Computer • Chapter 21

Social Networking

Social Networking
Evolution • Architecture • Data Flow • Algorithms • Feed Ranking • ML • Security • Popular Sites
Social Networking
  • Social Networking is described as a technology-driven system that enables users to create digital identities, establish social connections, generate content and interact through algorithm-controlled platforms using internet-based services.
English Summary
Social Networking is a technology-based system in which users create digital identities, establish social relationships, produce content and communicate through algorithm-driven platforms.

Identity

Digital profile

Connections

Social relationships

Content + Algorithms

Content creation and algorithm-controlled delivery.

Evolution of Social Networking
Evolution of social networking
Source-derived timeline using the exact phases and descriptions listed in the PDF.
PhasePlatformDescription in Source
1997SixDegreesAn early social networking site combining profiles and friend lists
2002FriendsterFriend-based networking
2004FacebookProfile + Feed system
2006TwitterMicroblogging
2009–2010WhatsApp (2009), Instagram (2010)Messaging and media sharing
Technical Architecture
Social networking architecture
Five core layers exactly as organized in the source.
LayerSource Examples / Role
User Interface Layer (UI)Profile, Feed, Buttons
Application LayerBusiness logic, API
Algorithm LayerRanking, Recommendation
Database LayerUser data, Posts
Network LayerInternet, CDN
Data Flow
Social networking data flow
Source-derived 7-step flow. The PDF calls the final cycle a Feedback Loop System.
  1. User uploads content.
  2. Content is stored in the database.
  3. Algorithm analyzes content.
  4. Ranking is applied.
  5. Feed is generated.
  6. User interaction is recorded.
  7. Feedback improves the algorithm.
Social Networking Algorithm

A Social Networking Algorithm is defined in the source as a set of rules and AI models that decides what content a user sees, when, and how often.

AlgorithmFunction in Source
Ranking AlgorithmDetermines order of posts
Recommendation AlgorithmFriend/video suggestions
Engagement AlgorithmMeasures likes and comments
Filtering AlgorithmRemoves spam
Advertisement AlgorithmTargeted Ads
Feed Ranking
Feed ranking factors
Main ranking factors listed in the source.
FactorExplanation
Affinity ScoreUser relationship strength
WeightContent type is one ranking signal; video is not universally ranked above images
Time DecayNew posts get priority
Engagement RateLikes, shares, comments
RelevanceUser interest matching
Source Summary
Ranking can consider relationships, recency, relevance and engagement. Signals differ by platform and feed; more interaction does not guarantee more reach.
Machine Learning in Social Networking
  • Machine Learning analyzes user behavior to personalize content.
  • Uses listed in the source: Personalized Feed, Fake Account Detection, Sentiment Analysis, Trend Prediction.
AI / ML ModelUse in Source
Collaborative FilteringRecommendation
NLPText understanding
Computer VisionImage/Video
Deep LearningVideo ranking
Graph AlgorithmsSocial network
Security & Privacy
Security and cyber crimes
Source-derived security/cyber-crime visual.
Education & Business Uses

Education

Social networking supports online learning and collaboration.

  • YouTube
  • Google Classroom
  • WhatsApp
  • Telegram

Business

  • Brand Promotion
  • Digital Marketing
  • Customer Support
  • Feedback Collection
  • Influencer Marketing
Popular Social Networking Sites — Developed Year & Founder
Social SiteDeveloped YearFounder(s) — Source
Facebook2004Mark Zuckerberg
Instagram2010Kevin Systrom, Mike Krieger
Twitter (X)2006Jack Dorsey, Biz Stone, Evan Williams, Noah Glass
LinkedInFounded 2002; launched 2003Reid Hoffman
WhatsApp2009Jan Koum, Brian Acton
YouTube2005Chad Hurley, Steve Chen, Jawed Karim
Snapchat2011Evan Spiegel, Bobby Murphy, Reggie Brown (co-founder acknowledged in settlement)
Telegram2013Pavel Durov and Nikolai Durov
Pinterest2010Ben Silbermann, Paul Sciarra, Evan Sharp
Reddit2005Steve Huffman, Alexis Ohanian
Quora2009Adam D’Angelo, Charlie Cheever
TikTokDouyin: 2016; international TikTok: 2017ByteDance; Zhang Yiming co-founded ByteDance
Revision NoteDistinguish founding from launch dates; selected founder lists and the Douyin/TikTok launch distinction have been corrected.
Quick Recall
One-Line Revision
  • Social Networking: digital identity + social connections + content + algorithm-controlled interaction.
  • Evolution: SixDegrees (1997) → Friendster (2002) → Facebook (2004) → Twitter (2006) → WhatsApp (2009) → Instagram (2010).
  • Architecture: UI → Application → Algorithm → Database → Network.
  • Data Flow: Upload → Store → Analyze → Rank → Feed → Record interaction → Feedback improves algorithm.
  • Feedback Loop System: user response helps improve the algorithm.
  • Algorithms: Ranking • Recommendation • Engagement • Filtering • Advertisement.
  • Feed Factors: Affinity • Weight • Time Decay • Engagement • Relevance.
  • ML Uses: Personalized Feed • Fake Account Detection • Sentiment Analysis • Trend Prediction.
  • AI Models: Collaborative Filtering • NLP • Computer Vision • Deep Learning • Graph Algorithms.
  • Security: Encryption • 2FA • Privacy Settings • Content Moderation • AI Monitoring.
  • Cyber Crimes: Phishing • Cyber Bullying • Identity Theft • Fake Profiles • Deepfake.
  • Education: YouTube • Google Classroom • WhatsApp • Telegram.
  • Business: Promotion • Marketing • Support • Feedback • Influencer Marketing.
Exam Traps
Architecture layer ≠ Algorithm type: “Algorithm Layer” is a system layer; Ranking/Recommendation/etc. are algorithm categories.
Affinity Score ≠ Engagement Rate: Affinity = relationship strength; Engagement = likes/shares/comments.
Source boundary: Page 219 starts “India’s Achievements in IT Field,” so it is not merged into this chapter.