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Psychiatry on Twitter: Content Analysis of the Use of Psychiatric Terms in French
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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)
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| 2 |
« “Twitta” “Intellectuelle” “Influenceuse” ? Être enseignante-chercheuse sur twitter »
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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)
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| 3 |
Renouvellement paradigmatique dans l’analyse des discours numériques : le cas de la communication politique sur les RSN
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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)
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| 4 |
Chapter 11. Consumer opinion about smoked bacon using Twitter and textual analysis: The challenge continues
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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)
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| 5 |
#Bittersweet: Positive, negative, and mixed emotions in twitter posts ...
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| 6 |
A Multilingual Dataset of COVID-19 Vaccination Attitudes on Twitter ...
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| 7 |
A Multilingual Dataset of COVID-19 Vaccination Attitudes on Twitter ...
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| 10 |
MULDASA: Multifactor Lexical Sentiment Analysis of Social-Media Content in Nonstandard Arabic Social Media
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In: Applied Sciences; Volume 12; Issue 8; Pages: 3806 (2022)
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| 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
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In: Remote Sensing; Volume 14; Issue 5; Pages: 1199 (2022)
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Abstract:
Social media texts spontaneously produced and uploaded by the public contain a wealth of disaster information. As a supplementary data source for remote sensing, they have played an important role in disaster reduction and emergency response in recent years. However, social media also has certain flaws, such as insufficient location information, etc. This affects the efficiency of combining these data with remote sensing data. For flood disasters in particular, extensively flooded areas limit the distribution of social media data, which makes it difficult for these data to function as they should. In this paper, we propose a disaster reduction framework to solve these problems. We first used an approach that was based on search engine and lexical rules to automatically extract disaster-related location information from social media texts. Then, we combined the extracted information with the upload location of social media itself to construct location-pointing relationships. These relationships were used to build a new social network, which can be used in combination with remote sensing images for disaster analysis. The analysis integrated the advantages of social media and remote sensing. It can not only provide macro disaster information in the study area but can also assist in evaluating the disaster situation in different flooded areas from the perspective of public observation. In addition, the timeliness of social media data also improved the continuity and situational awareness of flood monitoring. A case study of the flood disaster in the Yangtze River Basin in China in 2020 was used to verify the effectiveness of the method described in this paper.
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Keyword:
disaster reduction; flood disaster; information mining; remote sensing; social media
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URL: https://doi.org/10.3390/rs14051199
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| 12 |
Analysis of the Full-Size Russian Corpus of Internet Drug Reviews with Complex NER Labeling Using Deep Learning Neural Networks and Language Models
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In: Applied Sciences; Volume 12; Issue 1; Pages: 491 (2022)
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| 13 |
Social Media and the Pandemic: Consumption Habits of the Spanish Population before and during the COVID-19 Lockdown
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In: Sustainability; Volume 14; Issue 9; Pages: 5490 (2022)
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| 14 |
Climate Change Sentiment Analysis Using Lexicon, Machine Learning and Hybrid Approaches
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In: Sustainability; Volume 14; Issue 8; Pages: 4723 (2022)
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| 15 |
Artificial Intelligent in Education
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In: Sustainability; Volume 14; Issue 5; Pages: 2862 (2022)
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| 16 |
eHealth Engagement on Facebook during COVID-19: Simplistic Computational Data Analysis
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In: International Journal of Environmental Research and Public Health; Volume 19; Issue 8; Pages: 4615 (2022)
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| 17 |
How Do Chinese People View Cyberbullying? A Text Analysis Based on Social Media
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In: International Journal of Environmental Research and Public Health; Volume 19; Issue 3; Pages: 1822 (2022)
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| 18 |
Knowledge Discovery from Large Amounts of Social Media Data
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In: Applied Sciences; Volume 12; Issue 3; Pages: 1209 (2022)
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| 19 |
Detecting Depression Signs on Social Media: A Systematic Literature Review
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In: Healthcare; Volume 10; Issue 2; Pages: 291 (2022)
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| 20 |
A Novel Method of Generating Geospatial Intelligence from Social Media Posts of Political Leaders
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In: Information; Volume 13; Issue 3; Pages: 120 (2022)
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