Social Media Interest Analysis via Influencer Graphs
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Solution Overview
Problem
Extracting meaningful information from the vast volume of social media posts is challenging due to the lack of data about the individuals posting, making it difficult for market researchers to identify users' views on products and services.
Innovation Solution
A computer-implemented method analyzes information from social media platforms by determining topics of interest for each user based on their content creation and influence within their network, weighting content topics, and visualizing differences between user groups to identify unique interest topics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If social media posts are monitored to identify users' views on products and services, then market research capability is improved, but the complexity of extracting meaningful information increases due to lack of user data
Solution Approach 1:
The patent introduces an influencer graph as an intermediary structure that connects users based on influence relationships. This mediator enables indirect observation of user interests by analyzing the content posted by influencers and their connected users, thereby improving market research capability without requiring direct access to private user data, thus resolving the contradiction between research capability and extraction complexity
Solution Approach 2:
The patent replaces traditional mechanical text analysis methods with a network-based approach using influencer graphs and weighted topic models. Instead of directly analyzing individual user posts, the system substitutes this with analyzing the network structure and aggregated content from influencers, reducing the complexity of extracting meaningful information while maintaining or improving research insights
2Ease of manufacture
If traditional text analysis is used on social media posts, then implementation is simpler, but the volume and complexity of information makes obtaining useful knowledge daunting
Solution Approach 1:
The patent segments the social media analysis problem into distinct components: (1) building an influencer graph representing user relationships, (2) identifying content topics from posts, (3) weighting topics based on influencer relationships, and (4) aggregating results to identify user interests. This segmentation makes the overall complex task manageable while preserving useful knowledge that would be lost in holistic analysis
Solution Approach 2:
The patent adds a network dimension to traditional text analysis by incorporating the influencer graph structure. Instead of analyzing posts in isolation (one-dimensional text analysis), the system analyzes posts within the context of user relationships (adding network dimension), thereby extracting more useful knowledge without excessive complexity increase
Data Source
AI summary
Systems and methods are provided for analyzing people's interests, based on signals available within social media. In general, the systems and methods can include determining interests for a group of people that differentiate the group from another group. First, topics of interest for each individual can be calculated based on topics associated with their activity on social media websites and topics associated with people in their social media network. The interest topics of people in a first group can be compared to the interest topics of people in a second group to determine which interest topics have a high affinity for one group but not the other. The invention can further provide for visualization of the topic distribution between the two groups, which can include illustrations of the number of people in each group who are interested in a plurality of topics and/or the prevalence of each of the plurality of topics in the first group relative to the second group.