Towards automatic annotation and detection of fake news

Mohammad Majid Akhtar, Ishan Karunanayake, Bibhas Sharma, Rahat Masood, Muhammad Ikram, Salil S. Kanhere

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review


Automated accounts or bots on Online Social Networks (OSNs) play a significant role in disseminating information, including false news, which may instigate cyber propaganda. The existing research on fake news detection does not account for the existence of bots. Also, they only focus on identifying fake news in “the articles shared in posts” rather than the post’s (textual) content and use manually labeled limited datasets. In this research, we overcome the challenge of data scarcity by proposing an automated approach for labeling data using verified fact-checked statements on OSNs such as Twitter. Moreover, we analyze the presence and impact of bots and show that bots change their behavior over time. Our experiments focus on COVID-19, collect 10.22 million COVID-19-related tweets, and use our annotation model to build an extensive ground truth dataset for classification purposes. We evaluated our automatic annotation model on two existing COVID-19-related misinformation datasets and achieved a ∼2% increase in precision compared to the existing annotation models. In addition, our best classification model achieves 83% precision, 96% recall, and a ∼4% false positive rate on our annotated dataset, outperforming existing techniques.

Original languageEnglish
Title of host publicationProceedings of the 48th IEEE Conference on Local Computer Networks LCN 2023
EditorsEyuphan Bulut, Florian Tschorsch, Kanchana Thilakarathna
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages9
ISBN (Electronic)9798350300734
ISBN (Print)9798350300741
Publication statusPublished - 2023
EventIEEE Conference on Local Computer Networks (48th : 2023) - Daytona Beach, United States
Duration: 1 Oct 20235 Oct 2023
Conference number: 48th

Publication series

ISSN (Print)2831-7742
ISSN (Electronic)2832-1421


ConferenceIEEE Conference on Local Computer Networks (48th : 2023)
Abbreviated titleLCN
Country/TerritoryUnited States
CityDaytona Beach


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