Identifying Automatically Generated Headlines using Transformers Article Swipe
Antonis Maronikolakis
,
Hinrich Schütze
,
Mark Stevenson
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.18653/v1/2021.nlp4if-1.1
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.18653/v1/2021.nlp4if-1.1
False information spread via the internet and social media influences public opinion and user activity, while generative models enable fake content to be generated faster and more cheaply than had previously been possible. In the not so distant future, identifying fake content generated by deep learning models will play a key role in protecting users from misinformation. To this end, a dataset containing human and computer-generated headlines was created and a user study indicated that humans were only able to identify the fake headlines in 47.8% of the cases. However, the most accurate automatic approach, transformers, achieved an overall accuracy of 85.7%, indicating that content generated from language models can be filtered out accurately.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.18653/v1/2021.nlp4if-1.1
- https://aclanthology.org/2021.nlp4if-1.1.pdf
- OA Status
- gold
- References
- 23
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3158796155
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3158796155Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.18653/v1/2021.nlp4if-1.1Digital Object Identifier
- Title
-
Identifying Automatically Generated Headlines using TransformersWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2021Year of publication
- Publication date
-
2021-01-01Full publication date if available
- Authors
-
Antonis Maronikolakis, Hinrich Schütze, Mark StevensonList of authors in order
- Landing page
-
https://doi.org/10.18653/v1/2021.nlp4if-1.1Publisher landing page
- PDF URL
-
https://aclanthology.org/2021.nlp4if-1.1.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://aclanthology.org/2021.nlp4if-1.1.pdfDirect OA link when available
- Concepts
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Misinformation, Computer science, Transformer, Social media, Generative grammar, Generative model, Key (lock), The Internet, Artificial intelligence, Machine learning, World Wide Web, Computer security, Engineering, Voltage, Electrical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
23Number of works referenced by this work
- Related works (count)
-
20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.faster | 24 |
| abstract_inverted_index.humans | 75 |
| abstract_inverted_index.models | 17, 46, 108 |
| abstract_inverted_index.public | 10 |
| abstract_inverted_index.social | 7 |
| abstract_inverted_index.spread | 2 |
| abstract_inverted_index.cheaply | 27 |
| abstract_inverted_index.content | 20, 41, 104 |
| abstract_inverted_index.created | 68 |
| abstract_inverted_index.dataset | 61 |
| abstract_inverted_index.distant | 37 |
| abstract_inverted_index.future, | 38 |
| abstract_inverted_index.opinion | 11 |
| abstract_inverted_index.overall | 98 |
| abstract_inverted_index.However, | 89 |
| abstract_inverted_index.accuracy | 99 |
| abstract_inverted_index.accurate | 92 |
| abstract_inverted_index.achieved | 96 |
| abstract_inverted_index.filtered | 111 |
| abstract_inverted_index.identify | 80 |
| abstract_inverted_index.internet | 5 |
| abstract_inverted_index.language | 107 |
| abstract_inverted_index.learning | 45 |
| abstract_inverted_index.activity, | 14 |
| abstract_inverted_index.approach, | 94 |
| abstract_inverted_index.automatic | 93 |
| abstract_inverted_index.generated | 23, 42, 105 |
| abstract_inverted_index.headlines | 66, 83 |
| abstract_inverted_index.indicated | 73 |
| abstract_inverted_index.possible. | 32 |
| abstract_inverted_index.containing | 62 |
| abstract_inverted_index.generative | 16 |
| abstract_inverted_index.indicating | 102 |
| abstract_inverted_index.influences | 9 |
| abstract_inverted_index.previously | 30 |
| abstract_inverted_index.protecting | 53 |
| abstract_inverted_index.accurately. | 113 |
| abstract_inverted_index.identifying | 39 |
| abstract_inverted_index.information | 1 |
| abstract_inverted_index.transformers, | 95 |
| abstract_inverted_index.misinformation. | 56 |
| abstract_inverted_index.computer-generated | 65 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.5699999928474426 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.0822767 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |