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.

scholarly journals Volkswagen’s crisis communication: Twitter use during #dieselgate

2021 ◽  
Author(s):  
Josephine Lim
Keyword(s):  
Social Media ◽  
Environmental Protection Agency ◽  
Crisis Communication ◽  
Crisis Response ◽  
Response Strategy ◽  
Open Communication ◽  
Social Media Networks ◽  
Human Voice ◽  
Twitter Account ◽  
Clean Air

On September 18, 2015, Volkswagen became embroiled in a global crisis after the U.S. Environmental Protection Agency (EPA) publically announced Volkswagen’s violation of the Clean Air Act through the installation of “defeat devices” that trick emission tests. The Volkswagen emissions scandal was covered by media around the world and news spread quickly on social media networks, such as Twitter, though a trending hashtag, #dieselgate. Through studying Volkswagen’s Twitter accounts (the Twitter account for the overall brand, a regional Twitter account and a Twitter account targeting the press), this case study analyzes Volkswagen’s adoption of the Situational Crisis Communication Theory’s (SCCT) rebuild and bolstering crisis response strategy on Twitter, but with little open communication through this medium. Information shared on Volkswagen’s Twitter accounts was inconsistent and Volkswagen’s limited adoption of a conversational, human voice on social media affected the virality of organizational messaging. Volkswagen was progressing towards recovering its social currency on Twitter, but updates on the crisis or similar news related to the situation encourages greater hostility and apathy towards the organization.

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.

scholarly journals Volkswagen’s crisis communication: Twitter use during #dieselgate

2021 ◽  
Author(s):  
Josephine Lim
Keyword(s):  
Social Media ◽  
Environmental Protection Agency ◽  
Crisis Communication ◽  
Crisis Response ◽  
Response Strategy ◽  
Open Communication ◽  
Social Media Networks ◽  
Human Voice ◽  
Twitter Account ◽  
Clean Air

On September 18, 2015, Volkswagen became embroiled in a global crisis after the U.S. Environmental Protection Agency (EPA) publically announced Volkswagen’s violation of the Clean Air Act through the installation of “defeat devices” that trick emission tests. The Volkswagen emissions scandal was covered by media around the world and news spread quickly on social media networks, such as Twitter, though a trending hashtag, #dieselgate. Through studying Volkswagen’s Twitter accounts (the Twitter account for the overall brand, a regional Twitter account and a Twitter account targeting the press), this case study analyzes Volkswagen’s adoption of the Situational Crisis Communication Theory’s (SCCT) rebuild and bolstering crisis response strategy on Twitter, but with little open communication through this medium. Information shared on Volkswagen’s Twitter accounts was inconsistent and Volkswagen’s limited adoption of a conversational, human voice on social media affected the virality of organizational messaging. Volkswagen was progressing towards recovering its social currency on Twitter, but updates on the crisis or similar news related to the situation encourages greater hostility and apathy towards the organization.

scholarly journals Marketing #WeTheNorth: Analyzing Fandom, Nationalism, and Brand Loyalty of the Toronto Raptors Basketball Team

2021 ◽  
Author(s):  
Sasha C. Ricciuti
Keyword(s):  
Social Media ◽  
Brand Loyalty ◽  
Research Question ◽  
Media Analysis ◽  
Social Media Analysis ◽  
The Face ◽  
Twitter Account ◽  
Research Questions ◽  
Negative Sentiment ◽  
Fan Engagement

Established as a successful marketing slogan during the 2014 NBA playoffs, the #WeTheNorth campaign became the face of branding for the Toronto Raptors franchise that enhanced brand loyalty and unified Canadian basketball fans. The following Major Research Project (MRP) explores two different research questions surrounding a social media analysis of the Toronto Raptors #WeTheNorth campaign. The first research question examines the Raptors’ fan perspective, and focuses on the connotative messages that are incorporated into the #WeTheNorth campaign to broaden the team’s message and re-vamp the team’s national identity. The second research question examines the organization’s perspective and focuses on how the Raptors brand utilizes sports nationalism in their social media efforts to support fan engagement. This paper also reinforces research from previous academic findings that include: nationalism, community, collective fandom, social media, semiotics, and branding. Using an analytics tool named Sysomos, a content analysis of the Raptors’ official Twitter account was conducted to gather primary research. One Hundred Tweets were gathered per research question, and then coded to provide insight regarding the #WeTheNorth campaign from the 2018/19 NBA regular season. Findings for the first research question reinforce national fandom, and the support fan unification via the use of the #WeTheNorth hashtag. In addition, over 35% of Tweets from fans included positive sentiment, compared to the 17% that had negative sentiment. Findings for the second research question focus on branding, semiotics, and fan engagement levels that the Toronto Raptors social media team tries to enforce. Results proved that over 60% of Tweets included some form of request for fan participation, with 17% of Tweets containing positive Tweet sentiment. Overall, as long as the #WeTheNorth campaign remains the Raptors’ primary marketing slogan the campaign should continue to reinforce national fandom and support positive online fan engagement.

Towards More Meaningful Social Media Analysis: Case Study of Utilizing an Age Prediction Algorithm to Identify and Code Reddit Posts about E-cigarettes by Youth vs. Adults (Preprint)

2021 ◽  
Author(s):  
Mario Navarro ◽  
Andrea Malterud ◽  
Zachary Cahn ◽  
Laura Baum ◽  
Thomas Bukowski ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Data Science ◽  
Media Analysis ◽  
Prediction Algorithm ◽  
Age Group ◽  
Social Media Analysis ◽  
Machine Learning Classification ◽  
Minimum Age ◽  
Qualitative Coding

BACKGROUND Previous qualitative studies and data science studies using Reddit for tobacco research are limited by the lack of available demographic information. Social media investigations are often limited to manual qualitative coding or machine learning classification in isolation. OBJECTIVE This study combines both machine learning methods and manual qualitative coding to provide contextual age nuance to social media analysis. By being able to predict a Redditor’s age using publicly available data, the most popular posts can be analyzed and qualitatively coded to provide nuanced comparisons on thematic topics by age group. METHODS The current study combines these two methods to 1) predict Reddit users’ age into two categories (13-20, 21-54) and 2) qualitatively code Electronic Nicotine Delivery System [ENDS] related Reddit posts within the two age groups. We identified Reddit posts on three topics: Vaping in General, Tobacco 21 Minimum Age Laws, and Flavor Restriction Policies. An age algorithm was used to predict Reddit users’ ages (13-20 or 21-54 year old users). The 25 posts with the highest karma score (number of upvotes minus number of downvotes) for each query and each predicted age group were qualitatively coded. RESULTS The top three, two of which were part of the query, out of nine, topics that emerged were “Flavor Restriction Policies”, “Tobacco 21 Policies”, and “Use”. Tobacco 21 and Flavor Restriction Policy posts were prominent coding categories. Opposition to flavor restriction policies was a prominent sub-category for both groups, but more common in the 21-54 group. The 13-20 group was more likely to discuss opposition to minimum age laws as well as access to flavored ENDS products. The 21-54 group more commonly mentioned general vaping use behavior. CONCLUSIONS Users predicted to be in the 13-20 age group posted about different ENDS-related topics on Reddit than users predicted to be in the 21-54 age group. Future studies could use these complementary methods with social media data to gain insights from target audiences.

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.

Sign in / Sign up

Export Citation Format

Share Document