Assisted Living

Uddin, Md. Zia

doi:10.1007/978-3-031-11534-9_1

Cited by 1 publication

(1 citation statement)

References 35 publications

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“…Therefore, the rest of the case studies are concerned with machine learning and a deep learning-based efficient approach for fake news detection by means of various state-of-the-art approaches. Deep learning has contributed a lot recently in many fields such as pattern analysis and artificial intelligence [31][32][33], with important applications in fake news detection model development [34][35][36][37][38]. However, deep learning has two major disadvantages: the first disadvantage is the overfitting problem most of the time and the second one is that it takes a lot of time to model the underlying data.…”

Section: Machine Learning For Fake News Detection 41 Overviewmentioning

confidence: 99%

The PolitiFact-Oslo Corpus: A New Dataset for Fake News Analysis and Detection

Põldvere,

Uddin,

Thomas

2023

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This study presents a new dataset for fake news analysis and detection, namely, the PolitiFact-Oslo Corpus. The corpus contains samples of both fake and real news in English, collected from the fact-checking website PolitiFact.com. It grew out of a need for a more controlled and effective dataset for fake news analysis and detection model development based on recent events. Three features make it uniquely placed for this: (i) the texts have been individually labelled for veracity by experts, (ii) they are complete texts that strictly correspond to the claims in question, and (iii) they are accompanied by important metadata such as text type (e.g., social media, news and blog). In relation to this, we present a pipeline for collecting quality data from major fact-checking websites, a procedure which can be replicated in future corpus building efforts. An exploratory analysis based on sentiment and part-of-speech information reveals interesting differences between fake and real news as well as between text types, thus highlighting the importance of adding contextual information to fake news corpora. Since the main application of the PolitiFact-Oslo Corpus is in automatic fake news detection, we critically examine the applicability of the corpus and another PolitiFact dataset built based on less strict criteria for various deep learning-based efficient approaches, such as Bidirectional Long Short-Term Memory (Bi-LSTM), LSTM fine-tuned transformers such as Bidirectional Encoder Representations from Transformers (BERT) and RoBERTa, and XLNet.

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