Tone Analysis for Social Media Adverse Engagement Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for identifying adverse user engagement in social media domains are inefficient in detecting inflammatory, digressive, or off-topic messages, leading to inadequate identification of valid adverse interactions.
Innovation Solution
The system employs cognitive analysis, tone analysis, and natural language processing (NLP) to classify posting accounts, validate comments, and derive context and topics, generating personality profiles to determine trends and restrict access to identified agitators, thereby improving efficiency in memory and network resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to identify adverse user engagement, then the system is simple to implement, but the detection accuracy for inflammatory and off-topic messages is insufficient
Solution Approach 1:
The patent introduces an intermediary NLP processing layer between the user input and the detection system. This intermediary component analyzes textual data entries for tone, context, and semantic meaning before classification, enabling accurate detection of inflammatory and off-topic messages without requiring direct complex analysis of all user interactions.
Solution Approach 2:
The patent replaces traditional mechanical/rule-based detection methods with NLP-based automated analysis. Instead of using simple keyword filtering or manual review processes, the system employs neural networks and natural language processing algorithms to automatically detect adverse engagement patterns, improving accuracy while managing complexity through automation.
2Reliability
If all textual data entries are processed to detect adverse engagement, then detection completeness is improved, but network resource consumption increases
Solution Approach 1:
The patent performs preliminary tone analysis and context determination on textual data entries before full processing. By initially assessing the tone and semantic context of comments, the system can identify and filter out clearly adverse entries early in the process, reducing the need to process all entries through more resource-intensive analysis stages.
Solution Approach 2:
The patent applies different processing depths to different types of textual data entries based on their local characteristics. Entries that appear to be clearly adverse or irrelevant are processed with higher intensity, while benign entries receive minimal processing, optimizing resource allocation based on the specific qualities and risks of individual data points.
3Loss of information
If personality profiles are generated for all posting accounts, then user understanding is improved, but processing time increases
Solution Approach 1:
The patent generates comprehensive personality profiles only for posting accounts that exhibit adverse engagement patterns or require detailed analysis. Rather than creating profiles for all users, the system applies this resource-intensive process selectively to accounts that need it most, balancing information quality with processing time constraints.
Data Source
AI summary
Aspects of the present invention disclose a method for detecting textual inputs of a user of a social media application and derives personality characteristic insights of a user. The method includes one or more processors identifying a textual data entry to an interactive internet-based application. The method further includes determining a tone of the textual data entry. The method further includes identifying a posting account corresponding to the textual data entry. The method further includes generating a personality profile corresponding to the posting account based on the textual data entry associated with the posting account. The method further includes determining a context of the textual data entry based on semantic features of the textual data entry. The method further includes classifying the personality profile corresponding to the posting account. The method further includes performing a defined action that prevents engagement between the posting account and the interactive internet-based application.