AI Web Crawler for Social Media Event Targeting
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Solution Overview
Problem
Businesses face challenges in efficiently leveraging technology to engage with customers through social media and web content, as they are hesitant to offload tasks to machines due to concerns about missing important information, leading to potential negative engagement or missed opportunities.
Innovation Solution
The implementation of an AI-powered system that uses web crawling, machine learning, and natural language processing to extract and classify relevant entity event information, allowing for automated targeting of segments based on recent events, enabling optimized engagement actions without requiring users to manually identify recipient lists.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated systems are used to monitor and respond to social media content, then productivity and response speed are improved, but reliability deteriorates due to concerns about missing important information
Solution Approach 1:
The patent introduces an AI-powered system as an intermediary between social media content and human users. The system crawls web content, extracts entity event information, classifies events, and generates segments for automated actions, serving as a mediator that processes information before presenting it to users for verification and action.
Solution Approach 2:
The system implements feedback mechanisms where users can verify AI-generated segments and event classifications. The human-in-the-loop verification process allows users to provide feedback on the accuracy of extracted information, which can be used to improve the system's reliability over time while maintaining high productivity.
2Reliability
If manual monitoring of social media content is performed, then reliability is maintained through human judgment, but productivity deteriorates due to the time-consuming nature of manual review
Solution Approach 1:
The patent segments the information processing task into distinct components: web crawling, entity recognition, event extraction, classification, and segment generation. This segmentation allows the AI system to handle routine processing automatically while human users focus on verifying critical segments, improving overall productivity without sacrificing reliability.
Solution Approach 2:
The system performs preliminary actions by automatically crawling web content, extracting entities, classifying events, and generating candidate segments before presenting them to users. This preliminary processing filters out routine tasks, allowing users to focus only on verification and critical decision-making, thereby improving productivity while maintaining reliability.
3Measurement precision
If comprehensive web crawling is performed to extract all relevant information, then measurement precision is improved, but device complexity increases due to the need for multiple processing components
Solution Approach 1:
The patent creates a universal AI-powered system that performs multiple functions: web crawling, entity recognition, event extraction, classification, and segment generation. This multi-functional system consolidates what would otherwise require separate tools and processes, reducing overall system complexity while maintaining comprehensive information extraction and high measurement precision.
Data Source
AI summary
Embodiments described herein include receiving an application flow defining logic for triggering automated actions for a candidate post on one or more web-based communication channels. An artificial intelligence (AI) powered web crawler may crawl a set of websites for target content. Responsive to identifying target content, machine learning may be leveraged to identify an entity name for an entity that is a subject of the target content and associate the entity with one or more event categories. Based on the event categories, a segment may be generated at runtime for the application flow where segment membership was not known at the time the application flow was defined or before application runtime.