DETECTING ONLINE FAKE NEWS USING MACHINE LEARNING AND TOPOLOGICAL DATA ANALYSIS
Agbasonu Valerian Chinedum, Amanze Bethran Chibuike, Agbakwuru Alphonsus Onyekachi · International Journal of Engineering Applied Sciences and Technology · 2023
The internet is rife with different types of disinformation openly available to the public. The spreading of accidental or malicious misinformation on social media, specifically in critical situations, such as real-world emergencies, can have negative consequences on our health, democracy and economy. This facilitates the spread of rumors on social media where users share and exchange the latest information with many readers, including a large volume of new information every second. However, updated news sharing on social media is not always true. Consequently, disinformation is rapidly being recognized as a global risk alongside terrorism, cancer and global warming. The increasing demand for fact checking at scale has stimulated a rapid development of automated solutions using technologies such as Natural Language Processing (NLP) and Machine Learning (ML) in order to reduce the required human effort. This paper will explore novel methods for automated fake news detection through the integration of two powerful approaches to data science, ML and Topological Data Analysis (TDA). The main strength of ML lies in its predictive power. Deep learning in particular has yielded some impressive practical successes in various text processing tasks. However, ML can fail in more exploratory talks aimed at understanding the nature of the data and uncovering its insights. TDA is a fairly new field that applies topology and geometry to analyze high-dimensional data and construct its compressed representation offering a more exploratory approach. This paper will explore how the strengths of those two fields can be integrated in order to create novel method for the analysis of text data, which will potentially lead to the creation of a state-of-the-art fake news detection model.