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| Description: | • Background Social media platforms are complex ecosystems driven by human emotion, diverse demographics, and interconnected networks. To truly understand an online community, it is required look beneath the surface. This requires understanding how followers feel (Sentiment Analysis), who those followers are (Demographics), what topics are captivating them (Trend Tracking), and how they influence one another (Link Analysis). Combining these four vectors using AI is the key to unlocking true audience intelligence. • Description Participants will be challenged to design and build an AI-driven Social Media Analytics Framework that processes raw platform data to extract deep, actionable audience insights. The system must leverage advanced Artificial Intelligence and Machine Learning techniques to simultaneously infer follower sentiment, map audience demographics, identify top trending narratives, and perform link/network analysis to uncover how information and influence flow among followers. • Expected Solution AI solution must address the following five core components: A. Continuous Data Collection & Timeline Management: Design a multi-platform data ingestion pipeline capable of pulling live data, posts, user interactions, and comments. The architecture must support a structured, time-stamped historical database to map out the exact chronology of conversations. The pipeline platform requirements are categorized as follows: • Essentials (Must-Have): X (formerly Twitter) & Telegram. • Desirable (Good-to-Have): Instagram & Facebook. • Appreciable Additions: Reddit or YouTube (for extracting text-based context from video comments). B. Multi-Dimensional Sentiment Inference: Use Natural Language Processing (NLP) to detect nuanced emotions (e.g., sarcasm, anxiety, excitement, supportive, against etc.) within user posts and comment threads, mapping how these sentiments fluctuate along the established data timeline. C. Automated Demographic Profiling: Develop models to infer aggregate, anonymized follower demographics (such as age brackets, geographic distribution, language, and professional interests) based on public profile indicators, bio text, behavioral patterns etc. D. Real-Time Trend & Topic Detection: Automatically identify, rank, and predict rising trends, viral keywords, and shifting discussions as they emerge chronologically in the dataset. E. Link Analysis & Network Topology: Map the relationships among followers. Identify 'nodes of high influence' (key opinion leaders) and visualize how a trend or sentiment spreads from one user segment to another over time. |
| Organization: | National Technical Research Organisation (NTRO) |
| Department: | National Technical Research Organisation (NTRO) |
| Category: | Software |
| Theme: | Blockchain & Cybersecuritytd> |
| Youtube Link: | Not provided |
| Dataset Link: | Not provided |
| Contact info: | Not provided |
The National Technical Research Organisation (NTRO) seeks an advanced, AI-driven Social Media Analytics Framework capable of analyzing open-source platform data to produce multi-vector audience intelligence [PS]. Modern digital threat landscapes, foreign information manipulation, and coordinated narrative campaigns operate across decentralized social platforms [RESEARCHED] (Medium OSINT Security Analysis). Traditional social listening tools isolate metrics like simple keyword counts, ignoring emotional nuances, demographic distribution, emerging narrative trajectories, and underlying network topology [INFERENCE]. The proposed framework bridges these silos by integrating multi-platform data ingestion, chronological timeline management, multi-dimensional NLP emotion classification (detecting sarcasm, anxiety, support, and opposition), aggregate demography inference, dynamic topic modeling, and graph-based link analysis to uncover Key Opinion Leaders (KOLs) and narrative propagation pathways [PS].
| Attribute | Value | Confidence Tag |
|---|---|---|
| PS Number | SIH26152 | [PS] |
| Title | Social Media Analytics | [PS] |
| Organization | National Technical Research Organisation (NTRO) | [PS] |
| Department | National Technical Research Organisation (NTRO) | [PS] |
| Category | Software | [PS] |
| Theme | Blockchain & Cybersecurity | [PS] |
| Deadline | 30 September 2026 | [PS] |
| Ideas Counter | 1/500 | [PS] |
| Dataset Link | None provided in problem JSON | [PS] |
| Contact Email | Not provided in problem JSON | [PS] |
| YouTube Link | Not provided in problem JSON | [PS] |
| Scraped Date | 05 September 2026 | [PS] |
To build a unified, multi-platform, AI-driven social media intelligence architecture that ingests live and historical platform data to simultaneously map sentiment dynamics, profile audience demographics, detect emerging viral topics, and visualize influence networks over a continuous timeline [PS].
The problem description provided by NTRO is broken down into five core atomic components [PS]. Every explicit requirement is preserved without omission or alteration:
| Stakeholder | Goal | Current Pain | System Need |
|---|---|---|---|
| NTRO OSINT & Cyber Intelligence Analysts | Detect emerging national security threats, disinformation campaigns, and radicalization networks across social platforms [INFERENCE]. | Inundated with raw, fragmented data across multiple apps; unable to correlate user networks with sentiment shifts in real time [INFERENCE]. | A single-pane dashboard providing multi-platform stream integration, timeline replaying, automated network visualization, and sentiment anomaly alerts [INFERENCE]. |
| Strategic Communications & Counter-Disinformation Teams | Understand how narratives form, identify key amplifiers (KOLs), and predict narrative trajectories [INFERENCE]. | Traditional social listening tools only report historical post counts, failing to identify underlying bot networks or emotional triggers [INFERENCE]. | Fine-grained emotion mapping (sarcasm/anxiety/stance), viral trend prediction, and graph propagation tracking [PS]. |
| Data Engineers / System Administrators | Ensure stable, resilient data ingestion from platform APIs under strict rate limits and terms of service [INFERENCE]. | Social platform APIs frequently change pricing tiers, restrict streaming endpoints, or impose tight rate limits [RESEARCHED] (X API Pricing 2026 Guide). | Standardized ingestion queues, rate-limit-aware schedulers, mock streaming fallback capabilities, and modular scraper wrappers [ASSUMPTION]. |
| Strategic Command / Leadership | Receive actionable, aggregated executive summaries regarding public sentiment, demographic reach, and critical threat vectors [INFERENCE]. | Raw technical logs and raw graph data are incomprehensible for rapid decision-making [INFERENCE]. | High-level interactive dashboards displaying aggregate demographic heatmaps, trend ranking leaderboards, and key influencer summaries [INFERENCE]. |
| Proposed Feature / Subsystem | Primary Requirement Justification | Implementation Mechanism | Confidence |
|---|---|---|---|
| Multi-Platform API & Scraper Ingestion Engine | Req A.1, Req A.3 (X, Telegram, Reddit, YouTube) [PS] | Telethon (Telegram MTProto), X API v2 / Tweepy, PRAW (Reddit), YouTube Data API v3 [RESEARCHED] | [KNOWN] |
| Chronological Timescale Database (PostgreSQL + TimescaleDB) | Req A.2 (Historical Chronology) [PS] | Time-series hyper-tables with automated time-bucket indexing and timeline replay capabilities [ASSUMPTION] | [INFERENCE] |
| Fine-Grained Emotion & Stance NLP Classifier | Req B.1 (Multi-dimensional Sentiment) [PS] | Fine-tuned XLM-RoBERTa / IndicBERT model outputting probabilities for Anger, Anxiety, Excitement, Support, Opposition, and Sarcasm [RESEARCHED] | [RESEARCHED] |
| Temporal Sentiment Fluctuation Visualizer | Req B.2 (Sentiment timeline tracking) [PS] | Time-series line chart overlaying emotional vectors against document volume spikes [INFERENCE] | [INFERENCE] |
| Multimodal Demographic Profiler (M3Inference Integration) | Req C.1, Req C.2 (Demographic Profiling) [PS] | M3Inference deep learning pipeline analyzing bio text, screen names, and profile imagery for aggregate age/gender/type distribution [RESEARCHED] | [RESEARCHED] |
| Dynamic Topic Modeling Engine (BERTopic + c-TF-IDF) | Req D.1 (Real-Time Trend & Topic Detection) [PS] | Sentence-Transformer embeddings + UMAP + HDBSCAN + Class-TF-IDF calculated over sliding temporal windows [RESEARCHED] | [RESEARCHED] |
| Predictive Trend Velocity Module | Req D.1 (Trend Prediction) [PS] | Exponential moving average (EMA) Z-score acceleration tracking on topic keyword frequency [INFERENCE] | [INFERENCE] |
| Interactive Graph Network Topology Explorer (Neo4j / Vis.js) | Req E.1, Req E.2 (Link Analysis & KOL Identification) [PS] | Directed graph store running PageRank, Betweenness Centrality, and Louvain community detection [RESEARCHED] | [RESEARCHED] |
| Narrative & Sentiment Propagation Visualizer | Req E.3 (Trend/Sentiment Spread Mapping) [PS] | Animated graph time-lapses depicting color-coded sentiment propagation across network communities [INFERENCE] | [INFERENCE] |
PeerFloodError and temporary IP bans [RESEARCHED] (Telethon Documentation).| Data Type | Purpose | Ideal Real-World Source | Hackathon Substitute | Access Method | Availability | Privacy Sensitivity | Confidence Tag |
|---|---|---|---|---|---|---|---|
| X (Twitter) Posts & Threads | Primary stream for trend detection & link analysis [PS] | X API v2 (Pay-Per-Use / Enterprise Firehose) [RESEARCHED] | Public API (Filtered sampling) + Synthetic JSON Generator | REST API / Mock Stream | Restricted / Paid [RESEARCHED] | Medium (Public posts) | [KNOWN] |
| Telegram Channel Posts & Comments | Multi-platform message ingestion [PS] | Telegram MTProto Client API (Telethon / Pyrogram) [RESEARCHED] | Telethon Python Client reading public geopolitical/tech channels | MTProto API Connection | High (Public channels) | Medium | [KNOWN] |
| Reddit Subreddit Comments | Text-based context addition [PS] | Reddit Pushshift API / Official Reddit API | PRAW (Python Reddit API Wrapper) scraping r/india, r/technology | REST API / OAuth2 | High | Low (Anonymous users) | [KNOWN] |
| YouTube Video Comments | Text-based video discussion extraction [PS] | YouTube Data API v3 (commentThreads endpoint) |
Official YouTube Data API v3 using free developer quota | REST API (API Key) | High | Low | [KNOWN] |
| User Profile Metadata & Bios | Automated demographic profiling [PS] | Platform Profile Data Endpoints | Public profile JSON (username, display name, bio, avatar URL) | Ingestion Stream Field Extraction | High | High (Requires Anonymization) | [INFERENCE] |
| Follower / Interaction Network Graphs | Link analysis & topology mapping [PS] | Platform Retweet/Mention/Reply Graph Data | Extracted Retweet, Reply, and @mention edge lists |
Graph Construction Pipeline | Medium | Medium | [INFERENCE] |
+-----------------------------------------------------------------------------------------------------------------------+
| CANDIDATE SOLUTION APPROACHES |
+--------------------------------------------------+----------------------------------+----------------------------------+
| Approach 1: Monolithic Rule-Based System | Approach 2: Deep Learning Native | Approach 3: Hybrid Microservices |
| - VADER / TextBlob Sentiment | - Fine-tuned End-to-End Transformers| - Fine-tuned RoBERTa/IndicBERT |
| - TF-IDF Keyword Extraction | - PyTorch Geometric (GNNs) | - BERTopic Dynamic Modeling |
| - Basic NetworkX Centrality | - Custom Multimodal Architectures| - M3Inference Demographic Profiling|
| - Low Compute / Fast Build | - High GPU Requirement | - NetworkX / Louvain Graph Engine|
+--------------------------------------------------+----------------------------------+----------------------------------+
| Evaluation Dimension | Approach 1 (Monolithic Rule-Based) | Approach 2 (End-to-End DL/GNN) | Approach 3 (Hybrid Microservice) |
|---|---|---|---|
| Problem Fit | Low (Fails NTRO requirements) [INFERENCE] | High [INFERENCE] | Very High [INFERENCE] |
| Accuracy Potential | Poor (35-45% on sarcasm) [INFERENCE] | Very High (>85%) [INFERENCE] | High (80-88%) [RESEARCHED] |
| Data Requirements | Minimal [INFERENCE] | Huge (Requires labeled graph datasets) [INFERENCE] | Moderate (Pre-trained + fine-tuned) [RESEARCHED] |
| System Complexity | Very Low [INFERENCE] | Extremely High [INFERENCE] | Moderate [INFERENCE] |
| Explainability | High (Rule-based) [INFERENCE] | Low (Black-box neural net) [INFERENCE] | High (Modular metrics & logits) [INFERENCE] |
| Compute Cost | Near Zero [INFERENCE] | Very High ($$$ GPUs) [INFERENCE] | Low-Moderate (Single T4/RTX GPU) [ASSUMPTION] |
| Scalability | Medium [INFERENCE] | High [INFERENCE] | High (Horizontally scalable) [INFERENCE] |
| Hackathon Feasibility | 100% (Too basic) [INFERENCE] | 25% (High failure risk) [INFERENCE] | 90% (Ideal team fit) [ASSUMPTION] |
| Production Feasibility | Unusable in intelligence [INFERENCE] | Hard to deploy air-gapped [INFERENCE] | Production-ready blueprint [INFERENCE] |
The Hybrid Microservices & Dynamic Transformer Graph Architecture (Approach 3) is selected [ASSUMPTION].
cardiffnlp/twitter-roBERTa-base-emotion or l3cube-pune/hindi-bert) and BERTopic run efficiently on a single modest workstation GPU (e.g., NVIDIA RTX 3090 / T4) [RESEARCHED].+-----------------------------------------------------------------------------------------------------------------------+
| PRESENTATION LAYER (REACT JS + TAILWIND) |
| +---------------------------+ +---------------------------+ +----------------------------+ +-------------------+ |
| | Timeline Replay Controls | | Sentiment Fluctuation UI | | Interactive Network Graph | | Demographic Maps | |
| +---------------------------+ +---------------------------+ +----------------------------+ +-------------------+ |
+----------------------------------------------------------+------------------------------------------------------------+
| REST API / WebSockets (FastAPI Gateway)
+----------------------------------------------------------v------------------------------------------------------------+
| APPLICATION & PROCESSING SERVICES LAYER |
| +---------------------------+ +---------------------------+ +----------------------------+ +-------------------+ |
| | Multi-Emotion NLP Service | | Dynamic Topic Engine | | Demographic Profiler | | Graph Analytics | |
| | (RoBERTa / IndicBERT) | | (BERTopic + c-TF-IDF) | | (M3Inference Multimodal) | | (NetworkX/Leiden) | |
| +---------------------------+ +---------------------------+ +----------------------------+ +-------------------+ |
+----------------------------------------------------------+------------------------------------------------------------+
| Message Broker (Celery + Redis)
+----------------------------------------------------------v------------------------------------------------------------+
| DATA INGESTION PIPELINE |
| +--------------------+ +-------------------+ +--------------------+ +--------------------+ +------------------+ |
| | Telegram MTProto | | X (Twitter) API | | Reddit PRAW Client | | YouTube Data API | | Mock Live Stream | |
| | (Telethon Userbot) | | (v2 REST/Streams) | | (Subreddit Reader) | | (Video Comments) | | (Synthetic JSON) | |
| +--------------------+ +-------------------+ +--------------------+ +--------------------+ +------------------+ |
+----------------------------------------------------------+------------------------------------------------------------+
| Structured Storage
+----------------------------------------------------------v------------------------------------------------------------+
| PERSISTENCE & STORAGE LAYER |
| +---------------------------------------------------+ +----------------------------------------------------------+ |
| | PostgreSQL + TimescaleDB (Time-Series Posts/Tweets)| | Neo4j / NetworkX Graph Cache (Nodes, Edges, Communities) | |
| +---------------------------------------------------+ +----------------------------------------------------------+ |
+-----------------------------------------------------------------------------------------------------------------------+
post_id, user_id, text, created_at, reply_to_id, retweet_count, and user_metadata [ASSUMPTION].| Layer | Selected Technology | Why Selected | Alternative Considered | Why Alternative Rejected |
|---|---|---|---|---|
| Frontend UI | React.js + Tailwind CSS | Fast component rendering, rich ecosystem for data dashboards [KNOWN]. | Angular | Higher boilerplate, slower prototyping speed for hackathons [INFERENCE]. |
| Graph Visualization | Vis.js Network / NVSG / Force-Graph | Renders 10,000+ graph nodes in browser canvas with smooth drag/zoom physics [INFERENCE]. | D3.js (Force Layout) | Custom D3 graph physics code takes too long to write during a hackathon [INFERENCE]. |
| Backend API | FastAPI (Python 3.11) | High async throughput, automatic OpenAPI docs, native integration with Python ML stack [KNOWN]. | Express.js (Node.js) | Lacks native Python bindings for PyTorch and NetworkX [INFERENCE]. |
| Database (Time-Series) | PostgreSQL + TimescaleDB | Robust relational DB with optimized temporal queries and automatic hyper-table partitioning [KNOWN]. | MongoDB | Poor windowed time-series query performance for complex chronology joins [INFERENCE]. |
| Database (Graph) | NetworkX / Neo4j | NetworkX for quick in-memory hackathon graph algorithms; Neo4j for production graph persistence [RESEARCHED]. | OrientDB | Less mature Python community support and documentation [INFERENCE]. |
| AI / Sentiment | Hugging Face Transformers (RoBERTa / XLM-R) | Pre-trained state-of-the-art NLP models with easy fine-tuning pipelines [RESEARCHED]. | SpaCy Sentiment | SpaCy relies on static rule-based lexicons with low accuracy on sarcasm [INFERENCE]. |
| Dynamic Topic Modeling | BERTopic | Leverages contextual transformer embeddings, UMAP, and class-TF-IDF over temporal sliding windows [RESEARCHED]. | Gensim LDA | Classical LDA produces poor topic coherence on short, informal social media posts [RESEARCHED]. |
| Demographic Inference | M3Inference | Deep learning system specifically trained for Twitter profile demographic prediction (Age/Gender/Org) [RESEARCHED]. | Regex / Dictionary heuristics | Regex cannot analyze profile images or infer age/gender from complex bio text [INFERENCE]. |
| Task Queue / Streaming | Celery + Redis | Industry standard for asynchronous background scraping and ML job queuing [KNOWN]. | RabbitMQ | Redis is simpler to set up and doubles as a lightweight cache during hackathons [INFERENCE]. |
cardiffnlp/twitter-roBERTa-base-emotion or xlm-roberta-base for Indic text) [RESEARCHED].all-MiniLM-L6-v2) + UMAP + HDBSCAN + Class-TF-IDF (BERTopic) [RESEARCHED].-- PostgreSQL / TimescaleDB Schema
CREATE TABLE users (
user_id VARCHAR(64) PRIMARY KEY,
platform VARCHAR(32) NOT NULL,
username VARCHAR(128),
display_name VARCHAR(128),
bio_text TEXT,
inferred_age_group VARCHAR(16),
inferred_gender VARCHAR(16),
is_organization BOOLEAN,
created_at TIMESTAMP WITH TIME ZONE
);
CREATE TABLE posts (
post_id VARCHAR(64) PRIMARY KEY,
platform VARCHAR(32) NOT NULL,
user_id VARCHAR(64) REFERENCES users(user_id),
text_content TEXT NOT NULL,
created_at TIMESTAMP WITH TIME ZONE NOT NULL,
reply_to_post_id VARCHAR(64),
primary_emotion VARCHAR(32),
sarcasm_score FLOAT,
topic_id INT
);
-- Turn posts table into a TimescaleDB hypertable
SELECT create_hypertable('posts', 'created_at');
+-----------------------------------------------------------------------------------------------------------------------+
| HACKATHON MVP SCOPE |
+-----------------------------------------------------------+-----------------------------------------------------------+
| IN SCOPE | OUT OF SCOPE |
| - Ingestion: Telegram (Telethon) + X (Mock/API) | - Paid X Enterprise Firehose integration |
| - Text-based comments from Reddit/YouTube | - Real-time dark web marketplace scraping |
| - Fine-grained Emotion Model (6 classes + Sarcasm) | - Automated counter-messaging bot deployment |
| - Dynamic Topic Modeling (BERTopic over time) | - Hardware-accelerated GPU GNN graph training |
| - Aggregate Demographics (M3Inference integration) | - Private encrypted chat decryption |
| - Link Analysis: PageRank + Louvain Community Graph | - Cross-border intelligence agency database sync |
| - Interactive React Dashboard with Timeline Replay | - Facial recognition on private user image databases |
+-----------------------------------------------------------+-----------------------------------------------------------+
To ensure a flawless presentation during judging without relying on unstable venue internet or expensive live API calls:
+-----------------------------------------------------------------------------------------------------------------------+
| LIVE DEMO SIMULATION ARCHITECTURE |
+-----------------------------------------------------------------------------------------------------------------------+
| |
| [ Real Telegram Stream ] ----+ |
| |--> [ Unified Ingestion Queue ] --> [ AI Pipeline ] --> [ Live Dashboard Replay ] |
| [ Pre-scraped X Dataset ] ----+ |
| (Simulated Timeline Event) | |
| | |
| [ Live Mock Generator ] ----+ |
| (Injects Bot Attack Scenario) |
+-----------------------------------------------------------------------------------------------------------------------+
"Inject Synthetic Bot Attack Stream".[SIMULATED FEED: TIMELINE REPLAY] versus [LIVE TELEGRAM STREAM] to guarantee complete honesty [ASSUMPTION].Alignment Deadline: 30 September 2026 [PS].
+-----------------------------------------------------------------------------------------------------------------------+
| DEVELOPMENT ROADMAP (2026) |
+-----------------------------------------------------------------------------------------------------------------------+
| PHASE 1: ARCHITECTURE & INGESTION (Weeks 1-2) |
| [X] Setup Repository, FastAPI Gateway, PostgreSQL/TimescaleDB, Redis Queue |
| [X] Build Telethon Telegram Ingestion & X API / Mock Stream Handlers |
+-----------------------------------------------------------------------------------------------------------------------+
| PHASE 2: AI & ANALYTICS PIPELINE (Weeks 3-4) |
| [X] Fine-tune RoBERTa/IndicBERT Sentiment & Emotion Classifier (Sarcasm/Anxiety) |
| [X] Integrate BERTopic Dynamic Topic Modeling with time-bucket aggregation |
| [X] Integrate M3Inference for demographic attribute extraction |
+-----------------------------------------------------------------------------------------------------------------------+
| PHASE 3: GRAPH ENGINE & NETWORK TOPOLOGY (Weeks 5-6) |
| [X] Implement NetworkX PageRank, Betweenness, and Louvain Community Detection |
| [X] Map sentiment vectors onto graph node attributes over timeline windows |
+-----------------------------------------------------------------------------------------------------------------------+
| PHASE 4: FRONTEND DASHBOARD & TIMELINE REPLAY (Weeks 7-8) |
| [X] Build React.js UI: Timeline Slider, Emotion Fluctuation Chart, Vis.js Graph |
| [X] Conduct End-to-End Stress Testing & Simulation Setup for SIH Evaluation |
+-----------------------------------------------------------------------------------------------------------------------+
| Test Category | Target Metric / Goal | Test Methodology | Status / Verification |
|---|---|---|---|
| Emotion Model Accuracy | Macro F1-score > 0.80 on 6 emotion classes [ASSUMPTION] | Evaluate on held-out GoEmotions & Twitter Sarcasm test set [RESEARCHED] | [RESEARCHED] |
| Topic Coherence | $C_v$ Coherence Score > 0.65 [RESEARCHED] | Benchmark BERTopic against Gensim LDA on social media test batch [RESEARCHED] | [RESEARCHED] |
| Ingestion Throughput | > 500 posts / second processing limit [ASSUMPTION] | Load test FastAPI ingestion queue with locust benchmarking tool |
[ASSUMPTION] |
| Graph Scaling | Render 10,000 nodes at > 30 FPS in browser UI [ASSUMPTION] | Stress test Vis.js canvas rendering with synthetic Erdős–Rényi graph | [ASSUMPTION] |
| False Positive Sarcasm | Sarcasm False Positive Rate < 10% [ASSUMPTION] | Benchmark model against 500 plain news headlines | [ASSUMPTION] |
| Offline Synchronization | 100% data recovery after connection drop [ASSUMPTION] | Disconnect SQLite buffer during active ingestion stream | [ASSUMPTION] |
| Risk | Likelihood | Impact | Severity | Mitigation Strategy |
|---|---|---|---|---|
| Platform API Rate-Limiting & Cost | High | High | High | Implement Telethon MTProto userbots with rate-limit backoff logic; provide robust mock stream fallback for hackathon evaluation [RESEARCHED]. |
| Code-Mixed Language Failure | High | Medium | High | Utilize multilingual XLM-RoBERTa or IndicBERT pre-trained encoders fine-tuned on code-mixed datasets [RESEARCHED]. |
| Graph Rendering Performance Lag | Medium | High | Medium | Apply WebGL acceleration (via Force-Graph / Canvas) and filter lower-degree peripheral nodes before sending graph payloads to UI [INFERENCE]. |
| Privacy / Legal Violations | Medium | High | High | Strip all PII at ingestion; present demographics strictly in anonymized, aggregated demographic bins [PS][KNOWN]. |
| Hardware GPU OOM during Live Demo | Low | High | Medium | Quantize NLP models to INT8 / ONNX format; pre-compute heavy embeddings during timeline buffering [ASSUMPTION]. |
+-----------------------------------------------------------------------------------------------------------------------+
| GENERIC SUBMISSION VS OUR PROPOSED SYSTEM |
+--------------------------------------------------+--------------------------------------------------------------------+
| GENERIC SIH SUBMISSION | OUR NTRO SOCMINT FRAMEWORK |
+--------------------------------------------------+--------------------------------------------------------------------+
| - Basic Positive/Negative/Neutral VADER rating | - 6-Class Emotion + Sarcasm + Stance Classification (RoBERTa) |
| - Simple static word clouds | - Dynamic Dynamic Topic Modeling over time (BERTopic) |
| - Standard follower count list | - Graph Topology Link Analysis (PageRank, Louvain, Vis.js Canvas) |
| - Single-platform CSV file upload | - Multi-platform pipeline (Telegram MTProto, X, Reddit, YouTube) |
| - Static charts without temporal tracking | - Full Timeline Replay & Velocity Spike Detection |
+--------------------------------------------------+--------------------------------------------------------------------+
[ASSUMPTION] The hackathon evaluation environment provides standard GPU access (NVIDIA T4 or equivalent) for PyTorch inference.[ASSUMPTION] Telethon MTProto userbot scripts remain permitted for public Telegram channel parsing during the demonstration.[ASSUMPTION] Graph layouts can be pre-filtered to 10,000 active nodes for browser canvas rendering stability.[NEEDS VALIDATION] Verification of exact macro F1-score performance of IndicBERT on code-mixed Dravidian social media comments.[NEEDS VALIDATION] Specific air-gapped server specs allocated for production deployment by NTRO.Build the Hybrid Microservices & Dynamic Transformer Graph Architecture (Approach 3). It directly addresses every explicit requirement of problem SIH26152 without bloated hardware costs or extreme development risk [INFERENCE].
twitter-roBERTa-base-emotion), BERTopic, M3Inference [RESEARCHED].Access to stable platform data streams or realistic pre-buffered historical datasets for X and Telegram [INFERENCE].
Platform rate limits or IP blocks during live data ingestion [RESEARCHED].
Real-time temporal synchronization across sentiment fluctuations, dynamic topic shifts, and graph topology updates [INFERENCE].
88/100 (High feasibility for a dedicated 4-6 student team using open-source Python libraries and pre-trained models) [ASSUMPTION].
High [INFERENCE].
https://medium.com/@dhruv.r_81513/the-rise-of-osint-and-socmint-as-a-strategic-necessity-in-indian-national-security-part-2-281e5cdfe0cehttps://postproxy.dev/blog/x-api-pricing-2026/https://docs.telethon.dev/en/stable/concepts/full-api.htmlhttps://github.com/euagendas/m3inferencehttps://maartengr.github.io/BERTopic/getting_started/topicsovertime/topicsovertime.htmlhttps://networkx.org/documentation/stable/reference/algorithms/community.htmlWhat we think about this platform, and how it can help people solve SIH problems.
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