Building a News Aggregator Web Application for Nigerian Political News Using RSS Feeds

Nigerian citizens seeking comprehensive political news coverage must visit multiple media websites individually, creating friction that discourages informed civic engagement. This study presents the development of a political news aggregator web application that consolidates content from major Nigerian news outlets into a single, searchable platform. The application was built using Python (Flask) and PostgreSQL, with an RSS feed parser automatically ingesting content from 20 Nigerian political news sources every 30 minutes. Content is categorised by topic (elections, governance, parties, corruption), geopolitical zone, and publication date using a keyword-based classification engine. A user interface built with Bootstrap allows browsing, searching, and bookmarking. The development process followed Agile methodology across four two-week sprints. Feed ingestion reliability across the 20 sources was tested over 30 days, showing an average uptime of 96.8 percent. Classification accuracy was evaluated against 500 manually labelled articles, yielding a macro-F1 score of 0.82. User testing with 40 participants over two weeks showed a mean session duration of 8.4 minutes, compared to 3.2 minutes reported for a single traditional news site. Users rated the aggregator 4.0 out of 5.0 for comprehensiveness. The study recommends adding sentiment tagging for political discourse analysis and integration with social media sharing features to expand the platform's utility for civic engagement.

Keywords: news aggregator, Nigerian political news, RSS feeds, Flask, content classification