The linguistic landscape of “controversial”: sentiment and theme distribution insights
The language used to frame controversial topics on social media has profound implications for public discourse and opinion formation, warranting a close examination of their sentiment and thematic distribution. This study investigates the sentiment and themes associated with controversial topi...
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2024
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| Online Access: | http://journalarticle.ukm.my/23581/1/Gema%20Online_24_1_5.pdf http://journalarticle.ukm.my/23581/ https://ejournal.ukm.my/gema/issue/view/1648 |
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| Summary: | The language used to frame controversial topics on social media has profound implications for
public discourse and opinion formation, warranting a close examination of their sentiment and
thematic distribution. This study investigates the sentiment and themes associated with
controversial topics by analyzing Reddit posts containing the token “controversial” in their titles
on three news-related subreddits, aiming to bridge a gap in existing literature by focusing on
platform-specific sentiment analysis with an emphasis on content typology. A mixed-methods
NLP approach instrumented via Python was employed, combining VADER-supported sentiment
analysis and a qualitative content analysis using n-grams to identify and categorize themes. The
sentiment analysis results indicated that most of the content had neutral sentiment, which testifies
to the predominantly fact-based approach to presenting information with lack of strong emotional
connotations. However, the overall compound sentiment scores were negative, which suggests a
strong negative undertone in the framing of controversial topics. The theme distribution analysis
revealed that Politics and Legislation was the most predominant theme, followed by Technology
and Surveillance, Social Issues and Controversies, Health and Medicine, and Environment and
Energy. This distribution attests to a range of societal issues that generate controversy on social
media platforms. Study findings can be used by content creators and social media analysts to track
online content sentiment, guide content moderation practices, and improve audience engagement.
By demonstrating the potential of NLP techniques, this study also contributes to the fields of media
research and language technology, which can encourage a better scholarly evaluation of online
discourse. |
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