Harnessing AI for Health Message Generation: The Folic Acid Message Engine (Preprint)

2021 ◽  
Author(s):  
Ralf Schmaelzle ◽  
Shelby Wilcox
Keyword(s):  
Social Media ◽  
Folic Acid ◽  
Public Awareness ◽  
Ethical Challenges ◽  
Online Study ◽  
Successful Communication ◽  
Machine Learning Model ◽  
Starting Point ◽  
Scientific Potential ◽  
B Vitamin

BACKGROUND Communication campaigns utilizing social media can raise public awareness, but they are difficult to sustain. One barrier is the need to constantly generate and post novel, yet on-topic messages, which creates a resource-intensive bottleneck. OBJECTIVE Here, we harness the latest advances in artificial intelligence (AI) to build a system that can generate a large number of candidate messages, which could be used for a campaign. The topic of folic acid, a B-vitamin that helps prevent major birth defects, serves as an example, but the system can work with other topics as well. METHODS We used the Generative-Pre-trained-Transformer-2 (GPT2) architecture, a machine learning model trained on a large natural language corpus, and fine tuned it using a dataset of auto-downloaded tweets about #folicacid. The fine tuned model was then used as a message engine, that is to create new messages about this topic. We carried out an online study to gauge how human raters evaluate the AI-generated tweet messages compared to original, human-crafted messages. RESULTS We find that the Folic Acid Message Engine can easily create several hundreds of new messages that appear natural to humans. Online raters evaluated the clarity and quality of a selected sample AI-generated tweets as on par with human-generated ones. Overall, these results show that it is feasible to use such a message engine to suggest messages for online campaigns. CONCLUSIONS The message engine can serve as a starting point for more sophisticated AI-guided message creation systems for health communication. Beyond the practical potential of such systems for campaigns in the age of social media, they also hold great scientific potential for quantitative analysis of message characteristics that promote successful communication. We discuss future developments and obvious ethical challenges that need to be addressed as AI technologies for health persuasion enter the stage.

Hugh Jackman vs. Skin Cancer Awareness Month: Promoting Public Interest in Basal Cell Carcinoma (Preprint)

2020 ◽  
Author(s):  
Trevor Torgerson ◽  
Jennifer Austin ◽  
Jam Khojasteh ◽  
Matt Vassar
Keyword(s):  
Social Media ◽  
Skin Cancer ◽  
Basal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Basal Cell ◽  
Moving Average ◽  
Public Awareness ◽  
Sun Exposure ◽  
Cancer Awareness ◽  
Autoregressive Integrated Moving Average

BACKGROUND Public awareness for BCC is particularly important, as its major risk factors — increased sun exposure and number of sunburns — are largely preventable. OBJECTIVE Determine whether social media posts from celebrities has an affect on public awareness of basal cell carcinoma. METHODS We used Google Trends to investigate whether public awareness for basal cell carcinoma (BCC) increased following social media posts from Hugh Jackman. To forecast the expected search interest for BCC, melanoma and sunscreen in the event that each celebrity had not posted on social media, we used the autoregressive integrated moving average (ARIMA) algorithm. RESULTS We found that social media posts from Hugh Jackman, a well-known actor, increased relative search interest above the expected search interest calculated using an ARIMA forecasting model. CONCLUSIONS Our results also suggest that increasing awareness by Skin Cancer Awareness Month may be less effective for BCC, but a celebrity spokesperson has the potential to increase awareness. BCC is largely preventable, so increasing awareness could lead to a decrease in incidence.

scholarly journals Public Awareness: What Climate Change Scientists Should Consider

2020 ◽  
Vol 12 (20) ◽  
pp. 8369
Author(s):  
Mohammad Rahimi
Keyword(s):  
Climate Change ◽  
Social Media ◽  
Large Scale ◽  
Public Awareness ◽  
Connected Network ◽  
Multi Scale ◽  
Valuable Addition ◽  
Scientific Approach ◽  
Design Solutions ◽  
Mitigate Climate Change

In this Opinion, the importance of public awareness to design solutions to mitigate climate change issues is highlighted. A large-scale acknowledgment of the climate change consequences has great potential to build social momentum. Momentum, in turn, builds motivation and demand, which can be leveraged to develop a multi-scale strategy to tackle the issue. The pursuit of public awareness is a valuable addition to the scientific approach to addressing climate change issues. The Opinion is concluded by providing strategies on how to effectively raise public awareness on climate change-related topics through an integrated, well-connected network of mavens (e.g., scientists) and connectors (e.g., social media influencers).

scholarly journals The Promoter, Celebrity, and Joker Roles in Journalists’ Social Media Performance

2021 ◽  
Vol 7 (1) ◽  
pp. 205630512199064
Author(s):  
Claudia Mellado ◽  
Alfred Hermida
Keyword(s):  
Social Media ◽  
Public Spheres ◽  
Professional Norms ◽  
Starting Point ◽  
Private And Public ◽  
Media Performance

One of the main challenges of studying journalistic roles in social media practice is that the profession’s conceptual boundaries have become increasingly blurred. Social media has developed as a space used by audiences to consume, share, and discuss news and information, offering novel locations for journalists to intervene at professional and personal levels and in private and public spheres. This article takes the “journalistic ego” domain as its starting point to examine how journalists perform three specific roles on social media: the promoter, the celebrity, and the joker. To investigate these roles in journalistic performance, the article situates their emergence and operationalization in a broader epistemological context, examining how journalists engage with, contest, and/or diverge from different professional norms and practices, as well as the conflict between traditional and social media-specific roles of journalists.

Fuzzy based feature engineering architecture for sentiment analysis of medical discussion over online social networks

2021 ◽  
pp. 1-13
Author(s):  
C S Pavan Kumar ◽  
L D Dhinesh Babu
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Membership Function ◽  
Online Social Networks ◽  
Learning Model ◽  
Feature Engineering ◽  
Machine Learning Model ◽  
Social Media Platforms

Sentiment analysis is widely used to retrieve the hidden sentiments in medical discussions over Online Social Networking platforms such as Twitter, Facebook, Instagram. People often tend to convey their feelings concerning their medical problems over social media platforms. Practitioners and health care workers have started to observe these discussions to assess the impact of health-related issues among the people. This helps in providing better care to improve the quality of life. Dementia is a serious disease in western countries like the United States of America and the United Kingdom, and the respective governments are providing facilities to the affected people. There is much chatter over social media platforms concerning the patients’ care, healthy measures to be followed to avoid disease, check early indications. These chatters have to be carefully monitored to help the officials take necessary precautions for the betterment of the affected. A novel Feature engineering architecture that involves feature-split for sentiment analysis of medical chatter over online social networks with the pipeline is proposed that can be used on any Machine Learning model. The proposed model used the fuzzy membership function in refining the outputs. The machine learning model has obtained sentiment score is subjected to fuzzification and defuzzification by using the trapezoid membership function and center of sums method, respectively. Three datasets are considered for comparison of the proposed and the regular model. The proposed approach delivered better results than the normal approach and is proved to be an effective approach for sentiment analysis of medical discussions over online social networks.

The Sentiment Analysis Reviewing Indosat Services from Twitter Using the Naive Bayes Classifier

2020 ◽  
Vol 1 (2) ◽  
pp. 61-66
Author(s):  
Febri Astiko ◽  
Achmad Khodar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Learning Model ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Machine Learning Model ◽  
Bayes Algorithm

This study aims to design a machine learning model of sentiment analysis on Indosat Ooredoo service reviews on social media twitter using the Naive Bayes algorithm as a classifier of positive and negative labels. This sentiment analysis uses machine learning to get patterns an model that can be used again to predict new data.

The Effect of Instagram Influencers on Purchase Intentions Mediated by Brand Image on Cosmetic Products (Study on Gen Z Women)

2021 ◽  
Vol 4 (2) ◽  
pp. 111-123
Author(s):  
Dewi Tamara ◽  
Lidiya Heriyati ◽  
Tsabita Hanifa ◽  
Michelle Carmen
Keyword(s):  
Social Media ◽  
Purchase Intention ◽  
Convergent Validity ◽  
Discriminant Validity ◽  
Brand Image ◽  
Public Awareness ◽  
Daily Basis ◽  
Purchase Intentions ◽  
Generation Z ◽  
The Relationship

Rise of internet usage gives a sense of urgency for marketer to develop enchanted promotion methods through the help of social media. This research focuses on analyze the correlation between social media influencer and purchase intention with brand image as mediating variables. Object of this research is Generation Z women since their perspectives are rarely elaborated in previous research. Sample of this research is Generation Z women, actively using make-ups and skincare, and use Instagram in their daily basis. Validity is measured through convergent validity and discriminant validity, while reliability is measured through cronbach’s alpha and composite reliability. Hypotheses are measured using PLS-SEM and considered as significant if t-value > t-table. Results indicate that social media influencer significantly correlated with purchase intention when mediated with brand image. Specifically, numbers of followers, high-activity on social media, and influencer credibility influence brand image and purchase intention in significant ways. Moreover, positive brand image, public awareness, and brand uniqueness determined as mediating factors on the relationship social media influencer and purchase intention.  

Global Social Media Ethics and the Responsibility of Journalism

2021 ◽  
Author(s):  
David A. Craig
Keyword(s):  
Social Media ◽  
Communication Network ◽  
Public Discourse ◽  
Common Ground ◽  
Structural Characteristics ◽  
Media Ethics ◽  
Ethical Challenges ◽  
Ethical Implications ◽  
Ethical Frameworks ◽  
Media Practices

Social media have amplified and accelerated the ethical challenges that communicators, professional and otherwise, face worldwide. The work of ethical journalism, with a priority of truthful communication, offers a paradigm case for examining the broader challenges in the global social media network. The evolution of digital technologies and the attendant expansion of the communication network pose ethical difficulties for journalists connected with increased speed and volume of information, a diminished place in the network, and the cross-border nature of information flow. These challenges are exacerbated by intentional manipulation of social media, human-run or automated, in many countries including internal suppression by authoritarian regimes and foreign influence operations to spread misinformation. In addition, structural characteristics of social media platforms’ filtering and recommending algorithms pose ethical challenges for journalism and its role in fostering public discourse on social and political issues, although a number of studies have called aspects of the “filter bubble” hypothesis into question. Research in multiple countries, mostly in North America and Europe, has examined social media practices in journalism, including two issues central to social media ethics—verification and transparency—but ethical implications have seldom been discussed explicitly in the context of ethical theory. Since the 1980s and 1990s, scholarship focused on normative theorizing in relation to journalism has matured and become more multicultural and global. Scholars have articulated a number of ethical frameworks that could deepen analysis of the challenges of social media in the practice of journalism. However, the explicit implications of these frameworks for social media have largely gone unaddressed. A large topic of discussion in media ethics theory has been the possibility of universal or common principles globally, including a broadening of discussion of moral universals or common ground in media ethics beyond Western perspectives that have historically dominated the scholarship. In order to advance media ethics scholarship in the 21st-century environment of globally networked communication, in which journalists work among a host of other actors (well-intentioned, ill-intentioned, and automated), it is important for researchers to apply existing media ethics frameworks to social media practices. This application needs to address the challenges that social media create when crossing cultures, the common difficulties they pose worldwide for journalistic verification practices, and the responsibility of journalists for countering misinformation from malicious actors. It is also important to the further development of media ethics scholarship that future normative theorizing in the field—whether developing new frameworks or redeveloping current ones—consider journalistic responsibilities in relation to social media in the context of both the human and nonhuman actors in the communication network. The developing scholarly literature on the ethics of algorithms bears further attention from media ethics scholars for the ways it may provide perspectives that are complementary to existing media ethics frameworks that have focused on human actors and organizations.

Artificial Intelligence, Social Media and Depression. ‘Patient’ Autonomy Revisited

2021 ◽  
Vol 66 (Special Issue) ◽  
pp. 133-133
Author(s):  
Regina Mueller ◽  
◽  
Sebastian Laacke ◽  
Georg Schomerus ◽  
Sabine Salloch ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Mental Disorders ◽  
Patient Autonomy ◽  
Ethical Challenges ◽  
Traditional Concept ◽  
Ethical Implications ◽  
Health Related ◽  
Twitter Users ◽  
Using Data

"Artificial Intelligence (AI) systems are increasingly being developed and various applications are already used in medical practice. This development promises improvements in prediction, diagnostics and treatment decisions. As one example, in the field of psychiatry, AI systems can already successfully detect markers of mental disorders such as depression. By using data from social media (e.g. Instagram or Twitter), users who are at risk of mental disorders can be identified. This potential of AI-based depression detectors (AIDD) opens chances, such as quick and inexpensive diagnoses, but also leads to ethical challenges especially regarding users’ autonomy. The focus of the presentation is on autonomy-related ethical implications of AI systems using social media data to identify users with a high risk of suffering from depression. First, technical examples and potential usage scenarios of AIDD are introduced. Second, it is demonstrated that the traditional concept of patient autonomy according to Beauchamp and Childress does not fully account for the ethical implications associated with AIDD. Third, an extended concept of “Health-Related Digital Autonomy” (HRDA) is presented. Conceptual aspects and normative criteria of HRDA are discussed. As a result, HRDA covers the elusive area between social media users and patients. "

Framing in Health Communication: Identifying the Factors That Influence the Re-posting of Social Media Messages during COVID-19 Outbreaks in China (Preprint)

2021 ◽  
Author(s):  
Jingzhong Xie ◽  
Jun Lai ◽  
Dongying Zhang
Keyword(s):  
Social Media ◽  
Health Communication ◽  
Health Information ◽  
Information Needs ◽  
Information Sources ◽  
Negative Binomial ◽  
Public Awareness ◽  
Public Health Agencies ◽  
Media Messages ◽  
Action Frame

BACKGROUND Social media has become an important tool to implement risk communication in COVID-19 pandemic, and made health information can gain more exposure by re-posting. OBJECTIVE This paper attempts to identify the factors associated with re-posting of social media messages about health information METHODS Content analysis was applied to scrutinize 4396 Weibo posts that were posted by national and provincial public health agencies Weibo accounts and identified features of information sources and information features, and adopted Zero-Inflated Negative Binomial (ZINB) model to analyze the association between these features and the frequency of message being re-posted. RESULTS Results showed that the followers and the governmental level of information sources are correlated with increased message reposting. The information features, such as hashtags#, picture, video, emotional(!), and the usage of severity, reassurance, efficacy and action frame were associated with increased message reposting behaviors, while hyperlink and usage of uncertainty frame correlated with reduced message reposting behaviors. CONCLUSIONS The features of health information sources, structures , style and content should be paid close attention by health organizations and medical professionals to satisfy the public’s information needs and preferences, promote the public's health engagement. Suitable information systems designing, and health communication strategies making during different stages of the pandemic may improve public awareness of the COVID-19, alleviate negative emotions, promote preventive measures to curb the spread of the virus.

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