#ColonCancer: Social Media Discussions About Colorectal Cancer During the COVID-19 Pandemic

2022 ◽  
Author(s):  
Vishal R. Patel ◽  
Sofia Gereta ◽  
Christopher J. Blanton ◽  
Alexander L. Chu ◽  
Neha K. Reddy ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Social Media ◽  
Early Detection ◽  
Language Processing ◽  
Application Programming Interface ◽  
Cancer Awareness ◽  
Personal Experiences ◽  
Social Media Platforms ◽  
Application Programming

PURPOSE Colorectal cancer (CRC) is the second leading cause of cancer-related mortality worldwide. Social media platforms such as Twitter are extensively used to communicate about cancer care, yet little is known about the role of these online platforms in promoting early detection or sharing the lived experiences of patients with CRC. This study tracked Twitter discussions about CRC and characterized participating users to better understand public communication and perceptions of CRC during the COVID-19 pandemic. METHODS Tweets containing references to CRC were collected from January 2020 to April 2021 using Twitter's Application Programming Interface. Account metadata was used to predict user demographic information and classify users as either organizations, individuals, clinicians, or influencers. We compared the number of impressions across users and analyzed the content of tweets using natural language processing models to identify prominent topics of discussion. RESULTS There were 72,229 unique CRC-related tweets by 31,170 users. Most users were male (66%) and older than 40 years (57%). Individuals accounted for most users (44%); organizations (35%); clinicians (19%); and influencers (2%). Influencers made the most median impressions (35,853). Organizations made the most overall impressions (1,067,189,613). Tweets contained the following topics: bereavement (20%), appeals for early detection (20%), research (17%), National Colorectal Cancer Awareness Month (15%), screening access (14%), and risk factors (14%). CONCLUSION Discussions about CRC largely focused on bereavement and early detection. Online coverage of National Colorectal Cancer Awareness Month and personal experiences with CRC effectively stimulated goal-oriented tweets about early detection. Our findings suggest that although Twitter is commonly used for communicating about CRC, partnering with influencers may be an effective strategy for improving communication of future public health recommendations related to CRC.

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 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 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 Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set (Preprint)

2020 ◽  
Author(s):  
Emily Chen ◽  
Kristina Lerman ◽  
Emilio Ferrara
Keyword(s):  
Social Media ◽  
Data Collection ◽  
Application Programming Interface ◽  
Time Data ◽  
Data Set ◽  
Twitter Data ◽  
Social Media Platforms ◽  
Application Programming ◽  
Twitter Activity ◽  
Online Conversation

BACKGROUND At the time of this writing, the coronavirus disease (COVID-19) pandemic outbreak has already put tremendous strain on many countries' citizens, resources, and economies around the world. Social distancing measures, travel bans, self-quarantines, and business closures are changing the very fabric of societies worldwide. With people forced out of public spaces, much of the conversation about these phenomena now occurs online on social media platforms like Twitter. OBJECTIVE In this paper, we describe a multilingual COVID-19 Twitter data set that we are making available to the research community via our COVID-19-TweetIDs GitHub repository. METHODS We started this ongoing data collection on January 28, 2020, leveraging Twitter’s streaming application programming interface (API) and Tweepy to follow certain keywords and accounts that were trending at the time data collection began. We used Twitter’s search API to query for past tweets, resulting in the earliest tweets in our collection dating back to January 21, 2020. RESULTS Since the inception of our collection, we have actively maintained and updated our GitHub repository on a weekly basis. We have published over 123 million tweets, with over 60% of the tweets in English. This paper also presents basic statistics that show that Twitter activity responds and reacts to COVID-19-related events. CONCLUSIONS It is our hope that our contribution will enable the study of online conversation dynamics in the context of a planetary-scale epidemic outbreak of unprecedented proportions and implications. This data set could also help track COVID-19-related misinformation and unverified rumors or enable the understanding of fear and panic—and undoubtedly more.

scholarly journals Using Twitter for sentiment analysis towards AstraZeneca/Oxford, Pfizer/BioNTech and Moderna COVID-19 vaccines

2021 ◽  
pp. postgradmedj-2021-140685
Author(s):  
Robert Marcec ◽  
Robert Likic
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
English Language ◽  
Vaccination Campaign ◽  
Application Programming Interface ◽  
Social Media Platforms ◽  
Application Programming ◽  
Source Of Information ◽  
Programming Interface ◽  
Over Time

IntroductionA worldwide vaccination campaign is underway to bring an end to the SARS-CoV-2 pandemic; however, its success relies heavily on the actual willingness of individuals to get vaccinated. Social media platforms such as Twitter may prove to be a valuable source of information on the attitudes and sentiment towards SARS-CoV-2 vaccination that can be tracked almost instantaneously.Materials and methodsThe Twitter academic Application Programming Interface was used to retrieve all English-language tweets mentioning AstraZeneca/Oxford, Pfizer/BioNTech and Moderna vaccines in 4 months from 1 December 2020 to 31 March 2021. Sentiment analysis was performed using the AFINN lexicon to calculate the daily average sentiment of tweets which was evaluated longitudinally and comparatively for each vaccine throughout the 4 months.ResultsA total of 701 891 tweets have been retrieved and included in the daily sentiment analysis. The sentiment regarding Pfizer and Moderna vaccines appeared positive and stable throughout the 4 months, with no significant differences in sentiment between the months. In contrast, the sentiment regarding the AstraZeneca/Oxford vaccine seems to be decreasing over time, with a significant decrease when comparing December with March (p<0.0000000001, mean difference=−0.746, 95% CI=−0.915 to −0.577).ConclusionLexicon-based Twitter sentiment analysis is a valuable and easily implemented tool to track the sentiment regarding SARS-CoV-2 vaccines. It is worrisome that the sentiment regarding the AstraZeneca/Oxford vaccine appears to be turning negative over time, as this may boost hesitancy rates towards this specific SARS-CoV-2 vaccine.

scholarly journals Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set

10.2196/19273 ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. e19273 ◽  
Author(s):  
Emily Chen ◽  
Kristina Lerman ◽  
Emilio Ferrara
Keyword(s):  
Social Media ◽  
Data Collection ◽  
Application Programming Interface ◽  
Time Data ◽  
Data Set ◽  
Twitter Data ◽  
Social Media Platforms ◽  
Application Programming ◽  
Twitter Activity ◽  
Online Conversation

Background At the time of this writing, the coronavirus disease (COVID-19) pandemic outbreak has already put tremendous strain on many countries' citizens, resources, and economies around the world. Social distancing measures, travel bans, self-quarantines, and business closures are changing the very fabric of societies worldwide. With people forced out of public spaces, much of the conversation about these phenomena now occurs online on social media platforms like Twitter. Objective In this paper, we describe a multilingual COVID-19 Twitter data set that we are making available to the research community via our COVID-19-TweetIDs GitHub repository. Methods We started this ongoing data collection on January 28, 2020, leveraging Twitter’s streaming application programming interface (API) and Tweepy to follow certain keywords and accounts that were trending at the time data collection began. We used Twitter’s search API to query for past tweets, resulting in the earliest tweets in our collection dating back to January 21, 2020. Results Since the inception of our collection, we have actively maintained and updated our GitHub repository on a weekly basis. We have published over 123 million tweets, with over 60% of the tweets in English. This paper also presents basic statistics that show that Twitter activity responds and reacts to COVID-19-related events. Conclusions It is our hope that our contribution will enable the study of online conversation dynamics in the context of a planetary-scale epidemic outbreak of unprecedented proportions and implications. This data set could also help track COVID-19-related misinformation and unverified rumors or enable the understanding of fear and panic—and undoubtedly more.

Coming out of the closet? Audience reactions to the heteronormative disruptive advertising

2020 ◽  
Vol 3 (2) ◽  
pp. 75-102
Author(s):  
Ramasela Semang L. Mathobela ◽  
Shepherd Mpofu ◽  
Samukezi Mrubula-Ngwenya
Keyword(s):  
Social Media ◽  
Human Rights ◽  
Coming Out ◽  
Gender Issues ◽  
Qualitative Approach ◽  
Positive Development ◽  
Global Trend ◽  
Social Media Platforms ◽  
The Media

An emerging global trend of brands advertising their products through LGBTIQ+ individuals and couples indicates growth of gender awareness across the globe. The media, through advertising, deconstructs homophobia and associated cultures through the use of LGBTIQ+s in commercials. This qualitative research paper centres the advancement of debates on human rights and social media as critical in the interaction between corporates and consumers. The Gillette, Chicken Licken‘s Soul Sisters and We the Brave advertisements were used to critically analyse how audiences react to the use of LGBTIQ+ characters and casts through comments posted on the brands‘ social media platforms. Further, the paper explored the role of social media in the mediation of significant gender issues such as homosexuality that are considered taboo to engage in. The paper used a qualitative approach. Using the digital ethnography method to observe comments and interactions from the chosen advertisement‘s online platforms, the paper employed queer and constructionist theories to deconstruct discourses around same-sex relations as used in commercials, especially in quasiconservative. The data used in the paper included thirty comments of the brands customers and audiences obtained from Twitter, Facebook and YouTube. The paper concludes there are positive development in human rights awareness as seen through advertisements and campaigns that use LGBTIQ+ communities in a positive light across the world.

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.

Revolutionizing Mental Health with Social Media

2019 ◽  
pp. 259-284
Author(s):  
Munmun De Choudhury
Keyword(s):  
Social Media ◽  
Well Being ◽  
Linguistic Expression ◽  
Ethical Considerations ◽  
Self Disclosure ◽  
Vulnerable Communities ◽  
Tailored Interventions ◽  
Challenges And Opportunities ◽  
Social Media Platforms

Social media platforms have emerged as rich repositories of information relating to people’s activities, emotions, and linguistic expression. This chapter highlights how these data may be harnessed to reason about human mental and psychological well-being. It also discusses the emergent role of social media in providing a platform of self-disclosure and support to distressed and vulnerable communities. It reflects on how this new line of research bears potential for informing the design of timely and tailored interventions, provisions for improved personal and societal well-being assessment, privacy and ethical considerations, and the challenges and opportunities of the increasing ubiquity of social media.

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