Automated Multi-Source Social Media Content Analysis Workflow

This n8n workflow is designed to facilitate comprehensive topic analysis and content research across multiple social media platforms and content sources. The process begins with a user-triggered form submission where the core keywords are entered. Subsequently, the workflow employs various third-party APIs such as Apify, ScrapingBee, and native platform APIs like Twitter, Reddit, and YouTube to fetch recent posts, tweets, videos, and articles relevant to the specified keyword.

The fetched data from each source is formatted uniformly, including content, engagement score, and source metadata. These inputs are then processed through filtering, deep AI-driven content analysis, sentiment evaluation, and relevance determination. Various nodes utilize AI models (via LangChain and Google PaLM) to analyze the content for potential topics, trending signals, and newsworthiness. The workflow also includes clustering and trend prediction based on engagement and sentiment metrics.

In the final stages, the aggregated and analyzed data is synthesized into a comprehensive report, which is formatted into HTML and automatically sent via email and integrated into a collaborative platform like Feishu for notifications. Additionally, the raw data and report are archived in Google Sheets. This workflow enables content teams, marketers, or social media managers to stay ahead by quickly identifying high-potential topics, trending discussions, and emerging media trends, saving significant manual effort and ensuring timely content responses.