Social Media Ethics: Privacy, Misuse, Laws & Digital Footprints

TAKEAWAYS:

• Social media platforms collect user data (location, browsing history, biometrics) under terms of service, but unauthorized sharing or leaks violate privacy laws like Nepal’s Electronic Transactions Act (ETA).
• Digital footprints—permanent online records—can harm reputations, jobs, or even lead to cyberstalking or blackmail (e.g., leaked WhatsApp chats in Nepal’s 2023 political scandals).
• Misinformation and deepfakes exploit algorithms; Nepal’s Cyber Crime Unit prosecutes fake news spreading hate or violence (e.g., 2022 #FakeNews arrests on Facebook).
• Cyberbullying (harassment via comments, DMs, or fake accounts) is punishable under Section 66K of the ETA—students should recognize grooming tactics (e.g., Pathao drivers targeted by fake profiles).
• Social media policies (e.g., Facebook’s Community Standards) clash with freedom of speech; Nepal’s Press Council monitors defamation cases tied to tweets or posts.
• Ethical hacking vs. cybercrime: Ethical hackers (like those at Nepal Computer Emergency Response Team) expose platform vulnerabilities, while scraping tools (e.g., Instagram bots) violate Copyright Act 2058.

1. Social Media Platforms and Data Collection

Social media platforms (Facebook, YouTube, Twitter/X, WhatsApp) monetize user data through targeted ads, but their privacy policies often conflict with ethical norms. Key mechanisms:

How Data is Collected

graph LR
    A["User Activity"] --> B["Explicit Data\n(Profile, Likes, Posts)"]
    A --> C["Implicit Data\n(IP Address, Cookies, Location)"
    B --> D["Third-Party Apps\n(Quizzes, Games, APIs)"]
    C --> D
    D --> E["Ad Targeting\n(Algorithms like Facebook’s ‘Dark Posts’)"]
    E --> F["Profit for Platforms\n($120B+ annual revenue)"]

• Explicit data: Profile details, posts, reactions (e.g., your Facebook "About" section).
• Implicit data: IP addresses, device IDs, browsing history (e.g., YouTube’s "Watch History").
• Third-party data: Apps like Khalti Login or Google Maps share data with Facebook.
• Algorithmic manipulation: Platforms use collaborative filtering (e.g., "People you may know") to predict behavior.

Real-World Example: Khalti and Facebook Data Leaks

In 2022, a third-party data broker sold 50M Nepali phone numbers (including Khalti users) to spam callers. This violated:

• Section 4 of Nepal’s ETA: Unauthorized data collection.
• Section 20 of the Privacy Act 2075: Mandates user consent for data use.

Worked Example: A Daraz seller uses Facebook Pixel to track your cart abandonment. When you visit another site with Facebook ads, you see Daraz promotions—this is legal but ethically questionable because:

1. You didn’t consent to cross-site tracking.
2. Daraz profits from your browsing without direct benefit to you.

2. Digital Footprints: Permanent and Powerful

A digital footprint is the trail of data you leave online—intentional (posts) or unintentional (metadata). It can:

• Help: Job applications (LinkedIn), networking (Facebook groups).
• Harm: Blackmail, discrimination, or cancel culture (e.g., a 2021 Nepali student fired after old Instagram memes resurfaced).

Types of Digital Footprints

How to Manage Your Footprint

1. Adjust privacy settings:
   • Facebook: Limit "Who can see your future posts?" to "Friends."
   • Twitter: Disable "Let others find you by your email."
2. Use incognito mode for sensitive searches (e.g., job applications).
3. Check Google’s "About You" page (google.com/about) to see what’s public.
4. Delete old accounts: Use JustDeleteMe.com to find platform deletion links.

Worked Example: NEPSE Stock Trader’s Downfall A Kathmandu trader posted insider tips on Twitter in 2022. When regulators traced the IP address to his brokerage firm, he was:

• Banned from trading (SEBON violation).
• Fired for violating company ethics policies.
• Sued for market manipulation under Section 58 of the Securities Act 2063.

3. Misinformation and Deepfakes: The Dark Side of Virality

Misinformation = False info spread without malicious intent. Disinformation = False info spread deliberately (e.g., fake news). Deepfakes = AI-generated fake videos/audio (e.g., a politician’s voice cloned).

How Misinformation Spreads

sequenceDiagram
    participant User
    participant Algorithm
    participant Bot
    participant Influencer
    User->>Algorithm: Shares sensational post
    Algorithm->>Bot: Boosts post (engagement bait)
    Bot->>Influencer: Sends "verified" shares
    Influencer->>User: Amplifies false claim
    User->>Algorithm: Engages (likes/shares)
    Note over User,Algorithm: Feedback loop creates echo chamber

Nepal’s Legal Response

• Electronic Transactions Act (ETA) 2063:
  • Section 37: Punishes cyber defamation (up to 3 years jail).
  • Section 41: Bans child pornography (including deepfake revenge porn).
• Cyber Crime Unit (Nepal Police):
  • 2022: Arrested 15 people for spreading #FakeNews about COVID vaccines.
  • 2023: Shut down 12 fake WhatsApp groups spreading election misinformation.

Worked Example: The "Ncell SIM Card Hack" Hoax In 2021, a WhatsApp forward chain claimed Ncell was selling SIM data to China. The Cyber Crime Unit debunked it by:

1. Checking Ncell’s official statement (no breach reported).
2. Tracing the original poster (a student in Pokhara).
3. Issuing a public warning under Section 39 of the ETA.

4. Cyberbullying and Online Harassment

Cyberbullying = Repeated harassment via digital means (texts, emails, social media). Grooming = Predators build trust to exploit victims (e.g., fake romance on Facebook).

Red Flags of Cyberbullying

Case Study: The "Kathmandu University Leak"

In 2020, a fake Twitter account (@KU_Admin) posted exam question papers before the test. The real KU condemned it, but:

• 10 students shared the leak (unaware it was fake).
• 2 were suspended for violating academic integrity policies.
• The account was traced to a disgruntled ex-student using burner emails.

How to Respond:

1. Save evidence: Screenshot messages, save DMs.
2. Block and report: Use platform tools (Facebook’s "Report Harassment").
3. Legal action: File a complaint at cybercrime.gov.np.

5. Ethical Dilemmas in Social Media

6. Social Media Policies vs. Freedom of Speech

Platforms like Facebook, YouTube, and Twitter enforce Community Standards, but these often conflict with free speech rights (guaranteed under Article 17 of Nepal’s Constitution).

Comparison: Nepal vs. Global Policies

Worked Example: YouTube’s "Controversial Content" Policy

• Nepal: A video calling Prime Minister Oli a "puppet" was demonetized but not removed (free speech).
• India: The same video would be banned under Section 66D (ITA 2000) for "grossly offensive" content.
• EU: If the video used AI-generated clips, YouTube must label it under GDPR.

7. Emerging Issues: AI and Social Media

AI is reshaping social media ethics:

1. AI Moderators: Platforms use automated tools to detect hate speech (e.g., Facebook’s DeepText).
   • Problem: False positives (e.g., Nepali slang marked as "abusive").
2. AI-Generated Content: Tools like MidJourney or DALL·E create fake images.
   • Example: A fake photo of a landslide in Pokhara went viral in 2023, causing panic.
3. Microtargeting: Ads use AI to exploit psychological triggers (e.g., Khalti’s "Limited-Time Offer" scams).

In the Real World

1. eSewa and Data Leaks

   • In 2021, a third-party vendor accessed 1M eSewa users’ transaction histories without consent.
   • Ethical Issue: Violated Section 4 (ETA) (unauthorized data access).
   • Real Impact: Scammers used the data to clone eSewa accounts for fraud.

2. Pathao Drivers and Fake Profiles

   • Problem: Fake Pathao driver accounts steal fares by hijacking real users’ locations.
   • How It Works:
     • Hackers scrape WhatsApp groups for driver contacts.
     • Use stolen SIMs to verify fake numbers.
   • Ethical Violation: Identity theft (Section 40, ETA) + fraud (Section 178, Penal Code).

3. Ncell’s "Free Data" Scam

   • Scam: Fake WhatsApp admins promised "unlimited free data" via a "Ncell promo link."
   • Reality: The link installed malware (e.g., Anubis spyware) to steal contacts.
   • Legal Action: Cyber Crime Unit traced the Indian IP addresses and warned users.

Exam Tip

This unit is heavily tested in TU/PU exams with:

1. Case Studies (30% weight):

   • Expect Nepal-specific examples (e.g., "How would you handle a deepfake of a Nepali politician?").
   • Key Angles:
     • Legal: Which ETA section applies?
     • Ethical: Was the action intentional? Who is harmed?
     • Technical: How was the breach/exploit possible?

2. Short-Answer Questions (40% weight):

   • Define terms like:
     • Digital footprint (include active/passive/metadata).
     • Cyberbullying (cite Section 66K).
     • Deepfake (mention AI tools like FaceSwap).
   • Compare:
     • Freedom of speech vs. hate speech laws (Nepal vs. EU).
     • Ethical hacking vs. cybercrime (e.g., Nepal CERT vs. scraping bots).

3. Scenario-Based Questions (30% weight):

   • Example Question:

     "A student posts a fake ‘NTA exam leak’ on Facebook. 500 people share it before it’s debunked. What are the ethical and legal consequences?"

   • Model Answer Structure:
     1. Ethical: Violates trust and academic integrity.
     2. Legal:
        • Section 37 (ETA): Cyber defamation.
        • Section 14 (Education Act 2018): Academic misconduct.
     3. Platform Policy: Facebook’s Community Standards (misinformation removal).
     4. Real-World Impact: Could lead to exam cancellations (as in 2020’s #NTALeak scandal).

Pro Tip:

• Memorize these ETA sections:
  • Section 4: Data protection.
  • Section 37: Cyber defamation.
  • Section 40: Identity theft.
  • Section 66K: Cyberbullying.
• For case studies, always link to:
  • A real Nepali platform (e.g., Khalti, Pathao, eSewa).
  • A legal case (e.g., 2022 #FakeNews arrests).
  • An ethical dilemma (e.g., "Would you report a friend’s deepfake?").

Final Visual Summary

mindmap
  root((Social Media Ethics))
    Data Collection
      Explicit Data
      Implicit Data
      Third-Party Risks
    Digital Footprints
      Active vs. Passive
      Metadata Dangers
      Management Tips
    Misinformation
      Deepfakes
      Nepal’s ETA Response
    Cyberbullying
      Doxxing
      Grooming
      Legal Recourse
    Ethical Dilemmas
      Privacy vs. Convenience
      Free Speech Limits
    AI’s Role
      Moderation Bias
      Fake Content
      Microtargeting