Social Media Data Analysis Platform with Lexicon Clustering
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Solution Overview
Problem
Current methods for analyzing text-based social media data are inefficient due to lack of categorization, presence of spam, and outdated indexing, leading to overwhelming and misleading search results, with traditional search engines failing to effectively filter out irrelevant information.
Innovation Solution
A multi-user, multi-project data platform with modules for data capture, preparation, reporting, and classification, including a fetcher, parser, lexicon management, and data manager, which uses correlation calculations and force-directed graphs to visually identify and filter out spam and irrelevant entries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional search engines crawl and index vast amounts of social media information, then the quantity of available data increases, but the information becomes outdated and irrelevant
Solution Approach 1:
The system performs preliminary actions by continuously monitoring and capturing social media data in real-time before it becomes outdated. The crawler actively seeks new entries and updates the index proactively rather than waiting for periodic crawls, ensuring the information remains current and relevant.
Solution Approach 2:
The indexing system transitions from static periodic crawling to dynamic continuous monitoring. The system adapts its crawling behavior based on data freshness requirements, adjusting the frequency and intensity of data collection to maintain up-to-date information without unnecessary redundancy.
2Quantity of substance
If fire hosing technique is used to get all information from sources like Google or Twitter, then comprehensive data collection is achieved, but spam and porn are included making the data misleading
Solution Approach 1:
The system extracts and removes harmful content such as spam and porn from the collected social media data. Through automated filtering mechanisms and manual review processes, undesirable entries are identified and extracted from the dataset, leaving only relevant and legitimate information for analysis.
Solution Approach 2:
An intermediary filtering layer is introduced between data collection and data delivery. This intermediary system applies multiple filters including spam detection algorithms, content moderation rules, and relevance scoring to separate valuable information from harmful content before presenting results to users.
3Adaptability or versatility
If raw searching is performed through blog search engines, then all available blogs are returned, but the results are overwhelming and too time-consuming to sort through manually
Solution Approach 1:
The search results are segmented and organized into meaningful categories and clusters based on content similarity, user preferences, and relevance criteria. Instead of presenting a flat list of all blogs, the system divides results into manageable groups that are easier to navigate and analyze.
Solution Approach 2:
The system changes parameters by applying multiple filtering criteria simultaneously, including relevance scoring, recency, source reliability, and user-defined preferences. These parameter transformations convert the overwhelming raw result set into a refined, prioritized list that requires minimal manual sorting.
4Measurement precision
If well designed search filters are applied, then relevant information is narrowed down, but far too much information is still yielded requiring manual sorting
Solution Approach 1:
The system performs self-service by automatically sorting and ranking search results based on multiple criteria without requiring manual intervention. The automated sorting mechanism evaluates each result's relevance, recency, and quality, then orders them accordingly, eliminating the need for users to manually sort through filtered results.
Data Source
AI summary
A system for analyzing text-based information is presented. Each datum of information includes an author, a description and a timestamp. A fetcher fetches the raw information according to keywords. A parser parses the raw information to refine the results. A lexicon management module extracts lemmas from the raw information, and creates an edited lexicon containing the raw data and the lemmas for each datum. A data manager correlates lemmas in the edited lexicon and identifies clusters of lemmas that are correlated between each other. The results can be visually displayed to a user, and clusters of lemma that are less correlated than the other clusters can be visually identified. In one aspect, the user is able to excise the less correlated clusters, in order to further refine the results of the keyword search.