popularity to seek important users. Adaptively and dynamically identifying target audience appears to have considerable potential, especially in the rapidly growing area of enterprise social media information service.

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