000000051 001__ 51
000000051 005__ 20231115144313.0
000000051 02470 $$ahttps://doi.org/10.3886/jy2x-qc10$$2DOI
000000051 037__ $$aADMIN
000000051 245__ $$aThe Shapes of the Fourth Estate During the Pandemic: Profiling COVID-19 News Consumption in Eight Countries
000000051 246__ $$aCOVID2020 Twitter dataset
000000051 251__ $$av1
000000051 269__ $$a2023-08-03
000000051 336__ $$aDataset
000000051 510__ $$aWu, Siqi, Yang, Cai, and Xie, Lexing. The Shapes of the Fourth Estate During the Pandemic: Profiling COVID-19 News Consumption in Eight Countries. Inter-university Consortium for Political and Social Research [distributor], 2023-08-03. https://doi.org/10.3886/jy2x-qc10
000000051 520__ $$aCOVID2020 dataset provides a new, high-volume COVID-19 tweet dataset. It was collected from March 2020 to November 2020, covering eight months in the first year of the pandemic. The list of tracked COVID-19 keywords is obtained from "Emily Chen, Kristina Lerman, and Emilio Ferrara. 2020. Tracking Social Media Discourse about the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set. JMIR Public Health and Surveillance (2020)". Those keywords include not only generic terms such as "corona virus", "covid", but also non-pharmaceutical interventions such as "lockdown", "n95", and "social distancing."
<br><br>
This dataset is comprised of tweet IDs.
000000051 536__ $$oAir Force Office of Scientific Research$$cFA2386-20-1-4064
000000051 540__ $$aThis dataset has one level of access: Login Required.
<ul>
<li>Login Required files can be downloaded directly from the dataset record, once you have created and logged in to your SOMAR account. By downloading data files you agree to <a href="https://socialmediaarchive.org/pages/?page=Terms%20of%20Use&ln=en">ICPSR’s Terms of Use</a>.</li>
000000051 650__ $$apandemic
000000051 650__ $$anews media
000000051 650__ $$amedia coverage
000000051 650__ $$amedia influence
000000051 650__ $$amedia use
000000051 651__ $$zUnited States
000000051 651__ $$zUnited Kingdom
000000051 651__ $$zAustralia
000000051 651__ $$zCanada
000000051 651__ $$zGermany
000000051 651__ $$zSpain
000000051 651__ $$zFrance
000000051 651__ $$zTurkey
000000051 655__ $$aweb platform data
000000051 655__ $$atext
000000051 720__ $$aWu, Siqi$$eData Collector$$uUniversity of Michigan$$10000-0002-1384-6908$$2ORCID$$7Personal
000000051 720__ $$aYang, Cai$$eData Curator$$uThe Australian National University$$10000-0001-9923-3530$$2ORCID$$7Personal
000000051 720__ $$aXie, Lexing$$eSupervisor$$uThe Australian National University$$10000-0001-8319-0118$$2ORCID$$7Personal
000000051 8564_ $$yTweet IDs$$9b8b2cf66-79f5-46e1-9767-51034f5b9df0$$s858269220$$uhttps://socialmediaarchive.org/record/51/files/week_43_10-19_to_10-25.txt
000000051 8564_ $$yTweet IDs$$9cff8f863-d388-4176-a3b7-6d5d4b41dad3$$s3682130280$$uhttps://socialmediaarchive.org/record/51/files/week_13_03-23_to_03-29.txt
000000051 8564_ $$yTweet IDs$$94fe1786e-0996-45a4-9678-5bf0f641767c$$s2498070060$$uhttps://socialmediaarchive.org/record/51/files/week_15_04-06_to_04-12.txt
000000051 8564_ $$yTweet IDs$$9867f3a68-1d17-4baa-82fe-9d055a84fdef$$s1555341960$$uhttps://socialmediaarchive.org/record/51/files/week_19_05-04_to_05-10.txt
000000051 8564_ $$yTweet IDs$$9092f5daa-1712-439d-b5e6-2a1265b31d37$$s1372195300$$uhttps://socialmediaarchive.org/record/51/files/week_21_05-18_to_05-24.txt
000000051 8564_ $$yTweet IDs$$961eefa0f-8205-4ae5-9967-5503957277e3$$s1187419620$$uhttps://socialmediaarchive.org/record/51/files/week_17_04-20_to_04-26_miss_3D.txt
000000051 8564_ $$yTweet IDs$$9b82ca1fa-3e43-4847-8a9f-07a9f04106b7$$s1071535680$$uhttps://socialmediaarchive.org/record/51/files/week_41_10-05_to_10-11.txt
000000051 8564_ $$yTweet IDs$$926580b1c-8581-492c-84c3-7bbcf370ee6d$$s1093118000$$uhttps://socialmediaarchive.org/record/51/files/week_23_06-01_to_06-07.txt
000000051 8564_ $$yTweet IDs$$9b567fa6b-95fd-46a3-b6f4-35183b2e2b71$$s1074894660$$uhttps://socialmediaarchive.org/record/51/files/week_25_06-15_to_06-21.txt
000000051 8564_ $$yTweet IDs$$953773a3c-13b7-474e-9ff8-4ed146ef363e$$s877999820$$uhttps://socialmediaarchive.org/record/51/files/week_37_09-07_to_09-13.txt
000000051 8564_ $$yTweet IDs$$955a43553-f612-436e-b02b-584c5ea76068$$s891847740$$uhttps://socialmediaarchive.org/record/51/files/week_33_08-10_to_08-16.txt
000000051 8564_ $$yTweet IDs$$91f447ffe-4ccd-4016-ad32-d1976a931d76$$s868355040$$uhttps://socialmediaarchive.org/record/51/files/week_31_07-28_to_08-02_miss_1D.txt
000000051 8564_ $$yTweet IDs$$9f555f6c1-4512-4bb1-922f-1a7ffc21e908$$s786700960$$uhttps://socialmediaarchive.org/record/51/files/week_39_09-21_to_09-27.txt
000000051 8564_ $$yTweet IDs$$938191d34-0917-42c2-820b-52014b64af91$$s775548020$$uhttps://socialmediaarchive.org/record/51/files/week_35_08-24_to_08-30.txt
000000051 8564_ $$yTweet IDs$$9db9f8038-9a42-4039-9b3b-43bcb1f919a4$$s756712180$$uhttps://socialmediaarchive.org/record/51/files/week_47_11-16_to_11-22.txt
000000051 8564_ $$yTweet IDs$$91a8a038a-015a-4a98-a9a5-a39d9ea3842c$$s630662160$$uhttps://socialmediaarchive.org/record/51/files/week_45_11-02_to_11-08.txt
000000051 906__ $$a2020-03$$b2020-11
000000051 907__ $$a2020-03$$b2020-11
000000051 908__ $$aTwitter
000000051 910__ $$aapplication programming interface (API)
000000051 911__ $$aWe want to understand on Twitter, during the pandemic,
RQ1: For a given country, in terms of political leaning, what is the breadth of readership for the media outlets there? 
RQ2: For a given media, in terms of political leaning, how does its audience profile vary across different countries?
000000051 912__ $$aData were collected from the Twitter filtered streaming API.
To reduce the computational load, we processed one week’s data in every two weeks. The resulting dataset contains 18 calendar weeks over an eight-month period (Mar-Nov, or week 13 to week 47 in 2020). We experienced several server glitches during data collection, and lost data for two entire weeks (week 27, 29) and another four days (in week 17, 31). In total, we obtained 999,040,035 COVID-19 tweets posted by 62,687,121 users.
000000051 913__ $$aGeolocated Twitter users in the following countries: United States, United Kingdom, Australia, Canada, Germany, Spain, France and Turkey
000000051 915__ $$anonprobability.availability
000000051 923__ $$ahttps://github.com/computationalmedia/media_landscape
000000051 980__ $$aDatasets
000000051 980__ $$aX
000000051 981__ $$aPublished$$b2023-08-03