Spatial Analysis of Disinformation in COVID-19 Related Tweets
Misinformation can amplify humanity's most significant challenges. As the novel coronavirus spreads across the world, concerns regarding the spreading of misinformation about it and also people downplaying the severity of it are also growing. This article investigates social media activity in May 2020, specifically Twitter, with respect to COVID-19, the themes of tweets, where the discussion is emerging from, disinformation shared about the virus, and its relationship with COVID-19 incidence rate at the state and county level. A geodatabase of all geotagged COVID-19 related tweets was compiled. Multiscale Geographically Weighted Regression was employed to examine the association between social media activity, population, and the spatial variability of disease incidence; our results suggest that MGWR could explain 96.7% of the variations. Moreover, Covid-19 related twitter dataset content analysis reveals a meaningful strong spatial relationship that exists between social media activity and known cases of COVID-19. Discourses analysis was conducted on tweets to index tweets downplaying the Pandemic or disseminating disinformation; the discourses analysis findings suggest that states in where twitter users spread more misinformation and showed more resistance to pandemic management measures in May are experiencing a surge in the number of cases in July..
| Medienart: |
|
|---|
Preprint| Erscheinungsjahr: |
2021 |
|---|---|
| Erschienen: |
2021 |
| Enthalten in: |
Preprints.org - (2021) vom: 14. Juni Zur Gesamtaufnahme - year:2021 |
|---|
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Forati, Amir Masoud [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
|---|
| doi: |
10.20944/preprints202009.0213.v1 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
preprintsorg019021569 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | preprintsorg019021569 | ||
| 003 | DE-627 | ||
| 005 | 20230429203640.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 201008s2021 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.20944/preprints202009.0213.v1 |2 doi | |
| 035 | |a (DE-627)preprintsorg019021569 | ||
| 035 | |a (preprintsorg)10.20944/preprints202009.0213 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Forati, Amir Masoud |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Spatial Analysis of Disinformation in COVID-19 Related Tweets |
| 264 | 1 | |c 2021 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a Misinformation can amplify humanity's most significant challenges. As the novel coronavirus spreads across the world, concerns regarding the spreading of misinformation about it and also people downplaying the severity of it are also growing. This article investigates social media activity in May 2020, specifically Twitter, with respect to COVID-19, the themes of tweets, where the discussion is emerging from, disinformation shared about the virus, and its relationship with COVID-19 incidence rate at the state and county level. A geodatabase of all geotagged COVID-19 related tweets was compiled. Multiscale Geographically Weighted Regression was employed to examine the association between social media activity, population, and the spatial variability of disease incidence; our results suggest that MGWR could explain 96.7% of the variations. Moreover, Covid-19 related twitter dataset content analysis reveals a meaningful strong spatial relationship that exists between social media activity and known cases of COVID-19. Discourses analysis was conducted on tweets to index tweets downplaying the Pandemic or disseminating disinformation; the discourses analysis findings suggest that states in where twitter users spread more misinformation and showed more resistance to pandemic management measures in May are experiencing a surge in the number of cases in July. | ||
| 650 | 4 | |a Social Sciences | |
| 650 | 4 | |a 300 | |
| 700 | 1 | |a Ghose, Rina |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Preprints.org |g (2021) vom: 14. Juni |
| 773 | 1 | 8 | |g year:2021 |g day:14 |g month:06 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.apgeog.2021.102473 |z lizenzpflichtig |3 Volltext |
| 856 | 4 | 0 | |u http://dx.doi.org/10.20944/preprints202009.0213.v1 |z kostenfrei |3 Volltext |
| 912 | |a preprintsorg | ||
| 912 | |a SSG-OLC-PHA | ||
| 951 | |a AR | ||
| 952 | |j 2021 |b 14 |c 06 | ||