Geospatial analysis of misinformation in COVID-19 related tweets

Forati, Amir Masoud; Ghose, Rina

doi:10.1016/j.apgeog.2021.102473

Cited by 34 publications

(29 citation statements)

References 57 publications

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“…Cuomo et al [ 15 ] performed a more granular analysis and investigated the longitudinal and geospatial relationships between volumes of self-reporting COVID-19 cases and elevated risks of virus spreading in the United States at the county level. Similar studies have found geolocated tweets on COVID-19 symptoms, concerns, and experiences are indicative of officially reported COVID-19 cases at the county level in the United States [ 16 ] and volumes of misinformation are related to increased COVID-19 cases at the state and county level in the United States [ 17 ]. Currently, few have investigated the temporal variations in public sentiment in a high geospatial resolution.…”

Section: Introductionmentioning

confidence: 80%

Temporal Variations and Spatial Disparities in Public Sentiment Toward COVID-19 and Preventive Practices in the United States: Infodemiology Study of Tweets

Kahanek¹,

Yu²,

Hong³

et al. 2021

JMIR Infodemiology

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Background During the COVID-19 pandemic, US public health authorities and county, state, and federal governments recommended or ordered certain preventative practices, such as wearing masks, to reduce the spread of the disease. However, individuals had divergent reactions to these preventive practices. Objective The purpose of this study was to understand the variations in public sentiment toward COVID-19 and the recommended or ordered preventive practices from the temporal and spatial perspectives, as well as how the variations in public sentiment are related to geographical and socioeconomic factors. Methods The authors leveraged machine learning methods to investigate public sentiment polarity in COVID-19–related tweets from January 21, 2020 to June 12, 2020. The study measured the temporal variations and spatial disparities in public sentiment toward both general COVID-19 topics and preventive practices in the United States. Results In the temporal analysis, we found a 4-stage pattern from high negative sentiment in the initial stage to decreasing and low negative sentiment in the second and third stages, to the rebound and increase in negative sentiment in the last stage. We also identified that public sentiment to preventive practices was significantly different in urban and rural areas, while poverty rate and unemployment rate were positively associated with negative sentiment to COVID-19 issues. Conclusions The differences between public sentiment toward COVID-19 and the preventive practices imply that actions need to be taken to manage the initial and rebound stages in future pandemics. The urban and rural differences should be considered in terms of the communication strategies and decision making during a pandemic. This research also presents a framework to investigate time-sensitive public sentiment at the county and state levels, which could guide local and state governments and regional communities in making decisions and developing policies in crises.

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Section: Introductionmentioning

confidence: 80%

Temporal Variations and Spatial Disparities in Public Sentiment Toward COVID-19 and Preventive Practices in the United States: Infodemiology Study of Tweets

Kahanek¹,

Yu²,

Hong³

et al. 2021

JMIR Infodemiology

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“…Inaccurate information related to the ongoing COVID-19 pandemic and the safety of vaccines and their side effects spread quickly through social media, especially via retweets on Twitter. Therefore, it has become more important to address misinformation ( Budhwani & Sun, 2020 ; Forati & Ghose, 2021 ; Singh et al, 2020 ). Prior research has explored the essential characteristics of retweet prediction, including retweeting behaviors, emoji and playfulness engagement, and number of followers.…”

Section: Discussionmentioning

confidence: 99%

Predicting the popularity of tweets by analyzing public opinion and emotions in different stages of Covid-19 pandemic

Mahdikhani

2022

International Journal of Information Management Data Insights

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In this study, public opinion and emotions regarding different stages of the Covid-19 pandemic from the outbreak of the disease to the distribution of vaccines were analyzed to predict the popularity of tweets. More than 1.25 million English tweets were collected, posted from January 20, 2020, to May 29, 2021. Five sets of content features, including topic analysis, topics plus TF-IDF vectorizer, bag of words (BOW) by TF-IDF vectorizer, document embedding, and document embedding plus TF-IDF vectorizer, were extracted and applied to supervised machine learning algorithms to generate a predictive model for the retweetability of posted tweets. The analysis showed that tweets with higher emotional intensity are more popular than tweets containing information on Covid-19 pandemic. This study can help to detect the public emotions during the pandemic and after vaccination and predict the retweetability of posted tweets in different stages of Covid-19 pandemic.

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“…Finally, regarding the limitations of our study, it lacks quantitative analysis of social, demographic or mobility factors that could additionally explain the results of our problem areas. Those factors have been widely studied using other GIS methods, as multiscale geographically weighted regression to contrast the strong spatial relation between Covid‐19 spread and other variables, such as social media activity (Forati & Ghose, 2021 ), density and urbanization degree (Dutta, Basu, & Das, 2021 ), or even built environment using analytical hierarchy methods (Rahman, Islam, & Islam, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Are spatial patterns of Covid‐19 changing? Spatiotemporal analysis over four waves in the region of Cantabria, Spain

Guerra

Salcines

Cantarero

2022

Transactions in GIS

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This research approaches the empirical study of the pandemic from a social science perspective. The main goal is to reveal spatiotemporal changes in Covid‐19, at regional scale, using GIS technologies and the emerging three‐dimensional bins method. We analyze a case study of the region of Cantabria (northern Spain) based on 29,288 geocoded positive Covid‐19 cases in the four waves from the outset in March 2020 to June 2021. Our results suggest three main spatial processes: a reversal in the spatial trend, spreading first followed by contraction in the third and fourth waves; then the reduction of hot spots that represent problematic areas because of high presence of cases and growing trends; and finally, an increase in cold spots. All this generates relevant knowledge to help policy‐makers from regional governments to design efficient containment and mitigation strategies. Our research is conducted from a geoprevention perspective, based on the application of targeted measures depending on spatial patterns of Covid‐19 in real time. It represents an opportunity to reduce the socioeconomic impact of global containment measures in pandemic management.

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Geospatial analysis of misinformation in COVID-19 related tweets

Cited by 34 publications

References 57 publications

Temporal Variations and Spatial Disparities in Public Sentiment Toward COVID-19 and Preventive Practices in the United States: Infodemiology Study of Tweets

Temporal Variations and Spatial Disparities in Public Sentiment Toward COVID-19 and Preventive Practices in the United States: Infodemiology Study of Tweets

Predicting the popularity of tweets by analyzing public opinion and emotions in different stages of Covid-19 pandemic

Are spatial patterns of Covid‐19 changing? Spatiotemporal analysis over four waves in the region of Cantabria, Spain

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