Rasa Theory and Digital News: Mapping Bharata’s Natyashastra as Engagement Metric onto Click-Through Rates in Digital News Reels

Аннотация

Digital news platforms increasingly rely on AI-driven recommendation systems that
enhance audience engagement by prioritizing emotionally stimulating content. However, the
dominant affective models used in these systems, including sentiment analysis and valence-arousal,
are often Western and tend to simplify emotions into basic categories like positive, negative, or
neutral (Purohit et al., 2025). Such analyses may not accurately reflect the complexity of emotions
in India's media ecosystems, which are characterized by multilingualism, where audience responses
are influenced by their social and cultural context (Nagarajan & Guntuku, 2021). To address this
limitation, this paper proposes using Bharata's Natyashastra-based Rasa Theory to capture audience
emotions in short-form digital news. In the Indian Knowledge System, Rasa Theory identifies nine
emotional states—Shringara, Hasya, Karuna, Raudra, Veera, Bhayanaka, Bibhatsa, Adbhuta, and
Shanta—arising from the interactions between Bhava, Vibhava, Anubhava, and Sanchari Bhava
(Gopal, 2025). Unlike polarity-based models, Rasa adopts an audience-centered perspective, where
content, presentation, and contextual appropriateness (Auchitya) collectively contribute to the
construction of meaning (Singh et al., 2024). Using a mixed-method approach that includes manual
annotation of 500 Instagram Reels and YouTube Shorts, as detailed in the paper, and applied across
12 Indian news sites, the study examines the correlation between dominant Rasas and audience
engagement behaviors. The research found that content evoking anger and fear may attract
immediate attention, but emotion-based responses like compassion and wonder enhance audience
loyalty and engagement with the content.