Abstract:
In this rapidly evolving digital age, societies rely
heavily upon social media to share news publicly due to the
speed of dissemination. With billions of users, the sharing of a
news item only takes a few minutes to represent diverged views,
with malicious or misleading content, to go viral. In 2018, Sri
Lanka experienced anti-Muslim riots, and in 2019, racist
uprisings initiated fake news in social media mainly in Sinhala
language. Considering the massive number of Sinhala language
posts shared at present and the deficiency of research work in
Sinhala fake news detection, an automatic fake news detection
technique is proposed in this research that can help to identify
fake news published in the Sinhala language which circulates on
social media sites. Approaches to detect fake news depend
heavily upon features inherent to either the explicit or implicit
features of user account and text content-based features of the
post, or any hybrid set of above features. Based on the literature,
social media users mainly consider verifiability to identify fake
news content. Therefore, the hybrid methodology proposed in
this research workmainly focused on the checking and verifying
whether the news text content appears on the credible sources.
The authenticity features of the user account that used to obtain
the news content were evaluated in the rule-based points
allocation schema. An accuracy of 78%was gained in predicting
fake news with this Rule-Based implementation.
