Dynamic Hashtag Generation via Corpus-Based Collation Model
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Solution Overview
Problem
Conventional hashtags are static and subjective, losing relevance over time and failing to capture dynamic trends in social media content.
Innovation Solution
A corpus-based approach is used to generate dynamic and non-subjective hashtags by analyzing user and cohort social media outputs, creating a hashtag collation model that infers optimal hashtags based on term usage and adjusts them over time to maintain relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional static hashtags are used, then the hashtag generation process is simple, but the hashtags lose relevance over time and fail to capture dynamic trends
Solution Approach 1:
The patent transforms static hashtags into dynamic, adaptive hashtags that automatically adjust to changing social media trends. The system continuously monitors cohort user behavior and updates hashtags in real-time, making them responsive to emerging topics and sentiments rather than remaining fixed once generated
Solution Approach 2:
The system implements a feedback loop where hashtag performance and usage are continuously monitored, and this information feeds back into the hashtag generation process. The model learns from cohort user interactions and adjusts future hashtag recommendations based on what resonates with the target audience
2Measurement precision
If conventional subjective hashtags are used, then the author's personal perspective is preserved, but the hashtags fail to reflect objective social media trends
Solution Approach 1:
The system segments the hashtag generation process into distinct components: objective trend analysis from cohort data and subjective author intent. By separating these functions, the system can incorporate objective social media trends while still allowing authors to express their personal perspective through content selection
Solution Approach 2:
The patent introduces an intermediary system that acts as a bridge between objective cohort data and subjective author expression. This intermediary analyzes trends objectively and provides informed hashtag recommendations that authors can then apply to their content, combining both objective precision and subjective intent
3Reliability
If real-time hashtag adjustment is implemented, then hashtag relevance is maintained, but the computational resources required increase
Solution Approach 1:
The system applies partial action by monitoring and adjusting only the most critical aspects of hashtag performance rather than continuously re-analyzing all data. It focuses computational resources on key trend indicators and high-impact hashtags, reducing overall energy consumption while maintaining reliability
Data Source
AI summary
A system and method for generating dynamic, non-subjective hashtags using a corpus-based approach includes capturing social media outputs from a user and from cohorts of the user to create a user corpora and a user cohort corpora, respectively, storing the user corpora and the user cohort corpora in a computer readable storage device coupled to the computing system, deriving a hashtag collation model by analyzing the user corpora and the user cohort corpora, the hashtag collation model being stored on the computer readable storage device, generating a best matched hashtag using the hashtag collation model, wherein the hashtag collation model infers the optimal hashtag from the user corpora and user cohort corpora based on a usage of one or more terms within the user corpora and the user cohort corpora, and adjusting the best matched hashtag over time as the user corpora and the user cohort corpora change over time.