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- W4285293513 abstract "Fake news spread around the internet has become a significant issue in recent years that attracts a lot of attention from both researchers and the general public. The major problem is that misinformation and propaganda which are proliferating across the digital landscape can greatly shape public opinion on many on-going topics, potentially having a significant political and social impact. Social networks are particularly well-suited for the distribution of made-up content by the usage of specialized software tools and artificial user accounts. Considering this, automatic fake news detection has become a primary goal of researchers in this field, which should help minimize the spread of misinformation by accurately flagging potentially problematic digital material. Yet, many challenges to effective fake news detection remain as there are not many indicators that can reliably distinguish fabricated content from the genuine one. This paper provides an overview of recent efforts in the field and summarizes contemporary approaches to devising classification models for fake news detection." @default.
- W4285293513 created "2022-07-14" @default.
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- W4285293513 date "2022-01-01" @default.
- W4285293513 modified "2023-10-14" @default.
- W4285293513 title "Fake News Detection on Social Networks – a Brief Overview of Methods and Approaches" @default.
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- W4285293513 doi "https://doi.org/10.1007/978-3-030-97947-8_67" @default.
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