[2211.14505] Predictive linguistic cues for fake news: a societal artificial intelligence problem
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Computer Science > Computation and Language

arXiv:2211.14505 (cs)
[Submitted on 26 Nov 2022]

Title:Predictive linguistic cues for fake news: a societal artificial intelligence problem

Authors:Sandhya Aneja, Nagender Aneja, Ponnurangam Kumaraguru
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Abstract:Media news are making a large part of public opinion and, therefore, must not be fake. News on web sites, blogs, and social media must be analyzed before being published. In this paper, we present linguistic characteristics of media news items to differentiate between fake news and real news using machine learning algorithms. Neural fake news generation, headlines created by machines, semantic incongruities in text and image captions generated by machine are other types of fake news problems. These problems use neural networks which mainly control distributional features rather than evidence. We propose applying correlation between features set and class, and correlation among the features to compute correlation attribute evaluation metric and covariance metric to compute variance of attributes over the news items. Features unique, negative, positive, and cardinal numbers with high values on the metrics are observed to provide a high area under the curve (AUC) and F1-score.
Subjects: Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2211.14505 [cs.CL]
  (or arXiv:2211.14505v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2211.14505
Journal reference: IAES International Journal of Artificial Intelligence (IJ-AI), Vol. 11, No. 4, December 2022, pp. 1252~1260
Related DOI: https://doi.org/10.11591/ijai.v11.i4.pp1252-1260

Submission history

From: Nagender Aneja [view email]
[v1] Sat, 26 Nov 2022 07:50:01 UTC (515 KB)
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