Tag: Automated Accounts

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Automated accounts that recycle one another’s material

Ten accounts repeating the same thing can look like ten sources.

That is one of the simplest ways automation can distort the apparent size of an online conversation. A bot does not need to invent anything. It can copy a caption, repost a link, slightly alter a sentence, or relay material from another automated account. After enough hops, the visible network looks busy even though very little independent creation occurred.

Researchers have documented this pattern in commercial social-media environments. A 2018 study of SoundCloud activity examined more than 12 million comments and found highly active suspicious accounts that posted repetitive comments, frequently reposted existing content, and contributed relatively little original material. The authors used comment uniqueness and network behavior as clues when distinguishing likely bots and semi-automated accounts from ordinary users. See Social bots in a commercial context — A case study on SoundCloud.

Circulation is not creation

Reposting has legitimate uses. Human communities share jokes, announcements, songs, emergency information, and news links constantly. Automated accounts can also perform useful redistribution: mirroring updates, relaying weather alerts, or syndicating posts to another platform.

The measurement problem begins when repeated circulation is interpreted as independent authorship or independent agreement.

Imagine one account posts a sentence. Twenty automated accounts copy it. Another hundred accounts encounter those copies instead of the original. A researcher who counts only visible posts might record 121 pieces of activity. A reader may perceive widespread agreement. Yet the intellectual source may still be one person, one script, or one upstream feed.

Attribution gets weaker as the material moves. Usernames change. Links disappear. Screenshots replace original posts. Small rewrites break exact-text matching. Eventually a recycled statement may look native to the account currently carrying it.

Repetition can manufacture apparent consensus

This is why source tracing matters more than raw counts.

A useful investigation asks whether accounts are posting independently, whether they share identical URLs or wording, whether their timing is synchronized, and whether the chain leads back to a common source. Recent research on coordinated reposting on Bluesky has used precisely this kind of timing and shared-content analysis to distinguish ordinary repost behavior from suspicious coordination.

None of this means repeated material is automatically bot-generated. Humans copy one another too. Fan communities, customer-service teams, volunteer campaigns, and newsrooms all reuse language.

The narrower point is easier to establish: many visible copies do not imply many independent origins.

Dead Internet Theory often treats repetition as evidence that nobody real is speaking. That conclusion goes too far. Repetition can be human, automated, coordinated, accidental, or mixed.

But when automated accounts recycle one another’s material, the internet can appear to contain more voices than it contains sources. That distinction matters.

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Bot traffic versus bot-authored public conversation

A statistic such as “53 percent of web traffic is automated” sounds like it should tell us how much of the internet is made by bots.

It does not.

It tells us something important, but narrower: machines are responsible for a large share of requests reaching websites and applications. That is a measurement of traffic, not a census of who wrote the visible conversation.

Imperva’s 2026 Bad Bot Report says automated systems generated more than 53 percent of observed web traffic in 2025. That is a remarkable number. It is also easy to misuse.

A request is not a comment

Web traffic measurements usually count HTTP requests. A crawler fetching an article creates a request. A monitoring service checking whether a page is alive creates a request. A scraper downloading prices creates requests. An API client retrieving data creates requests. An attacker testing credentials creates many requests very quickly.

None of those activities necessarily writes anything that another person will read.

Google describes Googlebot as the crawler used by Google Search. It visits pages automatically so they can be indexed. Those visits are bot traffic in the literal sense, but Googlebot is not sitting in the comments pretending to be your uncle.

The mismatch also works in the other direction. One automated account can publish thousands of posts while producing only a modest fraction of a platform’s total network traffic. Meanwhile a single human opening a modern page can trigger requests for HTML, images, scripts, fonts, analytics, advertisements, APIs, and background updates.

The units simply do not map cleanly.

Measuring synthetic conversation requires conversation data

If the question is “What share of public discussion is machine-authored?” the sample has to contain public discussion: posts, replies, comments, messages, or other defined conversational units.

Researchers then need a defensible method for deciding which items are automated. That may involve account behavior, posting tools, content provenance, network patterns, manual review, disclosures, or known ground-truth accounts. Each method has uncertainty and false positives, but at least it measures the thing being claimed.

A traffic report cannot substitute for that work.

This distinction matters for Dead Internet Theory because traffic statistics are often used as if they prove a stronger proposition: if most requests are automated, then most apparent human activity must also be automated. That conclusion does not follow.

The automated web is already enormous. Search crawlers, security scanners, monitoring systems, AI agents, scrapers, spam tools, and malicious bots really do talk to servers all day without a person clicking anything.

That fact is worth studying on its own.

But a machine requesting a webpage and a machine impersonating a person in a conversation are two different events. Counting the first cannot tell us how common the second is.