Challenges in Automated Detection of COVID-19 Misinformation Article Swipe
Drahomíra Herrmannová
,
Gautam Thakur
,
Joshua N. Grant
,
Varisara Tansakul
,
Bryan T. Eaton
,
Olivera Kotevska
,
Jordan Burdette
,
M. J. Smyth
,
Mónica L. Smith
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.4697993
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.4697993
The COVID-19 pandemic has made the dangers of the spread of misinformation obvious but despite much global effort to curbing its spread, fake information about the pandemic keeps proliferating. In this paper, we address the development of automated methods for verification of claims about COVID-19 and discuss the challenges associated with this task. We focus on labeled data collection, limitations of existing models, and difficulties of applying misinformation detection models in practical applications. Our initial analysis indicates label imbalance may be a particular challenge for developing claim verification models and we discuss options for alleviating this issue.
Related Topics
Concepts
Misinformation
Coronavirus disease 2019 (COVID-19)
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
Computer science
2019-20 coronavirus outbreak
Pandemic
Virology
Computer security
Medicine
Outbreak
Infectious disease (medical specialty)
Pathology
Disease
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5281/zenodo.4697993
- OA Status
- green
- Cited By
- 1
- OpenAlex ID
- https://openalex.org/W3216821096
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3216821096Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5281/zenodo.4697993Digital Object Identifier
- Title
-
Challenges in Automated Detection of COVID-19 MisinformationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-05-07Full publication date if available
- Authors
-
Drahomíra Herrmannová, Gautam Thakur, Joshua N. Grant, Varisara Tansakul, Bryan T. Eaton, Olivera Kotevska, Jordan Burdette, M. J. Smyth, Mónica L. SmithList of authors in order
- Landing page
-
https://doi.org/10.5281/zenodo.4697993Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.5281/zenodo.4697993Direct OA link when available
- Concepts
-
Misinformation, Coronavirus disease 2019 (COVID-19), Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Computer science, 2019-20 coronavirus outbreak, Pandemic, Virology, Computer security, Medicine, Outbreak, Infectious disease (medical specialty), Pathology, DiseaseTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2021: 1Per-year citation counts (last 5 years)
Full payload
| id | https://openalex.org/W3216821096 |
|---|---|
| doi | https://doi.org/10.5281/zenodo.4697993 |
| ids.doi | https://doi.org/10.5281/zenodo.4697993 |
| ids.mag | 3216821096 |
| ids.openalex | https://openalex.org/W3216821096 |
| fwci | 0.39109569 |
| type | article |
| title | Challenges in Automated Detection of COVID-19 Misinformation |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T11147 |
| topics[0].field.id | https://openalex.org/fields/33 |
| topics[0].field.display_name | Social Sciences |
| topics[0].score | 0.9994999766349792 |
| topics[0].domain.id | https://openalex.org/domains/2 |
| topics[0].domain.display_name | Social Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/3312 |
| topics[0].subfield.display_name | Sociology and Political Science |
| topics[0].display_name | Misinformation and Its Impacts |
| topics[1].id | https://openalex.org/T10664 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9564999938011169 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Sentiment Analysis and Opinion Mining |
| topics[2].id | https://openalex.org/T11775 |
| topics[2].field.id | https://openalex.org/fields/27 |
| topics[2].field.display_name | Medicine |
| topics[2].score | 0.9279999732971191 |
| topics[2].domain.id | https://openalex.org/domains/4 |
| topics[2].domain.display_name | Health Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2741 |
| topics[2].subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| topics[2].display_name | COVID-19 diagnosis using AI |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C2776990098 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7280696630477905 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q13579947 |
| concepts[0].display_name | Misinformation |
| concepts[1].id | https://openalex.org/C3008058167 |
| concepts[1].level | 4 |
| concepts[1].score | 0.6947295665740967 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q84263196 |
| concepts[1].display_name | Coronavirus disease 2019 (COVID-19) |
| concepts[2].id | https://openalex.org/C3007834351 |
| concepts[2].level | 5 |
| concepts[2].score | 0.4926345944404602 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q82069695 |
| concepts[2].display_name | Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) |
| concepts[3].id | https://openalex.org/C41008148 |
| concepts[3].level | 0 |
| concepts[3].score | 0.485811710357666 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[3].display_name | Computer science |
| concepts[4].id | https://openalex.org/C3006700255 |
| concepts[4].level | 3 |
| concepts[4].score | 0.48020896315574646 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q81068910 |
| concepts[4].display_name | 2019-20 coronavirus outbreak |
| concepts[5].id | https://openalex.org/C89623803 |
| concepts[5].level | 5 |
| concepts[5].score | 0.43178704380989075 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q12184 |
| concepts[5].display_name | Pandemic |
| concepts[6].id | https://openalex.org/C159047783 |
| concepts[6].level | 1 |
| concepts[6].score | 0.2754763960838318 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q7215 |
| concepts[6].display_name | Virology |
| concepts[7].id | https://openalex.org/C38652104 |
| concepts[7].level | 1 |
| concepts[7].score | 0.24218863248825073 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q3510521 |
| concepts[7].display_name | Computer security |
| concepts[8].id | https://openalex.org/C71924100 |
| concepts[8].level | 0 |
| concepts[8].score | 0.21519124507904053 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[8].display_name | Medicine |
| concepts[9].id | https://openalex.org/C116675565 |
| concepts[9].level | 2 |
| concepts[9].score | 0.12535473704338074 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q3241045 |
| concepts[9].display_name | Outbreak |
| concepts[10].id | https://openalex.org/C524204448 |
| concepts[10].level | 3 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q788926 |
| concepts[10].display_name | Infectious disease (medical specialty) |
| concepts[11].id | https://openalex.org/C142724271 |
| concepts[11].level | 1 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q7208 |
| concepts[11].display_name | Pathology |
| concepts[12].id | https://openalex.org/C2779134260 |
| concepts[12].level | 2 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q12136 |
| concepts[12].display_name | Disease |
| keywords[0].id | https://openalex.org/keywords/misinformation |
| keywords[0].score | 0.7280696630477905 |
| keywords[0].display_name | Misinformation |
| keywords[1].id | https://openalex.org/keywords/coronavirus-disease-2019 |
| keywords[1].score | 0.6947295665740967 |
| keywords[1].display_name | Coronavirus disease 2019 (COVID-19) |
| keywords[2].id | https://openalex.org/keywords/severe-acute-respiratory-syndrome-coronavirus-2 |
| keywords[2].score | 0.4926345944404602 |
| keywords[2].display_name | Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) |
| keywords[3].id | https://openalex.org/keywords/computer-science |
| keywords[3].score | 0.485811710357666 |
| keywords[3].display_name | Computer science |
| keywords[4].id | https://openalex.org/keywords/2019-20-coronavirus-outbreak |
| keywords[4].score | 0.48020896315574646 |
| keywords[4].display_name | 2019-20 coronavirus outbreak |
| keywords[5].id | https://openalex.org/keywords/pandemic |
| keywords[5].score | 0.43178704380989075 |
| keywords[5].display_name | Pandemic |
| keywords[6].id | https://openalex.org/keywords/virology |
| keywords[6].score | 0.2754763960838318 |
| keywords[6].display_name | Virology |
| keywords[7].id | https://openalex.org/keywords/computer-security |
| keywords[7].score | 0.24218863248825073 |
| keywords[7].display_name | Computer security |
| keywords[8].id | https://openalex.org/keywords/medicine |
| keywords[8].score | 0.21519124507904053 |
| keywords[8].display_name | Medicine |
| keywords[9].id | https://openalex.org/keywords/outbreak |
| keywords[9].score | 0.12535473704338074 |
| keywords[9].display_name | Outbreak |
| language | en |
| locations[0].id | doi:10.5281/zenodo.4697993 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400562 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Zenodo (CERN European Organization for Nuclear Research) |
| locations[0].source.host_organization | https://openalex.org/I67311998 |
| locations[0].source.host_organization_name | European Organization for Nuclear Research |
| locations[0].source.host_organization_lineage | https://openalex.org/I67311998 |
| locations[0].license | |
| locations[0].pdf_url | |
| locations[0].version | |
| locations[0].raw_type | article-journal |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.5281/zenodo.4697993 |
| locations[1].id | mag:3216821096 |
| locations[1].is_oa | False |
| locations[1].source | |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://zenodo.org/record/4697993 |
| indexed_in | datacite |
| authorships[0].author.id | https://openalex.org/A5022174119 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-2730-1546 |
| authorships[0].author.display_name | Drahomíra Herrmannová |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Drahomira Herrmannova |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5109581864 |
| authorships[1].author.orcid | https://orcid.org/0009-0001-0941-9444 |
| authorships[1].author.display_name | Gautam Thakur |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Gautam Thakur |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5010931667 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Joshua N. Grant |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Joshua N. Grant |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5081065900 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-4285-3859 |
| authorships[3].author.display_name | Varisara Tansakul |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Varisara Tansakul |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5110204765 |
| authorships[4].author.orcid | |
| authorships[4].author.display_name | Bryan T. Eaton |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Bryan Eaton |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5021188335 |
| authorships[5].author.orcid | https://orcid.org/0000-0003-1677-2243 |
| authorships[5].author.display_name | Olivera Kotevska |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Olivera Kotevska |
| authorships[5].is_corresponding | False |
| authorships[6].author.id | https://openalex.org/A5082249975 |
| authorships[6].author.orcid | |
| authorships[6].author.display_name | Jordan Burdette |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Jordan Burdette |
| authorships[6].is_corresponding | False |
| authorships[7].author.id | https://openalex.org/A5103490494 |
| authorships[7].author.orcid | |
| authorships[7].author.display_name | M. J. Smyth |
| authorships[7].author_position | middle |
| authorships[7].raw_author_name | Martin Smyth |
| authorships[7].is_corresponding | False |
| authorships[8].author.id | https://openalex.org/A5068843508 |
| authorships[8].author.orcid | https://orcid.org/0000-0003-0612-6643 |
| authorships[8].author.display_name | Mónica L. Smith |
| authorships[8].author_position | last |
| authorships[8].raw_author_name | Monica Smith |
| authorships[8].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.5281/zenodo.4697993 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Challenges in Automated Detection of COVID-19 Misinformation |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T11147 |
| primary_topic.field.id | https://openalex.org/fields/33 |
| primary_topic.field.display_name | Social Sciences |
| primary_topic.score | 0.9994999766349792 |
| primary_topic.domain.id | https://openalex.org/domains/2 |
| primary_topic.domain.display_name | Social Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/3312 |
| primary_topic.subfield.display_name | Sociology and Political Science |
| primary_topic.display_name | Misinformation and Its Impacts |
| cited_by_count | 1 |
| counts_by_year[0].year | 2021 |
| counts_by_year[0].cited_by_count | 1 |
| locations_count | 2 |
| best_oa_location.id | doi:10.5281/zenodo.4697993 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400562 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Zenodo (CERN European Organization for Nuclear Research) |
| best_oa_location.source.host_organization | https://openalex.org/I67311998 |
| best_oa_location.source.host_organization_name | European Organization for Nuclear Research |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I67311998 |
| best_oa_location.license | |
| best_oa_location.pdf_url | |
| best_oa_location.version | |
| best_oa_location.raw_type | article-journal |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.5281/zenodo.4697993 |
| primary_location.id | doi:10.5281/zenodo.4697993 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400562 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Zenodo (CERN European Organization for Nuclear Research) |
| primary_location.source.host_organization | https://openalex.org/I67311998 |
| primary_location.source.host_organization_name | European Organization for Nuclear Research |
| primary_location.source.host_organization_lineage | https://openalex.org/I67311998 |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | |
| primary_location.raw_type | article-journal |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.5281/zenodo.4697993 |
| publication_date | 2021-05-07 |
| publication_year | 2021 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 81 |
| abstract_inverted_index.In | 29 |
| abstract_inverted_index.We | 53 |
| abstract_inverted_index.be | 80 |
| abstract_inverted_index.in | 70 |
| abstract_inverted_index.of | 7, 10, 36, 41, 60, 65 |
| abstract_inverted_index.on | 55 |
| abstract_inverted_index.to | 18 |
| abstract_inverted_index.we | 32, 90 |
| abstract_inverted_index.Our | 73 |
| abstract_inverted_index.The | 0 |
| abstract_inverted_index.and | 45, 63, 89 |
| abstract_inverted_index.but | 13 |
| abstract_inverted_index.for | 39, 84, 93 |
| abstract_inverted_index.has | 3 |
| abstract_inverted_index.its | 20 |
| abstract_inverted_index.may | 79 |
| abstract_inverted_index.the | 5, 8, 25, 34, 47 |
| abstract_inverted_index.data | 57 |
| abstract_inverted_index.fake | 22 |
| abstract_inverted_index.made | 4 |
| abstract_inverted_index.much | 15 |
| abstract_inverted_index.this | 30, 51, 95 |
| abstract_inverted_index.with | 50 |
| abstract_inverted_index.about | 24, 43 |
| abstract_inverted_index.claim | 86 |
| abstract_inverted_index.focus | 54 |
| abstract_inverted_index.keeps | 27 |
| abstract_inverted_index.label | 77 |
| abstract_inverted_index.task. | 52 |
| abstract_inverted_index.claims | 42 |
| abstract_inverted_index.effort | 17 |
| abstract_inverted_index.global | 16 |
| abstract_inverted_index.issue. | 96 |
| abstract_inverted_index.models | 69, 88 |
| abstract_inverted_index.paper, | 31 |
| abstract_inverted_index.spread | 9 |
| abstract_inverted_index.address | 33 |
| abstract_inverted_index.curbing | 19 |
| abstract_inverted_index.dangers | 6 |
| abstract_inverted_index.despite | 14 |
| abstract_inverted_index.discuss | 46, 91 |
| abstract_inverted_index.initial | 74 |
| abstract_inverted_index.labeled | 56 |
| abstract_inverted_index.methods | 38 |
| abstract_inverted_index.models, | 62 |
| abstract_inverted_index.obvious | 12 |
| abstract_inverted_index.options | 92 |
| abstract_inverted_index.spread, | 21 |
| abstract_inverted_index.COVID-19 | 1, 44 |
| abstract_inverted_index.analysis | 75 |
| abstract_inverted_index.applying | 66 |
| abstract_inverted_index.existing | 61 |
| abstract_inverted_index.pandemic | 2, 26 |
| abstract_inverted_index.automated | 37 |
| abstract_inverted_index.challenge | 83 |
| abstract_inverted_index.detection | 68 |
| abstract_inverted_index.imbalance | 78 |
| abstract_inverted_index.indicates | 76 |
| abstract_inverted_index.practical | 71 |
| abstract_inverted_index.associated | 49 |
| abstract_inverted_index.challenges | 48 |
| abstract_inverted_index.developing | 85 |
| abstract_inverted_index.particular | 82 |
| abstract_inverted_index.alleviating | 94 |
| abstract_inverted_index.collection, | 58 |
| abstract_inverted_index.development | 35 |
| abstract_inverted_index.information | 23 |
| abstract_inverted_index.limitations | 59 |
| abstract_inverted_index.difficulties | 64 |
| abstract_inverted_index.verification | 40, 87 |
| abstract_inverted_index.applications. | 72 |
| abstract_inverted_index.misinformation | 11, 67 |
| abstract_inverted_index.proliferating. | 28 |
| cited_by_percentile_year.max | 93 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 9 |
| citation_normalized_percentile.value | 0.73392641 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |