AI Example - digging through the memory hole

The media and governments try to hide or alter information by deleting information previously published on the internet. Fortunately, there are archive sites that regularly save pages, in particular: https://archive.org/ and https://archive.ph/ 

To find that that pages have been deleted is not obvious. Most of the time, you just discover a page deletion or page move by accident. You have to have a link to a page that you saved and try to access, to find that it is no longer there.

Discovering alterations to pages is even more complex.

So I was wondering: wouldn't it be a great use of AI to find such deleted and altered pages?

Would an AI even understand the request?

My Question to Grok

Would it be possible for you to go through the entire internet archive recordings of a given mainstream media source - say the NZZ, the NYT etc. - and find out: - what articles were deleted and if deletions concern specific topics? - if and how they altered articles over time?

The impressive answer

Analyzing the entire Internet Archive (e.g., Wayback Machine) for a mainstream media outlet like Neue Zürcher Zeitung (NZZ) or The New York Times (NYT) to identify deleted articles and track alterations is theoretically possible but practically challenging due to the scale, complexity, and limitations of the data. Below, I’ll outline the feasibility, methodology, challenges, and potential insights, while addressing your specific questions about deletions (and their topics) and alterations over time.

Feasibility and Approach

The Internet Archive’s Wayback Machine captures snapshots of websites at irregular intervals, preserving HTML, images, and other content as it appeared at specific points in time. For a media outlet like NZZ or NYT, this includes article pages, homepages, and other publicly accessible content. However, the archive is not a complete record—it captures only what was available during a crawl, and some content may be blocked (e.g., by robots.txt) or behind paywalls.

To perform the analysis you’re requesting, one would need to:

1. Identify Snapshots: Collect all Wayback Machine captures for the target domain (e.g., nytimes.com or nzz.ch) over a specified time period.
2. Extract Articles: Scrape article URLs, titles, and content from each snapshot, accounting for changes in site structure.
3. Detect Deletions: Compare snapshots to identify articles present in earlier captures but absent in later ones, suggesting deletion or archiving.
4. Track Alterations: Compare versions of the same article across snapshots to detect changes in text, headlines, or metadata.
5. Analyze Topics: Use natural language processing (NLP) to categorize articles by topic and identify patterns in deletions or alterations.

This process would require significant computational resources, custom scripts, and access to the Internet Archive’s data (potentially via their API or bulk data access for researchers).

Challenges

1. Incomplete Archives:
   • The Wayback Machine does not capture every page or version of a site. Crawls are sporadic, and some articles may never have been archived.
   • Dynamic content (e.g., JavaScript-driven pages) or paywalled articles may not be fully captured.
   • Media outlets like NYT have blocked Wayback Machine crawlers at times, limiting available data (e.g., NYT attempted to block an Internet Archive crawler in 2023).
2. Scale:
   • NYT’s archive includes over 13 million articles since 1851, with millions more for NZZ or similar outlets.
   • Processing millions of snapshots, each containing thousands of pages, requires substantial storage and processing power.
3. Deletions vs. Archiving:
   • Articles may disappear from a site not because they were deleted but because they were moved to a different URL, archived behind a paywall, or restructured during site redesigns.
   • Distinguishing intentional deletion from technical changes (e.g., URL redirects) is difficult without insider knowledge.
4. Alterations:
   • Detecting changes requires comparing full-text versions of articles across snapshots. Minor edits (e.g., typo fixes) must be distinguished from significant alterations (e.g., retractions or rewritten narratives).
   • Some changes, like headline tweaks, may not be captured if snapshots are too infrequent.
5. Topic Analysis:
   • Classifying articles by topic requires NLP models to process large volumes of text, potentially in multiple languages (e.g., German for NZZ, English for NYT).
   • Topic modeling may miss nuanced patterns without manual review or predefined categories.
6. Legal and Ethical Constraints:
   • Media outlets may object to large-scale scraping of their archives, citing copyright or terms of service.
   • The Internet Archive’s data use policies may restrict bulk analysis for commercial purposes.

What Articles Were Deleted and Do Deletions Concern Specific Topics?

Detecting Deletions:

• By comparing snapshots, one could identify articles present in an earlier capture but missing in later ones. For example, if an article URL appears in a 2015 snapshot of nytimes.com but not in 2016, it may have been deleted or moved.
• Tools like the Wayback Machine’s CDX API can help list captured URLs, which can then be cross-referenced to detect absences.
• However, deletions are hard to confirm without access to the outlet’s internal records. An article may still exist behind a paywall or under a new URL.

Topic Patterns:

• To determine if deletions target specific topics, NLP techniques (e.g., topic modeling with LDA or keyword analysis) could classify articles based on content. For instance, articles about controversial political events, corporate scandals, or international conflicts could be flagged.
• Anecdotal evidence suggests some media outlets remove or alter articles to avoid legal liability, public backlash, or reputational damage. For example:
  • The NYT has been criticized for “stealth edits” (unannounced changes) to headlines or articles, such as altering references to “Hamas terrorists” to “gunmen” in 2023, possibly to adjust framing.
  • No large-scale studies confirm systematic deletion of specific topics, but case studies (e.g., via X posts or investigative reports) highlight deletions related to sensitive issues like war crimes or corporate malfeasance.
• Without comprehensive data, it’s speculative to claim deletions systematically target topics, but patterns could emerge with enough analysis (e.g., overrepresentation of political or legal topics in deleted content).

Limitations:

• The Internet Archive may not capture short-lived articles (e.g., posted and deleted between crawls).
• Topic analysis is resource-intensive and may require manual validation to avoid false positives.

If and How Articles Were Altered Over Time

Detecting Alterations:

• By comparing snapshots of the same article URL, one can identify changes in:
  • Headlines: E.g., NYT’s headline changes in 2023 from “Israeli Strike Kills Hundreds in Hospital” to “At Least 500 Dead in Blast at Gaza Hospital, Palestinians Say” within hours.
  • Body Text: Additions, removals, or rephrasings of paragraphs.
  • Metadata: Publication dates, author names, or tags.
• Tools like diff (for text comparison) or specialized software (e.g., Change Detection) can highlight alterations.

Types of Alterations:

• Minor Edits: Typo corrections, style updates, or clarifications. These are common and often benign.
• Significant Changes: Rewriting to shift narrative, remove controversial claims, or align with new information. For example:
  • Retractions or corrections due to factual errors.
  • Adjustments to avoid legal issues (e.g., defamation).
  • Changes in tone or framing, as seen in NYT’s coverage of sensitive geopolitical events.
• Unannounced Edits: Some outlets, including NYT, have been criticized for making “stealth edits” without public correction notices, which can obscure accountability.

Patterns Over Time:

• Alterations may reflect evolving editorial standards, legal pressures, or public sentiment. For example:
  • Articles from the early 2000s may have been edited to comply with modern digital formats or privacy laws (e.g., GDPR in Europe).
  • Coverage of ongoing conflicts (e.g., Ukraine, Israel-Palestine) may see frequent updates as new information emerges or political pressures shift.
• Historical articles (e.g., pre-1996 for NYT) are less likely to be altered due to their archival nature, but digital-era articles are more dynamic.

Limitations:

• Infrequent snapshots may miss intermediate changes.
• Paywalled or dynamic content may not be fully captured, skewing analysis.
• Distinguishing intentional alterations from routine updates requires context (e.g., editorial policies, external events).

Practical Insights and Alternatives

Given the challenges, a full analysis of NZZ or NYT’s Internet Archive records is likely infeasible for an individual or small team without significant resources. However, targeted studies could yield insights:

• Focus on Specific Periods or Topics: Analyze snapshots for a single year (e.g., 2020 for pandemic coverage) or a controversial event (e.g., 2023 Israel-Hamas conflict) to identify deletions or alterations.
• Use Case Studies: Investigate known instances of deletions or edits (e.g., via X posts or reports like The Intercept’s coverage of NYT’s crawler blocking) to build a qualitative picture.
• Collaborate with Researchers: Academic projects or organizations like the Internet Archive may have tools or datasets for bulk analysis.
• Alternative Sources: Cross-reference with other archives (e.g., LexisNexis, ProQuest) or social media discussions on X to identify deleted or altered content.

Example Findings (Hypothetical, Based on Available Evidence):

• NYT:
  • Deletions: Rare, but some articles from the digital era (post-1996) may be removed due to legal settlements or editorial decisions. Topics like corporate misconduct or geopolitical controversies may be overrepresented.
  • Alterations: Frequent for breaking news (e.g., headline changes within hours) or to align with evolving narratives. Stealth edits have been documented, particularly in sensitive foreign policy coverage.
• NZZ:
  • Less documented, but as a Swiss outlet, NZZ may alter articles to comply with European privacy laws (e.g., right to be forgotten) or to refine its neutral stance on international issues.
  • Deletions are likely minimal but could involve legal or reputational issues.

Conclusion

While it’s theoretically possible to analyze the Internet Archive for NZZ or NYT to identify deleted articles and alterations, practical constraints— incomplete archives, scale, and legal issues—make a comprehensive study difficult. Targeted analyses (e.g., specific years or topics) are more feasible and could reveal patterns, such as deletions tied to controversial topics or alterations reflecting editorial shifts. For deletions, topics like politics, legal disputes, or corporate scandals may be more prone to removal, while alterations often occur in breaking news or sensitive coverage. To pursue this, you’d need specialized tools, access to bulk data, and possibly collaboration with archival researchers.

If you’d like, I can:

• Suggest tools or scripts for smaller-scale analysis (e.g., scraping Wayback Machine snapshots).
• Search X or the web for specific instances of NZZ/NYT deletions or edits.
• Focus on a particular time period or topic for a deeper dive.

Let me know how you’d like to proceed!

CONCLUSION ABOUT THE AI ANSWER

I know very few people who would fully understand my question and its purpose, let alone have the required knowledge to provide such an extensive analysis.

Grok, at this point, appears smarter than 95% of humanity.