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Security in Computing",{},{"id":24,"text":1186,"url":24,"identifiers":1187},"ClarkDDWilsonDA comparison of commercial and military computer security policiesProceedings of the 1987 IEEE Symposium19871987 IEEE Symposium on Security and PrivacyApril 27-29, 1987Oakland, CAPiscataway, NJIEEE Computer Society",{"doi":1188},"10.1109\u002FSP.1987.10001",{"id":24,"text":1190,"url":24,"identifiers":1191},"10.1109\u002FSECPRI.1988.8106",{"doi":1190},{"id":24,"text":1193,"url":24,"identifiers":1194},"10.1109\u002FRISP.1990.63851",{"doi":1193},{"id":24,"text":1196,"url":24,"identifiers":1197},"10.1145\u002F1666420.1666444",{"doi":1196},{"id":24,"text":1199,"url":24,"identifiers":1200},"CavoukianAPrivacy by Design20132013-05-247 Foundational 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Achievablehttp:\u002F\u002Fwww.privacybydesign.ca\u002Findex.php\u002Fbig-data-privacy-together-is-achievable\u002F6GpVPLaXK",{},{"id":1208,"createTime":1209,"updateTime":1210,"relativeEntities":1211,"slug":1212,"properties":1213,"entityType":112,"verifyStatus":113,"verifyTime":1209,"verifyNote":114,"syncStatus":23,"languages":1231,"translateLanguages":24,"viewCount":25,"primaryUrl":1232,"fullTextUrl":24,"authors":1233,"publicationType":315,"publisherRelationship":1303,"citationCount":1335,"citationInfo":1336,"publishDate":24,"publishYear":24,"citationAnalyzeStatus":1340,"lastCitationAnalyze":1210,"indexDatabases":24,"openAccess":24,"references":1341,"isForceReanalyzing":542},"72825785-2b48-450d-809a-37aa6b4b1a8c","2024-09-25T18:33:49.420+00:00","2025-12-18T20:41:08.242+00:00",[],"Leveraging-Transfer-Learning-to-Analyze-Opinions-Attitudes-and-Behavioral-Intentions-Toward-COVID-19-Vaccines-Social-Media-Content-and-Temporal-Analysis",{"mag":1214,"keywords":1216,"pmc":1217,"openalex":1219,"abstract":1221,"title":1223,"pm":1225,"doi":1227,"gsPaper":1229},{"VOID":1215},"3180262449",{},{"VOID":1218},"8360338",{"VOID":1220},"W3180262449",{"EN":1222},"\u003Cjats:sec>\n            \u003Cjats:title>Background\u003C\u002Fjats:title>\n            \u003Cjats:p>The COVID-19 vaccine is considered to be the most promising approach to alleviate the pandemic. However, in recent surveys, acceptance of the COVID-19 vaccine has been low. To design more effective outreach interventions, there is an urgent need to understand public perceptions of COVID-19 vaccines.\u003C\u002Fjats:p>\n          \u003C\u002Fjats:sec>\n          \u003Cjats:sec>\n            \u003Cjats:title>Objective\u003C\u002Fjats:title>\n            \u003Cjats:p>Our objective was to analyze the potential of leveraging transfer learning to detect tweets containing opinions, attitudes, and behavioral intentions toward COVID-19 vaccines, and to explore temporal trends as well as automatically extract topics across a large number of tweets.\u003C\u002Fjats:p>\n          \u003C\u002Fjats:sec>\n          \u003Cjats:sec>\n            \u003Cjats:title>Methods\u003C\u002Fjats:title>\n            \u003Cjats:p>We developed machine learning and transfer learning models to classify tweets, followed by temporal analysis and topic modeling on a dataset of COVID-19 vaccine–related tweets posted from November 1, 2020 to January 31, 2021. We used the F1 values as the primary outcome to compare the performance of machine learning and transfer learning models. The statistical values and P values from the Augmented Dickey-Fuller test were used to assess whether users’ perceptions changed over time. The main topics in tweets were extracted by latent Dirichlet allocation analysis.\u003C\u002Fjats:p>\n          \u003C\u002Fjats:sec>\n          \u003Cjats:sec>\n            \u003Cjats:title>Results\u003C\u002Fjats:title>\n            \u003Cjats:p>We collected 2,678,372 tweets related to COVID-19 vaccines from 841,978 unique users and annotated 5000 tweets. The F1 values of transfer learning models were 0.792 (95% CI 0.789-0.795), 0.578 (95% CI 0.572-0.584), and 0.614 (95% CI 0.606-0.622) for these three tasks, which significantly outperformed the machine learning models (logistic regression, random forest, and support vector machine). The prevalence of tweets containing attitudes and behavioral intentions varied significantly over time. Specifically, tweets containing positive behavioral intentions increased significantly in December 2020. In addition, we selected tweets in the following categories: positive attitudes, negative attitudes, positive behavioral intentions, and negative behavioral intentions. We then identified 10 main topics and relevant terms for each category.\u003C\u002Fjats:p>\n          \u003C\u002Fjats:sec>\n          \u003Cjats:sec>\n            \u003Cjats:title>Conclusions\u003C\u002Fjats:title>\n            \u003Cjats:p>Overall, we provided a method to automatically analyze the public understanding of COVID-19 vaccines from real-time data in social media, which can be used to tailor educational programs and other interventions to effectively promote the public acceptance of COVID-19 vaccines.\u003C\u002Fjats:p>\n          \u003C\u002Fjats:sec>",{"EN":1224},"Leveraging Transfer Learning to Analyze Opinions, Attitudes, and Behavioral Intentions Toward COVID-19 Vaccines: Social Media Content and Temporal Analysis",{"VOID":1226},"34254942",{"VOID":1228},"10.2196\u002F30251",{"VOID":1230},"[]",[116],"https:\u002F\u002Fwww.jmir.org\u002F2021\u002F8\u002Fe30251",[1234,1256,1281],{"id":1235,"sortIndex":295,"researcher":24,"roles":1236,"affiliations":1237,"properties":1249},"eaa51b26-c438-4d91-91ac-6f5ca8a0318b",[],[1238],{"id":1239,"sortIndex":25,"affiliation":1240,"properties":24},"7b10e046-e83a-4a57-b3a9-615e7798fe61",{"id":1241,"createTime":1242,"updateTime":1243,"relativeEntities":1244,"slug":1245,"properties":1246,"entityType":45,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25},"b7d0959f-e901-402f-8c43-2eeab69a1b53","2024-01-12T08:15:02.635+00:00","2024-09-25T18:33:49.464+00:00",[],"Department-of-Medical-Informatics-West-China-Hospital-Sichuan-University-Chengdu-China",{"title":1247},{"VI":1248},"Department of Medical Informatics, West China Hospital, Sichuan University, Chengdu, China",{"openalex":1250,"orcid":1252,"title":1254},{"VOID":1251},"A5100456639",{"VOID":1253},"https:\u002F\u002Forcid.org\u002F0000-0002-1369-4625",{"EN":1255},"Jialin Liu",{"id":1257,"sortIndex":25,"researcher":24,"roles":1258,"affiliations":1259,"properties":1274},"b0c52571-758e-405e-a3f0-21558b583a09",[],[1260],{"id":1261,"sortIndex":25,"affiliation":1262,"properties":1271},"5a4c0dae-247f-4828-9909-20f39401ad34",{"id":1263,"createTime":1264,"updateTime":1265,"relativeEntities":1266,"slug":1267,"properties":1268,"entityType":45,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25},"6b1baf20-ed30-40d4-82e7-ab093a130402","2023-12-17T17:25:38.452+00:00","2024-10-16T22:21:38.286+00:00",[],"Department-of-Biomedical-Informatics-Vanderbilt-University-Medical-Center-Nashville-TN-USA",{"title":1269},{"VI":1270},"Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA",{"title":1272},{"EN":1273},"Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States",{"openalex":1275,"orcid":1277,"title":1279},{"VOID":1276},"A5091713549",{"VOID":1278},"https:\u002F\u002Forcid.org\u002F0000-0002-5003-5354",{"EN":1280},"Siru Liu",{"id":1282,"sortIndex":165,"researcher":24,"roles":1283,"affiliations":1284,"properties":1296},"4e8dda5b-315d-46b3-ad05-2c957e551da4",[],[1285],{"id":1286,"sortIndex":25,"affiliation":1287,"properties":24},"09cef0ac-672d-462a-bf5b-b9bb2c48ee33",{"id":1288,"createTime":1289,"updateTime":1290,"relativeEntities":1291,"slug":1292,"properties":1293,"entityType":45,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25},"d8325c03-fdfa-44fe-85ab-f4136ed93b54","2023-12-19T22:15:16.279+00:00","2024-09-25T18:33:49.446+00:00",[],"West-China-Medical-School-Sichuan-University-Chengdu-China",{"title":1294},{"VI":1295},"West China Medical School, Sichuan University, Chengdu, China",{"openalex":1297,"orcid":1299,"title":1301},{"VOID":1298},"A5101728259",{"VOID":1300},"https:\u002F\u002Forcid.org\u002F0000-0002-7051-0503",{"EN":1302},"Jili Li",{"url":24,"publisher":1304,"properties":1329},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1305,"slug":10,"properties":1306,"entityType":22,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25,"subjectFields":1312,"manageAffiliations":1313,"indexDatabases":1314,"url":87,"thumbnailPath":24,"statistic":24,"gsStatistic":24,"type":24,"analyzePriority":24},[],{"country":1307,"issn":1308,"introduce":1309,"eissn":1310,"title":1311},{"VOID":13},{"VOID":15},{"EN":17},{"VOID":19},{"EN":21},[],[],[1315,1322],{"id":68,"indexDatabase":1316,"url":83,"indexYears":24,"academicFieldIds":1321,"indexDatabaseRanking":24},{"id":70,"createTime":71,"updateTime":72,"relativeEntities":1317,"label":1318,"description":1319,"key":79,"publicationTags":1320,"standard":24},[],{"EN":75,"VI":75},{"VI":77,"EN":78},[81,82],[85,86],{"id":49,"indexDatabase":1323,"url":62,"indexYears":63,"academicFieldIds":1328,"indexDatabaseRanking":66},{"id":51,"createTime":52,"updateTime":53,"relativeEntities":1324,"label":1325,"description":1326,"key":59,"publicationTags":1327,"standard":24},[],{"EN":56,"VI":56},{"EN":56,"VI":58},[61],[65],{"volume":1330,"pages":1332,"issue":1334},{"VOID":1331},"23",{"VOID":1333},"e30251",{"VOID":734},29,{"total":1335,"publishYear":24,"statisticByYear":1337},{"2021":165,"2022":1338,"2023":1339,"2024":201},10,13,"ERROR_IN_GET_PLATFORM_ID",[1342,1345,1348,1351,1354,1357,1360,1363,1366,1369,1372,1375,1378,1381,1384,1387,1390,1393,1396,1399,1402,1405,1408,1411,1414,1417,1420,1423,1426,1429,1432,1435,1439,1443,1446,1450,1453,1456,1460,1463,1466,1469,1472,1475,1478,1481],{"id":24,"text":1343,"url":24,"identifiers":1344},"WHO Coronavirus Disease (COVID-19) DashboardWorld Health Organization20202021-01-31https:\u002F\u002Fcovid19.who.int\u002F",{},{"id":24,"text":1346,"url":24,"identifiers":1347},"10.1080\u002F10410236.2020.1838096",{"doi":1346},{"id":24,"text":1349,"url":24,"identifiers":1350},"10.1002\u002Fjmv.25965",{"doi":1349},{"id":24,"text":1352,"url":24,"identifiers":1353},"10.1111\u002Feci.13364",{"doi":1352},{"id":24,"text":1355,"url":24,"identifiers":1356},"10.1136\u002Fbmj.m4576",{"doi":1355},{"id":24,"text":1358,"url":24,"identifiers":1359},"10.1001\u002Fjamanetworkopen.2020.25594",{"doi":1358},{"id":24,"text":1361,"url":24,"identifiers":1362},"ChodoshSWhy only half of Americans say they would get a COVID-19 vaccinePopular Science202006012021-01-31https:\u002F\u002Fwww.popsci.com\u002Fstory\u002Fhealth\u002Fcovid-19-vaccine-poll\u002F",{},{"id":24,"text":1364,"url":24,"identifiers":1365},"D'souzaGDowdyDWhat is herd immunity and how can we achieve it with COVID-19?Johns Hopkins Bloomberg School of Public Health20202021-01-31https:\u002F\u002Fwww.jhsph.edu\u002Fcovid-19\u002Farticles\u002Fachieving-herd-immunity-with-covid19.html",{},{"id":24,"text":1367,"url":24,"identifiers":1368},"10.1016\u002Fj.ijnurstu.2020.103854",{"doi":1367},{"id":24,"text":1370,"url":24,"identifiers":1371},"10.1016\u002Fj.vaccine.2015.04.036",{"doi":1370},{"id":24,"text":1373,"url":24,"identifiers":1374},"10.1080\u002F21645515.2020.1780846",{"doi":1373},{"id":24,"text":1376,"url":24,"identifiers":1377},"10.2196\u002F28118",{"doi":1376},{"id":24,"text":1379,"url":24,"identifiers":1380},"TysonAJohnsonCFunkCU.S. public now divided over whether to get COVID-19 vaccinePew Research Center20202021-01-31https:\u002F\u002Fwww.pewresearch.org\u002Fscience\u002F",{},{"id":24,"text":1382,"url":24,"identifiers":1383},"10.1016\u002FS1473-3099(20)30426-6",{"doi":1382},{"id":24,"text":1385,"url":24,"identifiers":1386},"10.7759\u002Fcureus.7255",{"doi":1385},{"id":24,"text":1388,"url":24,"identifiers":1389},"BrennenJSimonFHowardPNielsenRTypes, sources, and claims of COVID-19 misinformation key findingsReuters Institute, University of Oxford2021-01-30https:\u002F\u002Freutersinstitute.politics.ox.ac.uk\u002Ftypes-sources-and-claims-covid-19-misinformation",{},{"id":24,"text":1391,"url":24,"identifiers":1392},"10.1007\u002Fs40037-019-00542-7",{"doi":1391},{"id":24,"text":1394,"url":24,"identifiers":1395},"10.3390\u002Fijerph15091974",{"doi":1394},{"id":24,"text":1397,"url":24,"identifiers":1398},"10.5365\u002FWPSAR.2015.6.1.013",{"doi":1397},{"id":24,"text":1400,"url":24,"identifiers":1401},"10.1109\u002Ftnsm.2020.3031034",{"doi":1400},{"id":24,"text":1403,"url":24,"identifiers":1404},"10.2196\u002F18941",{"doi":1403},{"id":24,"text":1406,"url":24,"identifiers":1407},"10.2196\u002F18700",{"doi":1406},{"id":24,"text":1409,"url":24,"identifiers":1410},"10.2196\u002F18796",{"doi":1409},{"id":24,"text":1412,"url":24,"identifiers":1413},"10.1080\u002F21645515.2017.1360456",{"doi":1412},{"id":24,"text":1415,"url":24,"identifiers":1416},"10.1001\u002Fjamanetworkopen.2020.22025",{"doi":1415},{"id":24,"text":1418,"url":24,"identifiers":1419},"10.1371\u002Fjournal.pone.0246306",{"doi":1418},{"id":24,"text":1421,"url":24,"identifiers":1422},"LiuSFerraroJGundlapalliAVChapmanWBucherBDetection of healthcare-associated infections using electronic health record data201811AMIA 2018 Annual SymposiumNovember 3-7, 2018San Francisco, CA",{},{"id":24,"text":1424,"url":24,"identifiers":1425},"Shi, J, 2019, AMIA Annu Symp Proc, 2019, 794",{},{"id":24,"text":1427,"url":24,"identifiers":1428},"DevlinJChangMLeeKToutanovaKBERT: Pre-training of deep bidirectional transformers for language understanding201906NAACL-HLT 20192019Minneapolis, MN41714186",{},{"id":24,"text":1430,"url":24,"identifiers":1431},"Demsar, J, 2006, J Mach Learn Res, 7, 1",{},{"id":24,"text":1433,"url":24,"identifiers":1434},"10.1080\u002F01621459.1979.10482531",{"doi":1433},{"id":24,"text":1436,"url":24,"identifiers":1437},"HidayatullahAPembraniEKurniawanWAkbarGPranataRTwitter topic modeling on football news2018042018 3rd International Conference on Computer and Communication Systems2018Nagoya, Japan467471",{"doi":1438},"10.1109\u002FCCOMS.2018.8463231",{"id":24,"text":1440,"url":24,"identifiers":1441},"Blei, DM, 2012, Communications of the ACM, 55, 77, 10.1145\u002F2133806.2133826",{"doi":1442},"10.1145\u002F2133806.2133826",{"id":24,"text":1444,"url":24,"identifiers":1445},"10.3156\u002Fjsoft.24.4_160_1",{"doi":1444},{"id":24,"text":1447,"url":24,"identifiers":1448},"SievertCShirleyKLDAvis: A method for visualizing and interpreting topics20140627Workshop on Interactive Language Learning, Visualization, and Interfaces2014Baltimore, MD6370",{"doi":1449},"10.3115\u002Fv1\u002FW14-3110",{"id":24,"text":1451,"url":24,"identifiers":1452},"LevySWangWCross-lingual transfer learning for COVID-19 outbreak alignmentarXiv2021-02-11http:\u002F\u002Farxiv.org\u002Fabs\u002F2006.03202",{},{"id":24,"text":1454,"url":24,"identifiers":1455},"SpangherAPengNMayJFerraraEEnabling low-resource transfer learning across COVID-19 corpora by combining event-extraction and co-training2020ACL 2020 Work NLP-COVID 2020July 2020onlineAssociation for Computational Linguistics",{},{"id":24,"text":1457,"url":24,"identifiers":1458},"TasneemFNaimJTasniaRHossainTChyACSECU-DSG at WNUT-2020 Task 2: Exploiting ensemble of transfer learning and hand-crafted features for identification of informative COVID-19 English tweets202011192020 EMNLP Workshop W-NUT: The Sixth Workshop on Noisy User-generated TextNovember 19, 2020Online394398",{"doi":1459},"10.18653\u002Fv1\u002F2020.wnut-1.55",{"id":24,"text":1461,"url":24,"identifiers":1462},"10.2196\u002F22624",{"doi":1461},{"id":24,"text":1464,"url":24,"identifiers":1465},"YinHYangSLiJDetecting topic and sentiment dynamics due to COVID-19 pandemic using social mediaarXiv202007052021-02-11http:\u002F\u002Farxiv.org\u002Fabs\u002F2007.02304",{},{"id":24,"text":1467,"url":24,"identifiers":1468},"10.1016\u002Fj.asoc.2020.106754",{"doi":1467},{"id":24,"text":1470,"url":24,"identifiers":1471},"LiILiYLiTAlvarez-NapagaoSGarciaDWhat are we depressed about when we talk about COVID19: mental health analysis on tweets using natural language processingarXiv202006082021-02-11https:\u002F\u002Farxiv.org\u002Fabs\u002F2004.10899",{},{"id":24,"text":1473,"url":24,"identifiers":1474},"10.1186\u002Fs12911-021-01465-2",{"doi":1473},{"id":24,"text":1476,"url":24,"identifiers":1477},"EvanegaSLynasMAdamsJSmolenyakKCoronavirus misinformation: quantifying sources and themes in the COVID-19 \"infodemic\"Alliance for Science2021-02-11https:\u002F\u002Fallianceforscience.cornell.edu\u002Fwp-content\u002Fuploads\u002F2020\u002F10\u002FEvanega-et-al-Coronavirus-misinformation-submitted_07_23_20-1.pdf",{},{"id":24,"text":1479,"url":24,"identifiers":1480},"MüllerMSalathéMKummervoldPCOVID-Twitter-BERT: a natural language processing model to analyse COVID-19 content on TwitterarXiv202005152021-01-28https:\u002F\u002Farxiv.org\u002Fabs\u002F2005.07503",{},{"id":24,"text":1482,"url":24,"identifiers":1483},"10.1186\u002F1748-5908-6-42",{"doi":1482},{"id":1485,"createTime":1486,"updateTime":1486,"relativeEntities":1487,"slug":1488,"properties":1489,"entityType":112,"verifyStatus":113,"verifyTime":1505,"verifyNote":114,"syncStatus":23,"languages":1506,"translateLanguages":24,"viewCount":25,"primaryUrl":1507,"fullTextUrl":24,"authors":1508,"publicationType":315,"publisherRelationship":1579,"citationCount":1611,"citationInfo":1612,"publishDate":24,"publishYear":24,"citationAnalyzeStatus":23,"lastCitationAnalyze":24,"indexDatabases":24,"openAccess":24,"references":1618,"isForceReanalyzing":542},"3c577322-8601-42cd-ba9d-60dc06a22aba","2024-10-03T20:23:45.248+00:00",[],"COVID-19-Vaccine-Related-Discussion-on-Twitter-Topic-Modeling-and-Sentiment-Analysis",{"mag":1490,"keywords":1492,"pmc":1493,"openalex":1495,"abstract":1497,"title":1499,"pm":1501,"doi":1503},{"VOID":1491},"3171261489",{},{"VOID":1494},"8244724",{"VOID":1496},"W3171261489",{"EN":1498},"\u003Cjats:sec>\n            \u003Cjats:title>Background\u003C\u002Fjats:title>\n            \u003Cjats:p>Vaccination is a cornerstone of the prevention of communicable infectious diseases; however, vaccines have traditionally met with public fear and hesitancy, and COVID-19 vaccines are no exception. Social media use has been demonstrated to play a role in the low acceptance of vaccines.\u003C\u002Fjats:p>\n          \u003C\u002Fjats:sec>\n          \u003Cjats:sec>\n            \u003Cjats:title>Objective\u003C\u002Fjats:title>\n            \u003Cjats:p>The aim of this study is to identify the topics and sentiments in the public COVID-19 vaccine–related discussion on social media and discern the salient changes in topics and sentiments over time to better understand the public perceptions, concerns, and emotions that may influence the achievement of herd immunity goals.\u003C\u002Fjats:p>\n          \u003C\u002Fjats:sec>\n          \u003Cjats:sec>\n            \u003Cjats:title>Methods\u003C\u002Fjats:title>\n            \u003Cjats:p>Tweets were downloaded from a large-scale COVID-19 Twitter chatter data set from March 11, 2020, the day the World Health Organization declared COVID-19 a pandemic, to January 31, 2021. We used R software to clean the tweets and retain tweets that contained the keywords vaccination, vaccinations, vaccine, vaccines, immunization, vaccinate, and vaccinated. The final data set included in the analysis consisted of 1,499,421 unique tweets from 583,499 different users. We used R to perform latent Dirichlet allocation for topic modeling as well as sentiment and emotion analysis using the National Research Council of Canada Emotion Lexicon.\u003C\u002Fjats:p>\n          \u003C\u002Fjats:sec>\n          \u003Cjats:sec>\n            \u003Cjats:title>Results\u003C\u002Fjats:title>\n            \u003Cjats:p>Topic modeling of tweets related to COVID-19 vaccines yielded 16 topics, which were grouped into 5 overarching themes. Opinions about vaccination (227,840\u002F1,499,421 tweets, 15.2%) was the most tweeted topic and remained a highly discussed topic during the majority of the period of our examination. Vaccine progress around the world became the most discussed topic around August 11, 2020, when Russia approved the world’s first COVID-19 vaccine. With the advancement of vaccine administration, the topic of instruction on getting vaccines gradually became more salient and became the most discussed topic after the first week of January 2021. Weekly mean sentiment scores showed that despite fluctuations, the sentiment was increasingly positive in general. Emotion analysis further showed that trust was the most predominant emotion, followed by anticipation, fear, sadness, etc. The trust emotion reached its peak on November 9, 2020, when Pfizer announced that its vaccine is 90% effective.\u003C\u002Fjats:p>\n          \u003C\u002Fjats:sec>\n          \u003Cjats:sec>\n            \u003Cjats:title>Conclusions\u003C\u002Fjats:title>\n            \u003Cjats:p>Public COVID-19 vaccine–related discussion on Twitter was largely driven by major events about COVID-19 vaccines and mirrored the active news topics in mainstream media. The discussion also demonstrated a global perspective. 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[No one in hospital unnecessarily.], 1",{},{"id":24,"text":2348,"url":24,"identifiers":2349},"10.1016\u002Fj.vaccine.2009.05.042",{"doi":2348},{"id":24,"text":2351,"url":24,"identifiers":2352},"10.1017\u002FS0950268814003240",{"doi":2351},{"id":24,"text":2354,"url":24,"identifiers":2355},"10.1017\u002FS0950268817001005",{"doi":2354},{"id":24,"text":2357,"url":24,"identifiers":2358},"10.1186\u002F1742-5573-4-3",{"doi":2357},{"id":24,"text":2360,"url":24,"identifiers":2361},"10.1016\u002Fj.jbi.2006.09.003",{"doi":2360},{"id":24,"text":2363,"url":24,"identifiers":2364},"10.1371\u002Fjournal.pone.0094130",{"doi":2363},{"id":24,"text":2366,"url":24,"identifiers":2367},"10.1371\u002Fjournal.pone.0073832",{"doi":2366},{"id":24,"text":2369,"url":24,"identifiers":2370},"10.1111\u002Firv.12226",{"doi":2369},{"id":24,"text":2372,"url":24,"identifiers":2373},"10.1029\u002FRG027i003p00345",{"doi":2372},{"id":24,"text":2375,"url":24,"identifiers":2376},"Dixon, M, 1993, JTECH, 10, 785",{},{"id":24,"text":2378,"url":24,"identifiers":2379},"10.1017\u002FS1350482798000577",{"doi":2378},{"id":2381,"createTime":2382,"updateTime":2382,"relativeEntities":2383,"slug":2384,"properties":2385,"entityType":112,"verifyStatus":113,"verifyTime":2382,"verifyNote":114,"syncStatus":23,"languages":2401,"translateLanguages":24,"viewCount":25,"primaryUrl":2402,"fullTextUrl":24,"authors":2403,"publicationType":315,"publisherRelationship":2575,"citationCount":2607,"citationInfo":2608,"publishDate":24,"publishYear":24,"citationAnalyzeStatus":23,"lastCitationAnalyze":24,"indexDatabases":24,"openAccess":24,"references":2611,"isForceReanalyzing":542},"a40b7ede-66d1-4808-b754-2ae03c22b809","2024-09-30T19:27:25.061+00:00",[],"Using-Facebook-Advertising-to-Recruit-Representative-Samples-Feasibility-Assessment-of-a-Cross-Sectional-Survey",{"mag":2386,"keywords":2388,"pmc":2389,"openalex":2391,"abstract":2393,"title":2395,"pm":2397,"doi":2399},{"VOID":2387},"2955540625",{},{"VOID":2390},"6718121",{"VOID":2392},"W2955540625",{"EN":2394},"\u003Cjats:sec>\n            \u003Cjats:title>Background\u003C\u002Fjats:title>\n            \u003Cjats:p>Facebook has shown promise as an economical means of recruiting participants for health research. However, few studies have evaluated this recruitment method in Canada, fewer still targeting older adults, and, to our knowledge, none specifically in Newfoundland and Labrador (NL).\u003C\u002Fjats:p>\n          \u003C\u002Fjats:sec>\n          \u003Cjats:sec>\n            \u003Cjats:title>Objective\u003C\u002Fjats:title>\n            \u003Cjats:p>This study aimed to assess Facebook advertising as an economical means of recruiting a representative sample of adults aged 35 to 74 years in NL for a cross-sectional health survey.\u003C\u002Fjats:p>\n          \u003C\u002Fjats:sec>\n          \u003Cjats:sec>\n            \u003Cjats:title>Methods\u003C\u002Fjats:title>\n            \u003Cjats:p>Facebook advertising was used to recruit for a Web-based survey on cancer awareness and prevention during April and May 2018; during recruitment, additional advertisements were targeted to increase representation of demographics that we identified as being underrepresented in our sample. Sociodemographic and health characteristics of the study sample were compared with distributions of the underlying population to determine representativeness. Cramer V indicates the magnitude of the difference between the sample and population distributions, interpreted as small (Cramer V=0.10), medium (0.30), and large (0.50). Sample characteristics were considered representative if there was no statistically significant difference in distributions (chi-square P>.01) or if the difference was small (V≤0.10), and practically representative if 0.10<V≤0.20. The cost per recruit of Facebook advertising was compared with a quote for a random digit dialing (RDD)–recruited postal survey to determine if this method was economical.\u003C\u002Fjats:p>\n          \u003C\u002Fjats:sec>\n          \u003Cjats:sec>\n            \u003Cjats:title>Results\u003C\u002Fjats:title>\n            \u003Cjats:p>Facebook advertising is feasible and economical to conduct survey research, reaching 34,012 people, of which 2067 clicked on the ad, for a final sample size of 1048 people at Can $2.18 per recruit versus the quoted Can $23,316.05 for 400 recruits (Can $35.52 per recruit) via RDD. The sample was representative of rural and urban geography (P=.02; V=0.073), practically representative of age (P=.003; V=0.145) and income (P<.001; V=0.188), and over-representative of women (P<.001; V=0.507) and higher levels of education (P<.001; V=0.488). The sample was representative of the proportion of people with a regular health care provider (P=.94; V=0.025), diabetes prevalence (P=.002; V=0.096), and having had a colonoscopy or sigmoidoscopy (P=.27; V=0.034), and it was practically representative of smoking status (P<.001; V=0.14), and body mass index (P<.001; V=0.135). The sample was not representative of arthritis prevalence (P<.001; V=0.573), perceived health (P<.001; V=0.384), or time since last seasonal flu shot (P<.001; V=0.449).\u003C\u002Fjats:p>\n          \u003C\u002Fjats:sec>\n          \u003Cjats:sec>\n            \u003Cjats:title>Conclusions\u003C\u002Fjats:title>\n            \u003Cjats:p>Facebook advertising offers an easy, rapid, and economical means to recruit a partially representative (representative or practically representative of 8 of the 13 characteristics studied) sample of middle-aged and older adults for health survey research. As Facebook uses a nonrandom targeting algorithm, caution is warranted in its applications for certain types of research.\u003C\u002Fjats:p>\n          \u003C\u002Fjats:sec>",{"EN":2396},"Using Facebook Advertising to Recruit Representative Samples: Feasibility Assessment of a Cross-Sectional 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