scholarly journals Artificial Intelligence-enabled analysis of social media data to understand public perceptions of COVID-19 contact tracing apps (Preprint)

10.2196/26618 ◽  
2020 ◽  
Author(s):  
Kathrin Cresswell ◽  
Ahsen Tahir ◽  
Zakariya Sheikh ◽  
Zain Hussain ◽  
Andrés Domínguez Hernández ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Public Perceptions ◽  
Contact Tracing ◽  
Social Media Data ◽  
Media Data

scholarly journals Harnessing artificial intelligence technology and social media data to support Cultural Ecosystem Service assessments

2021 ◽  
Author(s):  
Lukas Egarter Vigl ◽  
Thomas Marsoner ◽  
Valentina Giombini ◽  
Caroline Pecher ◽  
Heidi Simion ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Ecosystem Service ◽  
Social Media Data ◽  
Artificial Intelligence Technology ◽  
Cultural Ecosystem Service ◽  
Media Data

scholarly journals Contemporary English Pain Descriptors as Detected on Social Media Using Artificial Intelligence and Emotion Analytics Algorithms: Cross-sectional Study (Preprint)

2021 ◽  
Author(s):  
Ming Yi Tan ◽  
Charlene Enhui Goh ◽  
Hee Hon Tan
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
English Language ◽  
Reliability And Validity ◽  
Mcgill Pain Questionnaire ◽  
Cross Sectional ◽  
Social Media Data ◽  
Pain Questionnaire ◽  
Pain Descriptors ◽  
Media Data

BACKGROUND Pain description is fundamental to health care. The McGill Pain Questionnaire (MPQ) has been validated as a tool for the multidimensional measurement of pain; however, its use relies heavily on language proficiency. Although the MPQ has remained unchanged since its inception, the English language has evolved significantly since then. The advent of the internet and social media has allowed for the generation of a staggering amount of publicly available data, allowing linguistic analysis at a scale never seen before. OBJECTIVE The aim of this study is to use social media data to examine the relevance of pain descriptors from the existing MPQ, identify novel contemporary English descriptors for pain among users of social media, and suggest a modification for a new MPQ for future validation and testing. METHODS All posts from social media platforms from January 1, 2019, to December 31, 2019, were extracted. Artificial intelligence and emotion analytics algorithms (Crystalace and CrystalFeel) were used to measure the emotional properties of the text, including <i>sarcasm</i>, <i>anger</i>, <i>fear</i>, <i>sadness</i>, <i>joy</i>, and <i>valence</i>. Word2Vec was used to identify new pain descriptors associated with the original descriptors from the MPQ. Analysis of count and pain intensity formed the basis for proposing new pain descriptors and determining the order of pain descriptors within each subclass. RESULTS A total of 118 new associated words were found via Word2Vec. Of these 118 words, 49 (41.5%) words had a count of at least 110, which corresponded to the count of the bottom 10% (8/78) of the original MPQ pain descriptors. The count and intensity of pain descriptors were used to formulate the inclusion criteria for a new pain questionnaire. For the suggested new pain questionnaire, 11 existing pain descriptors were removed, 13 new descriptors were added to existing subclasses, and a new <i>Psychological</i> subclass comprising 9 descriptors was added. CONCLUSIONS This study presents a novel methodology using social media data to identify new pain descriptors and can be repeated at regular intervals to ensure the relevance of pain questionnaires. The original MPQ contains several potentially outdated pain descriptors and is inadequate for reporting the psychological aspects of pain. Further research is needed to examine the reliability and validity of the revised MPQ.

scholarly journals Health, Psychosocial, and Social issues emanating from COVID-19 pandemic based on Social Media Comments using Natural Language Processing (Preprint)

2020 ◽  
Author(s):  
Oladapo Oyebode ◽  
Chinenye Ndulue ◽  
Ashfaq Adib ◽  
Dinesh Mulchandani ◽  
Banuchitra Suruliraj ◽  
...  
Keyword(s):  
Social Media ◽  
Natural Language Processing ◽  
Language Processing ◽  
Social Issues ◽  
Public Perceptions ◽  
Psychosocial Issues ◽  
Social Media Data ◽  
Health Related ◽  
The Impact ◽  
Media Data

BACKGROUND The COVID-19 pandemic has caused a global health crisis that affects many aspects of human lives. In the absence of vaccines and antivirals, several behavioural change and policy initiatives, such as physical distancing, have been implemented to control the spread of the coronavirus. Social media data can reveal public perceptions toward how governments and health agencies across the globe are handling the pandemic, as well as the impact of the disease on people regardless of their geographic locations in line with various factors that hinder or facilitate the efforts to control the spread of the pandemic globally. OBJECTIVE This paper aims to investigate the impact of the COVID-19 pandemic on people globally using social media data. METHODS We apply natural language processing (NLP) and thematic analysis to understand public opinions, experiences, and issues with respect to the COVID-19 pandemic using social media data. First, we collect over 47 million COVID-19-related comments from Twitter, Facebook, YouTube, and three online discussion forums. Second, we perform data preprocessing which involves applying NLP techniques to clean and prepare the data for automated theme extraction. Third, we apply context-aware NLP approach to extract meaningful keyphrases or themes from over 1 million randomly-selected comments, as well as compute sentiment scores for each theme and assign sentiment polarity (i.e., positive, negative, or neutral) based on the scores using lexicon-based technique. Fourth, we categorize related themes into broader themes. RESULTS A total of 34 negative themes emerged, out of which 15 are health-related issues, psychosocial issues, and social issues related to the COVID-19 pandemic from the public perspective. Some of the health-related issues are increased mortality, health concerns, struggling health systems, and fitness issues; while some of the psychosocial issues include frustrations due to life disruptions, panic shopping, and expression of fear. Social issues include harassment, domestic violence, and wrong societal attitude. In addition, 20 positive themes emerged from our results. Some of the positive themes include public awareness, encouragement, gratitude, cleaner environment, online learning, charity, spiritual support, and innovative research. CONCLUSIONS We uncover various negative and positive themes representing public perceptions toward the COVID-19 pandemic and recommend interventions that can help address the health, psychosocial, and social issues based on the positive themes and other remedial ideas rooted in research. These interventions will help governments, health professionals and agencies, institutions, and individuals in their efforts to curb the spread of COVID-19 and minimize its impact, as well as in reacting to any future pandemics.

scholarly journals Sentiment Analysis in Social Media Data for Depression Detection Using Artificial Intelligence: A Review

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Nirmal Varghese Babu ◽  
E. Grace Mary Kanaga
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Sentiment Analysis ◽  
Social Media Data ◽  
Depression Detection ◽  
Media Data

scholarly journals Artificial Intelligent Technologies for the Construction Industry: How Are They Perceived and Utilized in Australia?

2022 ◽  
Vol 8 (1) ◽  
pp. 16
Author(s):  
Massimo Regona ◽  
Tan Yigitcanlar ◽  
Bo Xia ◽  
Rita Yi Man Li
Keyword(s):  
Social Media ◽  
Technology Adoption ◽  
Construction Industry ◽  
Methodological Approach ◽  
Public Perceptions ◽  
Social Media Analytics ◽  
Social Media Data ◽  
The Public ◽  
Negative Perceptions ◽  
Media Data

Artificial intelligence (AI) is a powerful technology that can be utilized throughout a construction project lifecycle. Transition to incorporate AI technologies in the construction industry has been delayed due to the lack of know-how and research. There is also a knowledge gap regarding how the public perceives AI technologies, their areas of application, prospects, and constraints in the construction industry. This study aims to explore AI technology adoption prospects and constraints in the Australian construction industry by analyzing social media data. This study adopted social media analytics, along with sentiment and content analyses of Twitter messages (n = 7906), as the methodological approach. The results revealed that: (a) robotics, internet-of-things, and machine learning are the most popular AI technologies in Australia; (b) Australian public sentiments toward AI are mostly positive, whilst some negative perceptions exist; (c) there are distinctive views on the opportunities and constraints of AI among the Australian states/territories; (d) timesaving, innovation, and digitalization are the most common AI prospects; and (e) project risk, security of data, and lack of capabilities are the most common AI constraints. This study is the first to explore AI technology adoption prospects and constraints in the Australian construction industry by analyzing social media data. The findings inform the construction industry on public perceptions and prospects and constraints of AI adoption. In addition, it advocates the search for finding the most efficient means to utilize AI technologies. The study helps public perceptions and prospects and constraints of AI adoption to be factored in construction industry technology adoption.

scholarly journals Contemporary English Pain Descriptors as Detected on Social Media Using Artificial Intelligence and Emotion Analytics Algorithms: Cross-sectional Study

10.2196/31366 ◽  
2021 ◽  
Vol 5 (11) ◽  
pp. e31366
Author(s):  
Ming Yi Tan ◽  
Charlene Enhui Goh ◽  
Hee Hon Tan
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
English Language ◽  
Reliability And Validity ◽  
Mcgill Pain Questionnaire ◽  
Cross Sectional ◽  
Social Media Data ◽  
Pain Questionnaire ◽  
Pain Descriptors ◽  
Media Data

Background Pain description is fundamental to health care. The McGill Pain Questionnaire (MPQ) has been validated as a tool for the multidimensional measurement of pain; however, its use relies heavily on language proficiency. Although the MPQ has remained unchanged since its inception, the English language has evolved significantly since then. The advent of the internet and social media has allowed for the generation of a staggering amount of publicly available data, allowing linguistic analysis at a scale never seen before. Objective The aim of this study is to use social media data to examine the relevance of pain descriptors from the existing MPQ, identify novel contemporary English descriptors for pain among users of social media, and suggest a modification for a new MPQ for future validation and testing. Methods All posts from social media platforms from January 1, 2019, to December 31, 2019, were extracted. Artificial intelligence and emotion analytics algorithms (Crystalace and CrystalFeel) were used to measure the emotional properties of the text, including sarcasm, anger, fear, sadness, joy, and valence. Word2Vec was used to identify new pain descriptors associated with the original descriptors from the MPQ. Analysis of count and pain intensity formed the basis for proposing new pain descriptors and determining the order of pain descriptors within each subclass. Results A total of 118 new associated words were found via Word2Vec. Of these 118 words, 49 (41.5%) words had a count of at least 110, which corresponded to the count of the bottom 10% (8/78) of the original MPQ pain descriptors. The count and intensity of pain descriptors were used to formulate the inclusion criteria for a new pain questionnaire. For the suggested new pain questionnaire, 11 existing pain descriptors were removed, 13 new descriptors were added to existing subclasses, and a new Psychological subclass comprising 9 descriptors was added. Conclusions This study presents a novel methodology using social media data to identify new pain descriptors and can be repeated at regular intervals to ensure the relevance of pain questionnaires. The original MPQ contains several potentially outdated pain descriptors and is inadequate for reporting the psychological aspects of pain. Further research is needed to examine the reliability and validity of the revised MPQ.

Post, Mine, and Be Disturbed: Social Media Data Mining

PsycCRITIQUES ◽  
2016 ◽  
Vol 61 (51) ◽  
Author(s):  
Daniel Keyes
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Social Media Data ◽  
Media Data
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