AI vs linguistic-based human judgement: Bridging the gap in pursuit of truth for fake news detection
One of the negative aspects of the world becoming more digitized has been fake news, i.e., online disinformation – false, often fabricated reports of events, written and read on websites. The term has already entered collective consciousness and become an inseparable element of scientific discourse....
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| Published in | Information sciences Vol. 679; p. 121097 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
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Elsevier Inc
01.09.2024
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| Subjects | |
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| Abstract | One of the negative aspects of the world becoming more digitized has been fake news, i.e., online disinformation – false, often fabricated reports of events, written and read on websites. The term has already entered collective consciousness and become an inseparable element of scientific discourse. Once a piece of news goes online, stopping it from spreading may become a complicated matter. Literature suggests that the two main pillars of the effective fight against fake news are education and detection. Thus, this paper describes a multidisciplinary study performed by a group of scientists representing two distinct fields - AI and linguistics. In their joint study, they compared, formally evaluated and explored the intersection between two approaches to fake news detection, i.e., the automated one, using a machine-learning-based tool, and the linguistic-based human judgement, using the data from two disinformation campaigns, sourced from two open benchmark fake news datasets. The study focused on the news' headlines as an effective proxy for the identification of fake news. In accordance with the achieved results, the paper argues that in the fight against fake news, the two approaches have the potential of augmenting and enhancing each other, utilizing the state-of-the-art technologies and linguistic knowledge. In addition, this paper provides a list of the linguistic features characteristic of possible disinformation, which is the most comprehensive collection of this kind in the subject literature to date. |
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| AbstractList | One of the negative aspects of the world becoming more digitized has been fake news, i.e., online disinformation – false, often fabricated reports of events, written and read on websites. The term has already entered collective consciousness and become an inseparable element of scientific discourse. Once a piece of news goes online, stopping it from spreading may become a complicated matter. Literature suggests that the two main pillars of the effective fight against fake news are education and detection. Thus, this paper describes a multidisciplinary study performed by a group of scientists representing two distinct fields - AI and linguistics. In their joint study, they compared, formally evaluated and explored the intersection between two approaches to fake news detection, i.e., the automated one, using a machine-learning-based tool, and the linguistic-based human judgement, using the data from two disinformation campaigns, sourced from two open benchmark fake news datasets. The study focused on the news' headlines as an effective proxy for the identification of fake news. In accordance with the achieved results, the paper argues that in the fight against fake news, the two approaches have the potential of augmenting and enhancing each other, utilizing the state-of-the-art technologies and linguistic knowledge. In addition, this paper provides a list of the linguistic features characteristic of possible disinformation, which is the most comprehensive collection of this kind in the subject literature to date. |
| ArticleNumber | 121097 |
| Author | Pawlicka, Aleksandra Choraś, Michał Pawlicki, Marek Kozik, Rafał Andrychowicz-Trojanowska, Agnieszka |
| Author_xml | – sequence: 1 givenname: Aleksandra orcidid: 0000-0003-4380-014X surname: Pawlicka fullname: Pawlicka, Aleksandra organization: University of Warsaw, Warsaw, Poland – sequence: 2 givenname: Marek surname: Pawlicki fullname: Pawlicki, Marek organization: Bydgoszcz University of Science and Technology, Bydgoszcz, Poland – sequence: 3 givenname: Rafał orcidid: 0000-0001-7122-3306 surname: Kozik fullname: Kozik, Rafał organization: Bydgoszcz University of Science and Technology, Bydgoszcz, Poland – sequence: 4 givenname: Agnieszka orcidid: 0000-0001-6657-8823 surname: Andrychowicz-Trojanowska fullname: Andrychowicz-Trojanowska, Agnieszka organization: University of Warsaw, Warsaw, Poland – sequence: 5 givenname: Michał orcidid: 0000-0003-1405-9911 surname: Choraś fullname: Choraś, Michał email: mchoras@itti.com.pl, chorasm@pbs.edu.pl organization: Bydgoszcz University of Science and Technology, Bydgoszcz, Poland |
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| Cites_doi | 10.4269/ajtmh.20-0812 10.1088/1757-899X/1099/1/012040 10.1126/science.aap9559 10.5539/ijel.v11n1p99 10.1016/j.asoc.2020.107050 10.1016/j.cognition.2018.06.011 10.1016/j.inffus.2019.12.012 10.1109/MITP.2022.3163007 10.1016/S0378-2166(02)00134-0 10.17951/ms.2019.3.95-114 10.1093/jigpal/jzac009 10.1038/s41598-021-03100-6 10.1207/s15327957pspr1003_2 10.1108/JPBM-12-2018-2179 10.1088/1742-6596/2161/1/012027 10.1080/10447318.2022.2097601 10.33077/uw.24511617.ms.2019.4.187 10.1145/2896377.2901462 10.1016/j.neucom.2023.02.005 10.1177/2053951719843310 10.1016/S0378-2166(00)00013-8 10.1017/S1930297500008640 |
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| Keywords | AI Disinformation Artificial intelligence Fake news Machine learning ML |
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| Title | AI vs linguistic-based human judgement: Bridging the gap in pursuit of truth for fake news detection |
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