Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/74842
Title: Automatically resolving forward-references in article headlines to identify clickbait
Authors: Warr, Rafael (2019)
Keywords: Social media
Internet advertising
Expert systems (Computer science)
Issue Date: 2019
Citation: Warr, R. (2019). Automatically resolving forward-references in article headlines to identify clickbait (Bachelor's dissertation).
Abstract: Clickbait is an ongoing problem within the world of social media. It floods our news feeds and is even used by newspaper websites as well. This causes annoyance as well as misleading information for the user. This dissertation constructs an expert system that implements forward referencing in an attempt to indicate clickbait. A list of rules are produced based on the knowledge base of forward referencing and a score for forward referencing is created. This score is then mapped to the clickbait domain in order to assess whether or not it can be a useful indicator. Unfortunately despite the fact that forward referencing is a widely used technique by journalists when writing article headlines, results were not up to standard as forward referencing is not sufficient alone. Should rules regarding linguistic features found in clickbait headlines be added however, the score could massively improve.
Description: B.SC.ICT(HONS)ARTIFICIAL INTELLIGENCE
URI: https://www.um.edu.mt/library/oar/handle/123456789/74842
Appears in Collections:Dissertations - FacICT - 2019
Dissertations - FacICTAI - 2019

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