In this section
@inproceedings{Freedman:2024:v1/2024.argmining-1.2,
author = {Freedman, G and Toni, F},
doi = {v1/2024.argmining-1.2},
pages = {15--28},
publisher = {Association for Computational Linguistics},
title = {Detecting scientific fraud using argument mining},
url = {http://dx.doi.org/10.18653/v1/2024.argmining-1.2},
year = {2024}
}
TY - CPAPER
AB - proliferation of fraudulent scientific research in recent years has precipitated a greater interest in more effective methods of detection. There are many varieties of academic fraud, but a particularly challenging type to detect is the use of paper mills and the faking of peer-review. To the best of our knowledge, there have so far been no attempts to automate this process.The complexity of this issue precludes the use of heuristic methods, like pattern-matching techniques, which are employed for other types of fraud. Our proposed method in this paper uses techniques from the Computational Argumentation literature (i.e. argument mining and argument quality evaluation). Our central hypothesis stems from the assumption that articles that have not been subject to the proper level of scrutiny will contain poorly formed and reasoned arguments, relative to legitimately published papers. We use a variety of corpora to test this approach, including a collection of abstracts taken from retracted papers. We show significant improvement compared to a number of baselines, suggesting that this approach merits further investigation.
AU - Freedman,G
AU - Toni,F
DO - v1/2024.argmining-1.2
EP - 28
PB - Association for Computational Linguistics
PY - 2024///
SP - 15
TI - Detecting scientific fraud using argument mining
UR - http://dx.doi.org/10.18653/v1/2024.argmining-1.2
UR - https://aclanthology.org/2024.argmining-1.2
UR - http://hdl.handle.net/10044/1/114933
ER -
