1 |
Psychiatry on Twitter: Content Analysis of the Use of Psychiatric Terms in French
|
|
|
|
In: ISSN: 2561-326X ; JMIR Formative Research ; https://hal.archives-ouvertes.fr/hal-03614832 ; JMIR Formative Research, JMIR Publications 2022, 6 (2), pp.e18539. ⟨10.2196/18539⟩ ; https://formative.jmir.org/2022/2/e18539 (2022)
|
|
BASE
|
|
Show details
|
|
2 |
« “Twitta” “Intellectuelle” “Influenceuse” ? Être enseignante-chercheuse sur twitter »
|
|
|
|
In: ISSN: 1763-0061 ; EISSN: 1963-1812 ; Tracés : Revue de Sciences Humaines ; https://hal.archives-ouvertes.fr/hal-03592945 ; Tracés : Revue de Sciences Humaines, ENS Éditions, A paraître (2022)
|
|
BASE
|
|
Show details
|
|
3 |
Renouvellement paradigmatique dans l’analyse des discours numériques : le cas de la communication politique sur les RSN
|
|
|
|
In: ISSN: 2116-1747 ; Etudes de stylistique anglaise ; https://hal-amu.archives-ouvertes.fr/hal-03584927 ; Etudes de stylistique anglaise, Société de stylistique anglaise, Lyon, 2022, Renaissance(s)/Rebirth(s), ⟨10.4000/esa.4816⟩ ; https://journals.openedition.org/esa/4816 (2022)
|
|
BASE
|
|
Show details
|
|
4 |
Chapter 11. Consumer opinion about smoked bacon using Twitter and textual analysis: The challenge continues
|
|
|
|
In: Sensory Analysis for the Development of Meat Products ; https://hal-agrosup-dijon.archives-ouvertes.fr/hal-03575175 ; Sensory Analysis for the Development of Meat Products, Elsevier, pp.181-196, 2022, 9780128228326. ⟨10.1016/B978-0-12-822832-6.00013-8⟩ (2022)
|
|
BASE
|
|
Show details
|
|
5 |
#Bittersweet: Positive, negative, and mixed emotions in twitter posts ...
|
|
|
|
BASE
|
|
Show details
|
|
6 |
A Multilingual Dataset of COVID-19 Vaccination Attitudes on Twitter ...
|
|
|
|
BASE
|
|
Show details
|
|
7 |
A Multilingual Dataset of COVID-19 Vaccination Attitudes on Twitter ...
|
|
|
|
BASE
|
|
Show details
|
|
10 |
MULDASA: Multifactor Lexical Sentiment Analysis of Social-Media Content in Nonstandard Arabic Social Media
|
|
|
|
In: Applied Sciences; Volume 12; Issue 8; Pages: 3806 (2022)
|
|
BASE
|
|
Show details
|
|
11 |
Extracting Disaster-Related Location Information through Social Media to Assist Remote Sensing for Disaster Analysis: The Case of the Flood Disaster in the Yangtze River Basin in China in 2020
|
|
|
|
In: Remote Sensing; Volume 14; Issue 5; Pages: 1199 (2022)
|
|
BASE
|
|
Show details
|
|
12 |
Analysis of the Full-Size Russian Corpus of Internet Drug Reviews with Complex NER Labeling Using Deep Learning Neural Networks and Language Models
|
|
|
|
In: Applied Sciences; Volume 12; Issue 1; Pages: 491 (2022)
|
|
BASE
|
|
Show details
|
|
13 |
Social Media and the Pandemic: Consumption Habits of the Spanish Population before and during the COVID-19 Lockdown
|
|
|
|
In: Sustainability; Volume 14; Issue 9; Pages: 5490 (2022)
|
|
BASE
|
|
Show details
|
|
14 |
Climate Change Sentiment Analysis Using Lexicon, Machine Learning and Hybrid Approaches
|
|
|
|
In: Sustainability; Volume 14; Issue 8; Pages: 4723 (2022)
|
|
BASE
|
|
Show details
|
|
15 |
Artificial Intelligent in Education
|
|
|
|
In: Sustainability; Volume 14; Issue 5; Pages: 2862 (2022)
|
|
BASE
|
|
Show details
|
|
16 |
eHealth Engagement on Facebook during COVID-19: Simplistic Computational Data Analysis
|
|
|
|
In: International Journal of Environmental Research and Public Health; Volume 19; Issue 8; Pages: 4615 (2022)
|
|
Abstract:
Understanding social media networks and group interactions is crucial to the advancement of linguistic and cultural behavior. This includes how people accessed advice on health during COVID-19 lockdown. Some people turned to social media to access information on health when other routes were curtailed by isolation rules, particularly among older generations. Facebook public pages, groups and verified profiles using keywords “senior citizen health”, “older generations”, and “healthy living” were analyzed over a 12-month period to examine engagement with social media promoting good mental health. CrowdTangle was used to source status updates, photo and video sharing information in the English language, which resulted in an initial 116,321 posts and 6,462,065 interactions. Data analysis and visualization were used to explore large datasets, including natural language processing for “message” content discovery, word frequency and correlational analysis as well as co-word clustering. Preliminary results indicate strong links to healthy aging information shared on social media, which showed correlations to global daily confirmed cases and daily deaths. The results can identify public concerns early on and address mental health issues among senior citizens on Facebook.
|
|
Keyword:
COVID-19; data analysis; mental health; natural language processing; netnography; social media; visualization
|
|
URL: https://doi.org/10.3390/ijerph19084615
|
|
BASE
|
|
Hide details
|
|
17 |
How Do Chinese People View Cyberbullying? A Text Analysis Based on Social Media
|
|
|
|
In: International Journal of Environmental Research and Public Health; Volume 19; Issue 3; Pages: 1822 (2022)
|
|
BASE
|
|
Show details
|
|
18 |
Knowledge Discovery from Large Amounts of Social Media Data
|
|
|
|
In: Applied Sciences; Volume 12; Issue 3; Pages: 1209 (2022)
|
|
BASE
|
|
Show details
|
|
19 |
Detecting Depression Signs on Social Media: A Systematic Literature Review
|
|
|
|
In: Healthcare; Volume 10; Issue 2; Pages: 291 (2022)
|
|
BASE
|
|
Show details
|
|
20 |
A Novel Method of Generating Geospatial Intelligence from Social Media Posts of Political Leaders
|
|
|
|
In: Information; Volume 13; Issue 3; Pages: 120 (2022)
|
|
BASE
|
|
Show details
|
|
|
|