Anomaly Detection for Scheduled Social Media Posts
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Solution Overview
Problem
There is a need for automated techniques to detect anomalies in scheduled social media posts due to external events that may occur between post creation and publication, as existing methods rely on manual tracking, which can lead to missed opportunities or undesirable responses.
Innovation Solution
The system automatically extracts keywords from scheduled posts using ontological classification, compares them with data from social media and web search engines at predetermined intervals, and alerts authors to anomalies by classifying deviations exceeding a threshold, recommending actions such as rescheduling or modifying the post.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual tracking of external events is used, then device complexity is reduced, but reliability deteriorates due to missed opportunities or undesirable responses
Solution Approach 1:
The system enables self-service by automatically monitoring external events and scheduled posts without requiring manual intervention. The anomaly detection system continuously scans external data sources, compares them with scheduled post keywords, and autonomously identifies potential anomalies, freeing users from manual tracking while maintaining high detection reliability.
Solution Approach 2:
The patent replaces manual mechanical tracking with automated electronic systems. Instead of humans manually monitoring external events, the system uses automated keyword extraction, data scanning, and computational anomaly detection algorithms to monitor and identify relevant external events, significantly improving reliability while managing complexity through automation.
2Reliability
If automated anomaly detection is implemented, then reliability improves, but device complexity increases
Solution Approach 1:
The system segments the anomaly detection process into distinct functional modules: keyword extraction module, external data scanning module, anomaly detection module, and alert generation module. This segmentation allows each component to perform its specific function independently, making the overall complex system more manageable and maintainable while ensuring reliable anomaly detection through specialized processing at each stage.
Solution Approach 2:
The system performs preliminary actions by extracting keywords and topics from scheduled posts in advance, and by continuously pre-scanning external data sources before actual anomaly detection is needed. This preparation work reduces the complexity of real-time detection by having pre-processed data ready for quick comparison and analysis when external events occur.
3Loss of time
If manual review of scheduled posts is performed, then ease of operation is maintained, but loss of time increases due to delayed detection
Solution Approach 1:
The system implements feedback by automatically comparing external events with scheduled post content and providing real-time alerts to users when anomalies are detected. This continuous feedback loop ensures timely detection of external events that may affect scheduled posts, dramatically reducing detection time while requiring minimal user action beyond receiving and responding to alerts.
Solution Approach 2:
The anomaly detection system operates continuously, constantly monitoring external data sources and comparing them with scheduled post keywords without interruption. This continuous action ensures that no external events are missed, reducing detection time to near-real-time while maintaining ease of operation through automated monitoring that requires no manual intervention from users.
Data Source
AI summary
Techniques are disclosed for automatically detecting anomalies in the content of a scheduled social media post, alerting a user to the presence of such anomalies before the content is posted and recommending a course of action when an anomaly is detected. A set of keywords is extracted from a scheduled post using an ontological classification technique. At predetermined time intervals, the keywords are compared with information obtained from one or more data sources to determine if an anomaly is present. If an anomaly is detected, the scheduled post is classified into one of at least three categories: supporting the post, neutral, or opposing the post. Once the anomaly is detected and the scheduled post is classified, the author of the post is alerted to the anomaly along with the categorization. Subsequently, the author may reschedule the post to an earlier or later time, delete the post, or change the post.