Contrasting Misinformation and Real-Information Dissemination Network Structures on Social Media During a Health Emergency
Objectives. To provide a comprehensive workflow to identify top influential health misinformation about Zika on Twitter in 2016, reconstruct information dissemination networks of retweeting, contrast mis- from real information on various metrics, and investigate how Zika misinformation proliferated on social media during the Zika epidemic.Methods. We systematically reviewed the top 5000 English-language Zika tweets, established an evidence-based definition of "misinformation," identified misinformation tweets, and matched a comparable group of real-information tweets. We developed an algorithm to reconstruct retweeting networks for 266 misinformation and 458 comparable real-information tweets. We computed and compared 9 network metrics characterizing network structure across various levels between the 2 groups.Results. There were statistically significant differences in all 9 network metrics between real and misinformation groups. Misinformation network structures were generally more sophisticated than those in the real-information group. There was substantial within-group variability, too.Conclusions. Dissemination networks of Zika misinformation differed substantially from real information on Twitter, indicating that misinformation utilized distinct dissemination mechanisms from real information. Our study will lead to a more holistic understanding of health misinformation challenges on social media.
Medienart: |
E-Artikel |
---|
Erscheinungsjahr: |
2020 |
---|---|
Erschienen: |
2020 |
Enthalten in: |
Zur Gesamtaufnahme - volume:110 |
---|---|
Enthalten in: |
American journal of public health - 110(2020), S3 vom: 14. Okt., Seite S340-S347 |
Sprache: |
Englisch |
---|
Beteiligte Personen: |
Safarnejad, Lida [VerfasserIn] |
---|
Links: |
---|
Themen: |
---|
Anmerkungen: |
Date Completed 12.11.2020 Date Revised 12.11.2020 published: Print Citation Status MEDLINE |
---|
doi: |
10.2105/AJPH.2020.305854 |
---|
funding: |
|
---|---|
Förderinstitution / Projekttitel: |
|
PPN (Katalog-ID): |
NLM315721677 |
---|
LEADER | 01000naa a22002652 4500 | ||
---|---|---|---|
001 | NLM315721677 | ||
003 | DE-627 | ||
005 | 20231225155529.0 | ||
007 | cr uuu---uuuuu | ||
008 | 231225s2020 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.2105/AJPH.2020.305854 |2 doi | |
028 | 5 | 2 | |a pubmed24n1052.xml |
035 | |a (DE-627)NLM315721677 | ||
035 | |a (NLM)33001726 | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
100 | 1 | |a Safarnejad, Lida |e verfasserin |4 aut | |
245 | 1 | 0 | |a Contrasting Misinformation and Real-Information Dissemination Network Structures on Social Media During a Health Emergency |
264 | 1 | |c 2020 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a ƒaComputermedien |b c |2 rdamedia | ||
338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
500 | |a Date Completed 12.11.2020 | ||
500 | |a Date Revised 12.11.2020 | ||
500 | |a published: Print | ||
500 | |a Citation Status MEDLINE | ||
520 | |a Objectives. To provide a comprehensive workflow to identify top influential health misinformation about Zika on Twitter in 2016, reconstruct information dissemination networks of retweeting, contrast mis- from real information on various metrics, and investigate how Zika misinformation proliferated on social media during the Zika epidemic.Methods. We systematically reviewed the top 5000 English-language Zika tweets, established an evidence-based definition of "misinformation," identified misinformation tweets, and matched a comparable group of real-information tweets. We developed an algorithm to reconstruct retweeting networks for 266 misinformation and 458 comparable real-information tweets. We computed and compared 9 network metrics characterizing network structure across various levels between the 2 groups.Results. There were statistically significant differences in all 9 network metrics between real and misinformation groups. Misinformation network structures were generally more sophisticated than those in the real-information group. There was substantial within-group variability, too.Conclusions. Dissemination networks of Zika misinformation differed substantially from real information on Twitter, indicating that misinformation utilized distinct dissemination mechanisms from real information. Our study will lead to a more holistic understanding of health misinformation challenges on social media | ||
650 | 4 | |a Journal Article | |
650 | 4 | |a Research Support, Non-U.S. Gov't | |
700 | 1 | |a Xu, Qian |e verfasserin |4 aut | |
700 | 1 | |a Ge, Yaorong |e verfasserin |4 aut | |
700 | 1 | |a Krishnan, Siddharth |e verfasserin |4 aut | |
700 | 1 | |a Bagarvathi, Arunkumar |e verfasserin |4 aut | |
700 | 1 | |a Chen, Shi |e verfasserin |4 aut | |
773 | 0 | 8 | |i Enthalten in |t American journal of public health |d 1971 |g 110(2020), S3 vom: 14. Okt., Seite S340-S347 |w (DE-627)NLM000012491 |x 1541-0048 |7 nnns |
773 | 1 | 8 | |g volume:110 |g year:2020 |g number:S3 |g day:14 |g month:10 |g pages:S340-S347 |
856 | 4 | 0 | |u http://dx.doi.org/10.2105/AJPH.2020.305854 |3 Volltext |
912 | |a GBV_USEFLAG_A | ||
912 | |a GBV_NLM | ||
951 | |a AR | ||
952 | |d 110 |j 2020 |e S3 |b 14 |c 10 |h S340-S347 |