Detecting early warning indicators to the rise of COVID-19 infection cases in the context of U.S.: An exploratory data analysis
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Autor:innen
Autor:in (Körperschaft)
Publikationsdatum
2022
Typ der Arbeit
Master
Studiengang
Sammlung
Typ
11 - Studentische Arbeit
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
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Verlag / Herausgebende Institution
Hochschule für Wirtschaft FHNW
Verlagsort / Veranstaltungsort
Olten
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
This work aims to investigate if social media data, Twitter in particular can be used to detect early warning indicators of COVID-19 pandemic in the United States (US). To demonstrate the viability of this work, English tweets were collected with a hasghtag of COVID-19 related topics ranges from 12th March to end of April 2020. With the help of with N-gram language model and Term Frequency and Inverse Document Frequency (TF-IDF) significant N-grams (N=2) such as (“new, york”), (“social, distancing”), (“stay, safe”), (“toilet, paper”), (“wash, hand”), (“tested, positive”), (look, like), (“front, line”), (“grocery, store”) etc. are extracted. The analysis shows that the appearances of the N-grams in Twitter directly reflect the characteristics of the infection cases and are almost similarly distributed over different clusters. This study also reveals that the tweets of (“new, york”) increases with (“stay, home”), (“social, distancing”), (“stay, safe”), (“look, like”) and (“tested positive”); and decreases with (“toilet, paper”). Ngrams with such relationships are recognized as indicators and are validated with the mapping of number of infection cases. Results show that social media data can project the actual scenario of infection curve and able to detect early warning indicators once the pandemic is moderately recognized.
Schlagwörter
Fachgebiet (DDC)
330 - Wirtschaft
Veranstaltung
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Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
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Begutachtung
Open Access-Status
Lizenz
Zitation
ADNAN MORSHED, Jaman, 2022. Detecting early warning indicators to the rise of COVID-19 infection cases in the context of U.S.: An exploratory data analysis. Olten: Hochschule für Wirtschaft FHNW. Verfügbar unter: https://irf.fhnw.ch/handle/11654/48615