Social Media Narratives and Political Polarization During Senate Political Crises in the Philippines
Description
This study used a qualitative design to analyze secondary data using thematic and discourse analysis. The data were collected from public posts on social media, online news platforms, government statements, and academic literature related to the political crisis in the Philippine Senate from 2025 to 2026. A purposive sampling technique was used to select relevant materials. The data were coded based on recurring themes such as political polarization, emotional discourse, misinformation, and institutional trust. The analysis was conducted through systematic classification and interpretation of patterns in digital political communication.
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Steps to reproduce
This study utilized a systematic digital data collection workflow to examine social media narratives and political polarization during Senate political crises in the Philippines. A qualitative–quantitative content analysis approach was employed, where relevant social media posts were identified and collected from publicly accessible platforms such as Facebook, X (Twitter), and online news comment sections. A predefined set of keywords related to Senate political crises, political figures, and governance issues was used to filter and extract relevant posts within a specified time frame covering major political events. After data extraction, posts were screened based on inclusion criteria such as relevance, language (English and Filipino), and public accessibility, while excluding duplicate, irrelevant, or non-contextual content. The cleaned dataset was then organized and coded using a structured coding framework to identify dominant narratives, sentiment orientation (positive, negative, neutral), and indicators of political polarization. Data analysis was conducted using manual coding supported by software tools such as Microsoft Excel or qualitative data analysis applications (e.g., NVivo, if applicable). The workflow ensured systematic collection, validation, and thematic classification of social media data for reliable interpretation of public discourse patterns.
Institutions
- Biliran Province State UniversityEastern Visayas, Naval