2. Data Description
3.1. Preliminaries: GeoCOV19Tweets Dataset
3.2. Collection Methodology
3.2.1. Hydrating Tweets
3.2.2. Determining Tweet Origin
3.2.4. Selecting Metropolitan Areas
3.2.5. Location-Based Filtering
3.3. Related Datasets
3.4. Ethical Considerations
3.5. Possible Compliance with FAIR
3.6. Statistical Summary
4. Detailed Statistics and Usage Notes
4.1. Statistics on Sentiment Scores
4.2. Statistics on Hashtags
4.3. Possible Use-Cases
- Given that different metropolitan areas were impacted differently by COVID-19 (in particular, New York was hit hard in the early days), how is this impact reflected in social media?
- Can tweets from areas (with different socioeconomic profiles) within metropolitan cities shed light on how socioeconomic status is correlated with COVID-19 impacts, and how such correlations manifest on social media? While limited surveys and studies have confirmed that COVID-19 disproportionately affected lower socioeconomic-status groups, to our knowledge, a full study through a social media lens has not yet emerged.
- Given policy measures that were enacted in different cities over time, what can we say about longitudinal differences (especially in terms of sentiment) between these cities?
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Montreal is excluded due to its significant French-speaking population, as discussed in Collection Methodology.
According to its documentation, GeoCOV19Tweets itself used the Python-based TextBlob package (on the text of the tweet) to automatically obtain a sentiment score.
While the place_type usually contains a string indicating the type of the place (e.g., ‘city’), in some cases, it may store the zipcode due to the need for a reverse geocoding service, as discussed in Collection Methodology.
https://textblob.readthedocs.io/en/dev/ (accessed on 15 June 2021).
https://scholarslab.github.io/learn-twarc/ (accessed on 15 June 2021).
The GeoCOV19Tweets dataset begins on 20 March 2020.
https://www.geocod.io/ (accessed on 15 June 2021).
According to https://www.census.gov/newsroom/press-releases/2020/south-west-fastest-growing.html (accessed on 15 June 2021); and https://www12.statcan.gc.ca/census-recensement/2016/as-sa/98-200-x/2016001/98-200-x2016001-eng.cfm (accessed on 15 June 2021).
As mentioned, the original dataset began sampling tweets on 20 March 2020. The average sentiment score for March is therefore taken over an 11-day period.
|Top Left||Bottom Right||Tweet Count||Percentage (%)|
|New York||(41.415634, −74.485085)||(40.411124, −71.853181)||20,979||40.3163|
|Los Angeles||(34.820691, −118.946542)||(33.602688, −117.275379)||13,893||26.6988|
|Toronto||(44.383080, −80.114152)||(43.284905, −78.473654)||5505||10.5792|
|Chicago||(42.391280, −88.501901)||(41.122449, −87.009653)||3171||6.0939|
|Houston||(30.218201, −95.934175)||(29.136616, −94.729970)||2220||4.2663|
|Phoenix||(33.942208, −112.752517)||(32.812873, −111.362060)||1123||2.1581|
|Philadelphia||(40.158714, −75.403683)||(39.777982, −74.913390)||1413||2.7154|
|San Antonio||(29.850468, −99.185990)||(28.902995, −97.884110)||697||1.3395|
|San Diego||(33.249462, −117.432605)||(32.533032, −116.733257)||1411||2.7116|
|Dallas||(33.249352, −97.130478)||(32.326729, −96.342209)||1624||3.1209|
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