- Title
- Applying insights from machine learning towards guidelines for the detection of text-based fake news
- Creator
- Ngada, Okuhle
- Subject
- Machine learning
- Subject
- Fake News
- Date Issued
- 2021-12
- Date
- 2021-12
- Type
- Master's theses
- Type
- text
- Identifier
- http://hdl.handle.net/10948/60243
- Identifier
- vital:64141
- Description
- Web-based technologies have fostered an online environment where information can be disseminated in a fast and cost-effective manner whilst targeting large and diverse audiences. Unfortunately, the rise and evolution of web-based technologies have also created an environment where false information, commonly referred to as “fake news”, spreads rapidly. The effects of this spread can be catastrophic. Finding solutions to the problem of fake news is complicated for a myriad of reasons, such as: what is defined as fake news, the lack of quality datasets available to researchers, the topics covered in such data, and the fact that datasets exist in a variety of languages. The effects of false information dissemination can result in reputational damage, financial damage to affected brands, and ultimately, misinformed online news readers who can make misinformed decisions. The objective of the study is to propose a set of guidelines that can be used by other system developers to implement misinformation detection tools and systems. The guidelines are constructed using findings from the experimentation phase of the project and information uncovered in the literature review conducted as part of the study. A selection of machine and deep learning approaches are examined to test the applicability of cues that could separate fake online articles from real online news articles. Key performance metrics such as precision, recall, accuracy, F1-score, and ROC are used to measure the performance of the selected machine learning and deep learning models. To demonstrate the practicality of the guidelines and allow for reproducibility of the research, each guideline provides background information relating to the identified problem, a solution to the problem through pseudocode, code excerpts using the Python programming language, and points of consideration that may assist with the implementation.
- Description
- Thesis (MA) --Faculty of Engineering, the Built Environment, and Technology, 2021
- Format
- computer
- Format
- online resource
- Format
- application/pdf
- Format
- 1 online resource (vi, 158 pages)
- Format
- Publisher
- Nelson Mandela University
- Publisher
- Faculty of Engineering, the Built Environment, and Technology
- Language
- English
- Rights
- Nelson Mandela University
- Rights
- All Rights Reserved
- Rights
- Open Access
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| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE1 | Ngada, O.pdf | 3 MB | Adobe Acrobat PDF | View Details Download |