A high-resolution temporal and geospatial content analysis of Twitter posts related to the COVID-19 pandemic

Ntompras, Charalampos; Drosatos, George; Kaldoudi, Eleni

doi:10.1007/s42001-021-00150-8

Cited by 10 publications

(7 citation statements)

References 33 publications

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“…Similar to tweet geotags, user profile locations are also intended to provide specific geographic information. Many studies to date have used location information derived from profile locations to supplement the information given by GPS geotags and provide a better sample size for analysis (Beauchamp, 2017 ; Stephens, 2020 ; Ntompras et al, 2022 ).…”

Section: Geolocation Of Twitter Data: Background and Related Workmentioning

confidence: 99%

“…Also using specialized software—in this case, Yahoo's PlaceFinder API—to extract real-world locations from profile location text, Dredze et al ( 2013 ) constructed a pipeline that is fast enough to return users' geographic locations in real time, proving useful for disease surveillance systems. Other applications of dedicated geolocation services and databases in the literature include the use of the Google Geocoding API and GeoNames 4 (Stephens, 2020 ; Ntompras et al, 2022 ). However, all these services are subjected to usage fees and/or restrictions regarding the size of the target name list as well as the speed of queries.…”

Section: Geolocation Of Twitter Data: Background and Related Workmentioning

confidence: 99%

“…Similar to research using regional properties to study survey respondents' living conditions (e.g., in urban sociology), research using Twitter data can examine the spatial distribution of tweets, compare the content of tweets across regions, or link Twitter data with external data sources by way of regional identifiers to study a variety of phenomena. Recent studies in the social sciences have used Twitter geoinformation to study the COVID-19 pandemic (Ntompras et al, 2022 ), influenza trends (Gao et al, 2018 ), crime (Hipp et al, 2018 ), language dialects (Huang et al, 2016 ), conspiracy theories (Stephens, 2020 ), polling (Beauchamp, 2017 ), travel and mobility (Blanford et al, 2015 ; Zhang et al, 2017 ; Wang et al, 2018 ; Levy et al, 2020 ), health behavior and outcomes (Wiedener and Li, 2014 ; Nguyen et al, 2017 ; Martinez et al, 2018 ), anti-immigrant attitudes (Menshikova and van Tubergen, 2022 ), happiness (Mitchell et al, 2013 ), and human behavior in environmental disasters (Murthy and Gross, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

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Efficient and Reliable Geocoding of German Twitter Data to Enable Spatial Data Linkage to Official Statistics and Other Data Sources

et al. 2022

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More and more, social scientists are using (big) digital behavioral data for their research. In this context, the social network and microblogging platform Twitter is one of the most widely used data sources. In particular, geospatial analyses of Twitter data are proving to be fruitful for examining regional differences in user behavior and attitudes. However, ready-to-use spatial information in the form of GPS coordinates is only available for a tiny fraction of Twitter data, limiting research potential and making it difficult to link with data from other sources (e.g., official statistics and survey data) for regional analyses. We address this problem by using the free text locations provided by Twitter users in their profiles to determine the corresponding real-world locations. Since users can enter any text as a profile location, automated identification of geographic locations based on this information is highly complicated. With our method, we are able to assign over a quarter of the more than 866 million German tweets collected to real locations in Germany. This represents a vast improvement over the 0.18% of tweets in our corpus to which Twitter assigns geographic coordinates. Based on the geocoding results, we are not only able to determine a corresponding place for users with valid profile locations, but also the administrative level to which the place belongs. Enriching Twitter data with this information ensures that they can be directly linked to external data sources at different levels of aggregation. We show possible use cases for the fine-grained spatial data generated by our method and how it can be used to answer previously inaccessible research questions in the social sciences. We also provide a companion R package, nutscoder, to facilitate reuse of the geocoding method in this paper.

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Section: Geolocation Of Twitter Data: Background and Related Workmentioning

confidence: 99%

Section: Geolocation Of Twitter Data: Background and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efficient and Reliable Geocoding of German Twitter Data to Enable Spatial Data Linkage to Official Statistics and Other Data Sources

et al. 2022

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“…It is interesting to note the decline in the number of COVID-19 news tweets in successive quarters of 2020 and 2021, which we explain by the appearance of an information epidemic in the first and second quarters of 2020 that news channels attempted to draw attention to. In many countries, the pandemic has caused great economic damage [ 41 ]. It is imperative to point out that the health crisis has long-term and far-reaching repercussions for the global economy beyond traditional measures.…”

Section: Resultsmentioning

confidence: 99%

Enhanced sentiment analysis regarding COVID-19 news from global channels

et al. 2022

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For a healthy society to exist, it is crucial for the media to focus on disease-related issues so that more people are widely aware of them and reduce health risks. Recently, deep neural networks have become a popular tool for textual sentiment analysis, which can provide valuable insights and real-time monitoring and analysis regarding health issues. In this paper, as part of an effort to develop an effective model that can elicit public sentiment on COVID-19 news, we propose a novel approach Cov-Att-BiLSTM for sentiment analysis of COVID-19 news headlines using deep neural networks. We integrate attention mechanisms, embedding techniques, and semantic level data labeling into the prediction process to enhance the accuracy. To evaluate the proposed approach, we compared it to several deep and machine learning classifiers using various metrics of categorization efficiency and prediction quality, and the experimental results demonstrate its superiority with 0.931 testing accuracy. Furthermore, 73,138 pandemic-related tweets posted on six global channels were analyzed by the proposed approach, which accurately reflects global coverage of COVID-19 news and vaccination.

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“…Several studies have examined the spatial differences. For example, Ntompras et al [ 14 ] conducted comparisons of the content of Twitter posts related to the COVID-19 pandemic across nations. They found several topics were triggered by local events, which implies that social media data can act as political, economic, and social monitoring in pandemics.…”

Section: Introductionmentioning

confidence: 99%

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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A high-resolution temporal and geospatial content analysis of Twitter posts related to the COVID-19 pandemic

Cited by 10 publications

References 33 publications

Efficient and Reliable Geocoding of German Twitter Data to Enable Spatial Data Linkage to Official Statistics and Other Data Sources

Efficient and Reliable Geocoding of German Twitter Data to Enable Spatial Data Linkage to Official Statistics and Other Data Sources

Enhanced sentiment analysis regarding COVID-19 news from global channels

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

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