Matches in SemOpenAlex for { <https://semopenalex.org/work/W3208994109> ?p ?o ?g. }
Showing items 1 to 95 of
95
with 100 items per page.
- W3208994109 abstract "It is essential to understand what topics related to the COVID19 pandemic forms informative and uninformative content on social networks instead of general information (which contains both informative and uninformative). Uninformative content is mainly based on personal opinions and is more suitable for sentimental analysis. Whereas informative content is based on facts, figures, and reports; therefore, it is beneficial to gain a more in-depth understanding for a better strategic response to COVID-19. Despite knowing this fact, there is still a lack of study performed to investigate the aspects of informative content to gain an in-depth understanding of COVID-19 discussed topics. We aim to fill this gap through the study presented in this paper. We used the dataset containing 4719 “informative” and 5281 “uninformative” labeled tweets to realize informative aspects. Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA) are popular topic modeling techniques. However, since both are based on an unsupervised approach, it is still unknown whether LDA or LSA effectively categorizes documents and how an appropriate number of topics can be determined. Therefore, we used both techniques to analyze tweets' content. Results show that LDA outperforms LSA by achieving a topic coherence score of 0.619 on uninformative and 0.599 on informative. In addition, based on LDA's results, it is also observed that most of the words that form informative content are death, case, coronavirus, people, confirmed, total, positive, tested, number, reported indicating tested, and death cases are the most concerned topics. On the other hand, words like immunity, fatality, protocol, thread, tourist, queue, blockade, eradication, prediction, detention, concerned are most likely to form uninformative content." @default.
- W3208994109 created "2021-11-08" @default.
- W3208994109 creator A5042896398 @default.
- W3208994109 creator A5056312459 @default.
- W3208994109 date "2021-09-13" @default.
- W3208994109 modified "2023-10-01" @default.
- W3208994109 title "Comparing Topic Modeling Techniques for Identifying Informative and Uninformative Content: A Case Study on COVID-19 Tweets" @default.
- W3208994109 cites W1551384396 @default.
- W3208994109 cites W2144211451 @default.
- W3208994109 cites W2147152072 @default.
- W3208994109 cites W2346720634 @default.
- W3208994109 cites W2738669462 @default.
- W3208994109 cites W2800670487 @default.
- W3208994109 cites W2899435611 @default.
- W3208994109 cites W2952214074 @default.
- W3208994109 cites W3096393000 @default.
- W3208994109 cites W3101739755 @default.
- W3208994109 cites W3102055941 @default.
- W3208994109 cites W3109782035 @default.
- W3208994109 cites W3138387439 @default.
- W3208994109 cites W3152332785 @default.
- W3208994109 cites W4206343790 @default.
- W3208994109 doi "https://doi.org/10.1109/iicaiet51634.2021.9573878" @default.
- W3208994109 hasPublicationYear "2021" @default.
- W3208994109 type Work @default.
- W3208994109 sameAs 3208994109 @default.
- W3208994109 citedByCount "4" @default.
- W3208994109 countsByYear W32089941092022 @default.
- W3208994109 countsByYear W32089941092023 @default.
- W3208994109 crossrefType "proceedings-article" @default.
- W3208994109 hasAuthorship W3208994109A5042896398 @default.
- W3208994109 hasAuthorship W3208994109A5056312459 @default.
- W3208994109 hasConcept C105795698 @default.
- W3208994109 hasConcept C119857082 @default.
- W3208994109 hasConcept C134306372 @default.
- W3208994109 hasConcept C136764020 @default.
- W3208994109 hasConcept C142724271 @default.
- W3208994109 hasConcept C144024400 @default.
- W3208994109 hasConcept C154945302 @default.
- W3208994109 hasConcept C162446236 @default.
- W3208994109 hasConcept C170133592 @default.
- W3208994109 hasConcept C171686336 @default.
- W3208994109 hasConcept C204321447 @default.
- W3208994109 hasConcept C23123220 @default.
- W3208994109 hasConcept C2778152352 @default.
- W3208994109 hasConcept C2779134260 @default.
- W3208994109 hasConcept C2781181686 @default.
- W3208994109 hasConcept C3008058167 @default.
- W3208994109 hasConcept C33923547 @default.
- W3208994109 hasConcept C36289849 @default.
- W3208994109 hasConcept C41008148 @default.
- W3208994109 hasConcept C500882744 @default.
- W3208994109 hasConcept C518677369 @default.
- W3208994109 hasConcept C524204448 @default.
- W3208994109 hasConcept C71924100 @default.
- W3208994109 hasConceptScore W3208994109C105795698 @default.
- W3208994109 hasConceptScore W3208994109C119857082 @default.
- W3208994109 hasConceptScore W3208994109C134306372 @default.
- W3208994109 hasConceptScore W3208994109C136764020 @default.
- W3208994109 hasConceptScore W3208994109C142724271 @default.
- W3208994109 hasConceptScore W3208994109C144024400 @default.
- W3208994109 hasConceptScore W3208994109C154945302 @default.
- W3208994109 hasConceptScore W3208994109C162446236 @default.
- W3208994109 hasConceptScore W3208994109C170133592 @default.
- W3208994109 hasConceptScore W3208994109C171686336 @default.
- W3208994109 hasConceptScore W3208994109C204321447 @default.
- W3208994109 hasConceptScore W3208994109C23123220 @default.
- W3208994109 hasConceptScore W3208994109C2778152352 @default.
- W3208994109 hasConceptScore W3208994109C2779134260 @default.
- W3208994109 hasConceptScore W3208994109C2781181686 @default.
- W3208994109 hasConceptScore W3208994109C3008058167 @default.
- W3208994109 hasConceptScore W3208994109C33923547 @default.
- W3208994109 hasConceptScore W3208994109C36289849 @default.
- W3208994109 hasConceptScore W3208994109C41008148 @default.
- W3208994109 hasConceptScore W3208994109C500882744 @default.
- W3208994109 hasConceptScore W3208994109C518677369 @default.
- W3208994109 hasConceptScore W3208994109C524204448 @default.
- W3208994109 hasConceptScore W3208994109C71924100 @default.
- W3208994109 hasLocation W32089941091 @default.
- W3208994109 hasOpenAccess W3208994109 @default.
- W3208994109 hasPrimaryLocation W32089941091 @default.
- W3208994109 hasRelatedWork W2163194442 @default.
- W3208994109 hasRelatedWork W2370554703 @default.
- W3208994109 hasRelatedWork W3031970385 @default.
- W3208994109 hasRelatedWork W3047601251 @default.
- W3208994109 hasRelatedWork W3098871628 @default.
- W3208994109 hasRelatedWork W3134718057 @default.
- W3208994109 hasRelatedWork W4210277973 @default.
- W3208994109 hasRelatedWork W4289781928 @default.
- W3208994109 hasRelatedWork W4310896543 @default.
- W3208994109 hasRelatedWork W4320024195 @default.
- W3208994109 isParatext "false" @default.
- W3208994109 isRetracted "false" @default.
- W3208994109 magId "3208994109" @default.
- W3208994109 workType "article" @default.