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 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 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 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 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

Psychological Stress Detection from Social Media Data using a Novel Hybrid Model

2018 ◽  
Vol 6 (8) ◽  
pp. 853-862
Author(s):  
Shaikha Hajera ◽  
Mohammed Mahmood Ali
Keyword(s):  
Social Media ◽  
Psychological Stress ◽  
Hybrid Model ◽  
Stress Detection ◽  
Social Media Data ◽  
Media Data

Smart De-Identification of Social Media Data

2014 ◽  
Author(s):  
Kathleen M. Carley ◽  
L. R. Carley ◽  
Jonathan Storrick
Keyword(s):  
Social Media ◽  
Social Media Data ◽  
Media Data

The Psychology of Job Loss: Using Social Media Data to Characterize and Predict Unemployment

2016 ◽  
Author(s):  
Davide Proserpio ◽  
Scott Counts ◽  
Apurv Jain
Keyword(s):  
Social Media ◽  
Job Loss ◽  
Social Media Data ◽  
Media Data
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