Abstract:
The Sri Lankan media industry has undergone significant transformations in recent
years. These changes are often driven by technological advancements and shifting
audience preferences. As a result, upholding media ethics in news reporting has
become more challenging. Over the years, the majority of machine learning-based
research has been primarily devoted only to detecting hate speech, fake news, and
offensive statements in developing semantic analysis systems. To tackle this
pressing gap the study will aim to develop an automatic solution to uncover
unethical reporting practices by identifying offensive patterns in Sinhala news
headlines using natural language processing techniques. It will also focus on
developing a solution to prevent its detrimental effects from thwarting them.
Solution development involved several steps, and the data collection process was
done by gathering over 2500 Sinhala news headlines from main digital media
portals. Those data were annotated as "violated" and "non-violated" based on the
code of ethics introduced by Verité Research Institute. Data preprocessing steps
included tokenization, stop word removal, punctuation removal, spelling correction,
numeric values removal, non-Sinhala values removal, and encoding. Two
classification algorithms, Support Vector Machine, and Logistic Regression were
used to train different models considering five feature extraction techniques. They
were TF-IDF, N-Gram, Count Vectorizer, Word2Vec, and FastText. Based on the
performance evaluated on the testing data, the combination of Support Vector
Machine (SVM) with TF-IDF was chosen as the primary model due to its robust
performance and adaptability across the vast data set and complex ethical scenarios
in news headlines. This selected model outperformed other approaches achieving an
accuracy of 91% on the tested dataset. Media ethics encompass a broad and
complex field. This research focuses only on detecting violations in selected media
ethics practices, specifically marginalization, prejudiced reporting, reporting on
suicides, and cases of women abuse. Future research can expand on this work by
addressing other ethical concerns in headlines, adding an automatic suggestion
feature to correct violated headlines and extending the analysis to
broader news content.
