scholarly journals Leveraging Natural Language Processing to Understand Public Outlook Towards the Influenza Vaccination

2021 ◽  
Author(s):  
Ankita Agarwal ◽  
William Romine ◽  
Tanvi Banerjee
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Latent Dirichlet Allocation ◽  
Temporal Trend ◽  
Healthcare Management ◽  
Data Set ◽  
Vaccine Preventable Diseases ◽  
The Public ◽  
Social Media Platforms ◽  
Flu Vaccine

<div>Understanding public outlook in healthcare management is important in the study of the various diseases. With respect to vaccinations, which play a major role in combating vaccine-preventable diseases, the study on their acceptance or rejection by the public becomes useful. In particular to the</div><div>influenza vaccine, studies on the public opinion and views is ongoing. Social media platforms like Twitter help us to leverage thoughts and attitudes related to the flu vaccine. The data set used for our analysis contained tweets related to vaccines which were collected using vaccine-related keywords over a period of twelve months from February, 2018 to January, 2019. Out of these tweets, we filtered out the tweets specific to the flu vaccine and generated our corpus for further study. By using Latent Dirichlet Allocation (LDA), we identified eighteen topics comprising six major themes which best represented our corpus. In this paper, we discuss these six themes and subsequently analyze the trend observed in these themes over a period of twelve months. The themes identified covered various aspects related to the flu vaccine. Among the six major themes, four showed a distinctive temporal trend with respect to the annual flu season.</div><div><br></div>

scholarly journals Leveraging Natural Language Processing to Understand Public Outlook Towards the Influenza Vaccination

2021 ◽  
Author(s):  
Ankita Agarwal ◽  
William Romine ◽  
Tanvi Banerjee
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Latent Dirichlet Allocation ◽  
Temporal Trend ◽  
Healthcare Management ◽  
Data Set ◽  
Vaccine Preventable Diseases ◽  
The Public ◽  
Social Media Platforms ◽  
Flu Vaccine

<div>Understanding public outlook in healthcare management is important in the study of the various diseases. With respect to vaccinations, which play a major role in combating vaccine-preventable diseases, the study on their acceptance or rejection by the public becomes useful. In particular to the</div><div>influenza vaccine, studies on the public opinion and views is ongoing. Social media platforms like Twitter help us to leverage thoughts and attitudes related to the flu vaccine. The data set used for our analysis contained tweets related to vaccines which were collected using vaccine-related keywords over a period of twelve months from February, 2018 to January, 2019. Out of these tweets, we filtered out the tweets specific to the flu vaccine and generated our corpus for further study. By using Latent Dirichlet Allocation (LDA), we identified eighteen topics comprising six major themes which best represented our corpus. In this paper, we discuss these six themes and subsequently analyze the trend observed in these themes over a period of twelve months. The themes identified covered various aspects related to the flu vaccine. Among the six major themes, four showed a distinctive temporal trend with respect to the annual flu season.</div><div><br></div>

Mining Arabic Twitter conversations on health care: a new approach to analysing Arabic language on social media

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nael Alqtati ◽  
Jonathan A.J. Wilson ◽  
Varuna De Silva
Keyword(s):  
Health Care ◽  
Social Media ◽  
Language Processing ◽  
Data Science ◽  
Arabic Language ◽  
Marketing Communications ◽  
Social Media Analytics ◽  
Data Set ◽  
Content Type ◽  
Social Media Platforms

Purpose This paper aims to equip professionals and researchers in the fields of advertising, branding, public relations, marketing communications, social media analytics and marketing with a simple, effective and dynamic means of evaluating consumer behavioural sentiments and engagement through Arabic language and script, in vivo. Design/methodology/approach Using quantitative and qualitative situational linguistic analyses of Classical Arabic, found in Quranic and religious texts scripts; Modern Standard Arabic, which is commonly used in formal Arabic channels; and dialectical Arabic, which varies hugely from one Arabic country to another: this study analyses rich marketing and consumer messages (tweets) – as a basis for developing an Arabic language social media methodological tool. Findings Despite the popularity of Arabic language communication on social media platforms across geographies, currently, comprehensive language processing toolkits for analysing Arabic social media conversations have limitations and require further development. Furthermore, due to its unique morphology, developing text understanding capabilities specific to the Arabic language poses challenges. Practical implications This study demonstrates the application and effectiveness of the proposed methodology on a random sample of Twitter data from Arabic-speaking regions. Furthermore, as Arabic is the language of Islam, the study is of particular importance to Islamic and Muslim geographies, markets and marketing. Social implications The findings suggest that the proposed methodology has a wider potential beyond the data set and health-care sector analysed, and therefore, can be applied to further markets, social media platforms and consumer segments. Originality/value To remedy these gaps, this study presents a new methodology and analytical approach to investigating Arabic language social media conversations, which brings together a multidisciplinary knowledge of technology, data science and marketing communications.

scholarly journals Big Data, Natural Language Processing, and Deep Learning to Detect and Characterize Illicit COVID-19 Product Sales: Infoveillance Study on Twitter and Instagram

10.2196/20794 ◽  
2020 ◽  
Vol 6 (3) ◽  
pp. e20794
Author(s):  
Tim Ken Mackey ◽  
Jiawei Li ◽  
Vidya Purushothaman ◽  
Matthew Nali ◽  
Neal Shah ◽  
...  
Keyword(s):  
Public Health ◽  
Social Media ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Second Phase ◽  
The Public ◽  
Health Products ◽  
Social Media Platforms

Background The coronavirus disease (COVID-19) pandemic is perhaps the greatest global health challenge of the last century. Accompanying this pandemic is a parallel “infodemic,” including the online marketing and sale of unapproved, illegal, and counterfeit COVID-19 health products including testing kits, treatments, and other questionable “cures.” Enabling the proliferation of this content is the growing ubiquity of internet-based technologies, including popular social media platforms that now have billions of global users. Objective This study aims to collect, analyze, identify, and enable reporting of suspected fake, counterfeit, and unapproved COVID-19–related health care products from Twitter and Instagram. Methods This study is conducted in two phases beginning with the collection of COVID-19–related Twitter and Instagram posts using a combination of web scraping on Instagram and filtering the public streaming Twitter application programming interface for keywords associated with suspect marketing and sale of COVID-19 products. The second phase involved data analysis using natural language processing (NLP) and deep learning to identify potential sellers that were then manually annotated for characteristics of interest. We also visualized illegal selling posts on a customized data dashboard to enable public health intelligence. Results We collected a total of 6,029,323 tweets and 204,597 Instagram posts filtered for terms associated with suspect marketing and sale of COVID-19 health products from March to April for Twitter and February to May for Instagram. After applying our NLP and deep learning approaches, we identified 1271 tweets and 596 Instagram posts associated with questionable sales of COVID-19–related products. Generally, product introduction came in two waves, with the first consisting of questionable immunity-boosting treatments and a second involving suspect testing kits. We also detected a low volume of pharmaceuticals that have not been approved for COVID-19 treatment. Other major themes detected included products offered in different languages, various claims of product credibility, completely unsubstantiated products, unapproved testing modalities, and different payment and seller contact methods. Conclusions Results from this study provide initial insight into one front of the “infodemic” fight against COVID-19 by characterizing what types of health products, selling claims, and types of sellers were active on two popular social media platforms at earlier stages of the pandemic. This cybercrime challenge is likely to continue as the pandemic progresses and more people seek access to COVID-19 testing and treatment. This data intelligence can help public health agencies, regulatory authorities, legitimate manufacturers, and technology platforms better remove and prevent this content from harming the public.

scholarly journals Social Media Sharing of Articles About Measles in a European Context: Text Analysis Study (Preprint)

2021 ◽  
Author(s):  
Dominik Wawrzuta ◽  
Mariusz Jaworski ◽  
Joanna Gotlib ◽  
Mariusz Panczyk
Keyword(s):  
Social Media ◽  
Latent Dirichlet Allocation ◽  
Scientific Information ◽  
Vaccine Uptake ◽  
Vaccine Hesitancy ◽  
Effective Vaccine ◽  
Data Set ◽  
Vaccination Rates ◽  
Nature Of Information ◽  
Social Media Platforms

BACKGROUND Despite the existence of an effective vaccine, measles still threatens the health and lives of many Europeans. Notably, during the COVID-19 pandemic, measles vaccine uptake declined; as a result, after the pandemic, European countries will have to increase vaccination rates to restore the extent of vaccination coverage among the population. Because information obtained from social media are one of the main causes of vaccine hesitancy, knowledge of the nature of information pertaining to measles that is shared on social media may help create educational campaigns. OBJECTIVE In this study, we aim to define the characteristics of European news about measles shared on social media platforms (ie, Facebook, Twitter, and Pinterest) from 2017 to 2019. METHODS We downloaded and translated (into English) 10,305 articles on measles published in European Union countries. Using latent Dirichlet allocation, we identified main topics and estimated the sentiments expressed in these articles. Furthermore, we used linear regression to determine factors related to the number of times a given article was shared on social media. RESULTS We found that, in most European social media posts, measles is only discussed in the context of local European events. Articles containing educational information and describing world outbreaks appeared less frequently. The most common emotions identified from the study’s news data set were fear and trust. Yet, it was found that readers were more likely to share information on educational topics and the situation in Germany, Ukraine, Italy, and Samoa. A high amount of anger, joy, and sadness expressed within the text was also associated with a higher number of shares. CONCLUSIONS We identified which features of news articles were related to increased social media shares. We found that social media users prefer sharing educational news to sharing informational news. Appropriate emotional content can also increase the willingness of social media users to share an article. Effective media content that promotes measles vaccinations should contain educational or scientific information, as well as specific emotions (such as anger, joy, or sadness). Articles with this type of content may offer the best chance of disseminating vital messages to a broad social media audience.

scholarly journals Big Data, Natural Language Processing, and Deep Learning to Detect and Characterize Illicit COVID-19 Product Sales: Infoveillance Study on Twitter and Instagram (Preprint)

2020 ◽  
Author(s):  
Tim Ken Mackey ◽  
Jiawei Li ◽  
Vidya Purushothaman ◽  
Matthew Nali ◽  
Neal Shah ◽  
...  
Keyword(s):  
Public Health ◽  
Social Media ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Second Phase ◽  
The Public ◽  
Health Products ◽  
Social Media Platforms

BACKGROUND The coronavirus disease (COVID-19) pandemic is perhaps the greatest global health challenge of the last century. Accompanying this pandemic is a parallel “infodemic,” including the online marketing and sale of unapproved, illegal, and counterfeit COVID-19 health products including testing kits, treatments, and other questionable “cures.” Enabling the proliferation of this content is the growing ubiquity of internet-based technologies, including popular social media platforms that now have billions of global users. OBJECTIVE This study aims to collect, analyze, identify, and enable reporting of suspected fake, counterfeit, and unapproved COVID-19–related health care products from Twitter and Instagram. METHODS This study is conducted in two phases beginning with the collection of COVID-19–related Twitter and Instagram posts using a combination of web scraping on Instagram and filtering the public streaming Twitter application programming interface for keywords associated with suspect marketing and sale of COVID-19 products. The second phase involved data analysis using natural language processing (NLP) and deep learning to identify potential sellers that were then manually annotated for characteristics of interest. We also visualized illegal selling posts on a customized data dashboard to enable public health intelligence. RESULTS We collected a total of 6,029,323 tweets and 204,597 Instagram posts filtered for terms associated with suspect marketing and sale of COVID-19 health products from March to April for Twitter and February to May for Instagram. After applying our NLP and deep learning approaches, we identified 1271 tweets and 596 Instagram posts associated with questionable sales of COVID-19–related products. Generally, product introduction came in two waves, with the first consisting of questionable immunity-boosting treatments and a second involving suspect testing kits. We also detected a low volume of pharmaceuticals that have not been approved for COVID-19 treatment. Other major themes detected included products offered in different languages, various claims of product credibility, completely unsubstantiated products, unapproved testing modalities, and different payment and seller contact methods. CONCLUSIONS Results from this study provide initial insight into one front of the “infodemic” fight against COVID-19 by characterizing what types of health products, selling claims, and types of sellers were active on two popular social media platforms at earlier stages of the pandemic. This cybercrime challenge is likely to continue as the pandemic progresses and more people seek access to COVID-19 testing and treatment. This data intelligence can help public health agencies, regulatory authorities, legitimate manufacturers, and technology platforms better remove and prevent this content from harming the public.

Kim Kardashian: the Leader of the Influencers

2019 ◽  
Author(s):  
Laila Fariha Zein ◽  
Adib Rifqi Setiawan
Keyword(s):  
Social Media ◽  
The United States ◽  
Armenian Genocide ◽  
The Public ◽  
Self Defense ◽  
Global Commerce ◽  
The World ◽  
Social Media Platforms ◽  
International Issues ◽  
Global Influence

In today’s world, it is easier and easier to stay connected with people who are halfway across the world. Social media and a globalizing economy have created new methods of business, trade and socialization resulting in vast amounts of communication and effecting global commerce. Like her or hate her, Kimberly Noel Kardashian West as known as Kim Kardashian has capitalized on social media platforms and the globalizing economy. Kim is known for two things: famous for doing nothing and infamous for a sex tape. But Kim has not let those things define her. With over 105 million Instagram followers and 57 million Twitter followers, Kim has become a major global influence. Kim has travelled around the world, utilizing the success she has had on social media to teach make-up master classes with professional make-up artist, Mario Dedivanovic. She owns or has licensed several different businesses including: an emoji app, a personal app, a gaming app, a cosmetics line, and a fragrance line. Not to be forgotten, the Kardashian family show, ‘Keeping Up with the Kardashians’ has been on the air for ten years with Kim at the forefront. Kim also has three books: ‘Kardashian Konfidential’, ‘Dollhouse’, and ‘Selfish’. With her rising social media following, Kim has used the platforms to show her support for politicians and causes, particularly, recognition of the Armenian genocide. Kim also recently spoke at the Forbes’ women’s summit. Following the summit, Kim tweeted out her support for a recent movement on Twitter, #freeCyntoiaBrown which advocated for a young woman who claimed to have shot and killed the man who held her captive as a teenage sex slave in self-defense. Kim had her own personal lawyers help out Cyntoia on her case. Kim has also moved beyond advocating for issues within the confines of the United States. As mentioned earlier, she is known for advocating for recognition of the Armenian genocide. In the last two years, her show has made it a point to address the Armenian situation as it was then and as it is now. Kim has been recognized as a global influencer by others across the wordl. We believe Kim has become the same as political leaders when it comes to influencing the public. Kim’s story reveals that the new reality creates a perfect opportunity for mass disturbances or for initiating mass support or mass disapproval. Although Kim is typically viewed for her significance to pop culture, Kim’s business and social media following have placed her deep into the mix of international commerce. As her businesses continue to grow and thrive, we may see more of her influence on international issues and an increase in the commerce from which her businesses benefit.

Opportunities for Qualitative Analysis of Social Media Platforms in Dietetic Research and Practice

2020 ◽  
pp. 1-5
Author(s):  
Meghan Lynch ◽  
Irena Knezevic ◽  
Kennedy Laborde Ryan
Keyword(s):  
Social Media ◽  
Low Cost ◽  
Online News ◽  
News Article ◽  
Discussion Forums ◽  
Research And Practice ◽  
The Public ◽  
Related Data ◽  
Social Media Platforms ◽  
Article Comment

To date, most qualitative knowledge about individual eating patterns and the food environment has been derived from traditional data collection methods, such as interviews, focus groups, and observations. However, there currently exists a large source of nutrition-related data in social media discussions that have the potential to provide opportunities to improve dietetic research and practice. Qualitative social media discussion analysis offers a new tool for dietetic researchers and practitioners to gather insights into how the public discusses various nutrition-related topics. We first consider how social media discussion data come with significant advantages including low-cost access to timely ways to gather insights from the public, while also cautioning that social media data have limitations (e.g., difficulty verifying demographic information). We then outline 3 types of social media discussion platforms in particular: (i) online news article comment sections, (ii) food and nutrition blogs, and (iii) discussion forums. We discuss how each different type of social media offers unique insights and provide a specific example from our own research using each platform. We contend that social media discussions can contribute positively to dietetic research and practice.

TIME SCAVENGERS: REACHING THE PUBLIC THROUGH SCIENCE BLOGS AND DETERMINING WHAT CONTENT PERFORMS BEST ON SOCIAL MEDIA PLATFORMS

2020 ◽  
Author(s):  
Maggie Limbeck ◽  
◽  
Jennifer E. Bauer ◽  
Adriane R. Lam ◽  
Sarah Sheffield
Keyword(s):  
Social Media ◽  
The Public ◽  
Social Media Platforms ◽  
Science Blogs

Understanding the Role of Online Media Platforms in Airport Capital Projects and Community Outreach

2021 ◽  
pp. 036119812199347
Author(s):  
Max Z. Li ◽  
Megan S. Ryerson
Keyword(s):  
Social Media ◽  
Best Practices ◽  
Community Outreach ◽  
Capital Projects ◽  
The Public ◽  
Online Social Media ◽  
Social Media Platforms ◽  
Project Phases ◽  
Media Channels ◽  
Common Content

Community outreach and engagement efforts are critical to an airport’s role as an ever-evolving transportation infrastructure and regional economic driver. As online social media platforms continue to grow in both popularity and influence, a new engagement channel between airports and the public is emerging. However, the motivations behind and effectiveness of these social media channels remain unclear. In this work, we address this knowledge gap by better understanding the advantages, impact, and best practices of this newly emerging engagement channel available to airports. Focusing specifically on airport YouTube channels, we first document quantitative viewership metrics, and examine common content characteristics within airport YouTube videos. We then conduct interviews and site visits with relevant airport stakeholders to identify the motivations and workflow behind these videos. Finally, we facilitate sample focus groups designed to survey public perceptions of the effectiveness and value of these videos. From our four project phases, to maximize content effectiveness and community engagement potential, we synthesize the following framework of action items, recommendations, and best practices: (C) Consistency and community; (O) Organizational structure; (M) Momentum; (B) Branding and buy-in; (A) Activity; (T) Two-way engagement; (E) Enthusiasm; and (D) Depth, or as a convenient initialism, our COMBATED framework.

A guided latent Dirichlet allocation approach to investigate real-time latent topics of Twitter data during Hurricane Laura

2021 ◽  
pp. 016555152110077
Author(s):  
Sulong Zhou ◽  
Pengyu Kan ◽  
Qunying Huang ◽  
Janet Silbernagel
Keyword(s):  
Social Media ◽  
Real Time ◽  
Language Processing ◽  
Disaster Response ◽  
Domain Knowledge ◽  
Latent Dirichlet Allocation ◽  
Situational Awareness ◽  
High Performing ◽  
Latent Topics ◽  
Dirichlet Allocation

Natural disasters cause significant damage, casualties and economical losses. Twitter has been used to support prompt disaster response and management because people tend to communicate and spread information on public social media platforms during disaster events. To retrieve real-time situational awareness (SA) information from tweets, the most effective way to mine text is using natural language processing (NLP). Among the advanced NLP models, the supervised approach can classify tweets into different categories to gain insight and leverage useful SA information from social media data. However, high-performing supervised models require domain knowledge to specify categories and involve costly labelling tasks. This research proposes a guided latent Dirichlet allocation (LDA) workflow to investigate temporal latent topics from tweets during a recent disaster event, the 2020 Hurricane Laura. With integration of prior knowledge, a coherence model, LDA topics visualisation and validation from official reports, our guided approach reveals that most tweets contain several latent topics during the 10-day period of Hurricane Laura. This result indicates that state-of-the-art supervised models have not fully utilised tweet information because they only assign each tweet a single label. In contrast, our model can not only identify emerging topics during different disaster events but also provides multilabel references to the classification schema. In addition, our results can help to quickly identify and extract SA information to responders, stakeholders and the general public so that they can adopt timely responsive strategies and wisely allocate resource during Hurricane events.

Sign in / Sign up

Export Citation Format

Share Document