Abstract:
"Throughout history, humans have been referring to “News” as a significant information source for
decision-making in their day-to-day lives. With the advancement of technologies and internet
services, humans have moved away from traditional news outlets and started consuming online
news posted on social media. However, due to the less regulated environment in social media,
there is a high chance that people would be publishing fake news to mislead society for personal
gain. Over the years, with social media and online news consumption, going up in Sri Lanka, usage
of the Sinhala language on social media platforms has also improved with people using the
language to share information. Therefore, there is a need for a solution that could safeguard the
digital space of Sinhala users.
The project TrustTrace proposes a hybrid mechanism that incorporates content-based
classification and context-based classification to overcome the dissemination of Fake news in the
digital space. The project utilizes Machine learning ensemble methods and a credibility-based
scoring mechanism to tackle the problem.
A total of 5 Hybrid mechanisms were constructed by combining ensemble learners that predicted
with the highest accuracy with the credibility scoring mechanism. Out of the five algorithms,
XGBoost combined with sentence embeddings had the highest accuracy levels. The algorithm was
able to achieve an accuracy of 84% which goes past one of the similar approaches that has been
done for Sinhala FND."
