Aydın Tiryaki (2026)

Introduction

Today, social media platforms, especially X (formerly Twitter), have transformed from an “agora” where individuals freely express their opinions into a battlefield where manipulated data collide. From an engineering perspective, this information pollution is not a random chaos, but rather a designed or tolerated systematic disorder. To analyze this structure, the platform must be divided into two main problem clusters: Gray Areas (Manipulative Traffic) and Black Areas (Troll Armies and Malicious Structures).

1. GRAY AREA: Agenda Manipulation and “Keyword Stuffing”

The gray area covers posts that exploit the platform’s technical loopholes to gain visibility but are essentially “garbage” (spam) in content. The most prominent example is the pollution in the “Trend Topic” (TT) list. Users deceive the system by hiding advertisements, betting sites, or propaganda content—irrelevant to the topic—under the popular tags of the moment.

Problem: When a citizen clicks on the TT list to learn the real agenda, they are confronted with thousands of meaningless messages filled with irrelevant keywords (keyword stuffing). This situation renders the TT list dysfunctional and usurps the citizen’s right to access accurate information.

Algorithmic Solution Proposal (Shadow Banning): Complex artificial intelligence models are not even needed to solve this problem. A simple logical filter is sufficient:

• Logic: If a message contains more than a certain number (e.g., more than 3) of the popular words currently in the TT list, the system should mark this message as a “spam candidate.”
• Application: The system shows the user that their message has been published (so the user does not realize they are blocked), but in the background, it removes this message from search results and the TT pool.
• Result: The manipulator sees their own writing and is satisfied, but the system remains clean.

2. BLACK AREA: Troll Armies and “Two-Legged Bots”

The black area is the center of organized malice. The actors here are not just software (scripts/bots), but also real people (“Two-Legged Bots”) who are funded by public or private funds and operate within a chain of command. Their purpose is not to debate; it is to target, lynch, and suppress.

Problem: These structures attack the same target simultaneously with instructions received from outside (e.g., via messaging apps). Although platform management hides behind the defense of “We cannot see that the instruction came from outside,” this cannot be an excuse. Because even if the input is hidden, the output (action) is measurable.

Algorithmic Solution Proposal (Coordination Analysis and Contagion): To understand whether a crime or manipulation is organized, “Network Topology” and “Timestamp” analysis should be performed:

• Time Analysis: If thousands of accounts that do not know each other launch an attack with the same tag within a suspiciously short time interval (e.g., within 3 minutes), this is not an organic reaction but the result of an “Impulse Signal.”
• Clustering: When the system detects a troll army, it should evaluate not only that army but also the “peripheral supporters” of that army (secondary accounts that constantly retweet and support) within the same cluster.
• Scoring: If an account constantly moves with these “coordinated attack” clusters, its “Authenticity Score” should be reduced, and its interaction power should be reset.

3. THE COMPLAINT MECHANISM USED AS A WEAPON

One of the most dangerous tactics of troll armies is to have opposition or “unwanted” accounts closed through unfounded complaints made from thousands of fake accounts (Mass Reporting). In the current primitive system, a complaint from a troll and a complaint from a respected citizen count as “1” (one).

Solution: Weighted Voting System For a fair digital management, a “Trust Coefficient” must be introduced:

• Principle: The weight of an account’s complaint in the system should be proportional to that account’s past cleanliness.
• Application: If an account has previously been defined within the “Black Area” (troll network), the coefficient of any complaint made by this account should be ZERO. In other words, even if 10,000 trolls unite to report an account, the total effect should be zero, and the innocent account should be protected.
• Restoration of Reputation: Accounts that were closed in the past due to complaints from accounts later proven to be trolls should be automatically taken under review and reopened.

4. CONCLUSION: Capacity Exists, Will Does Not

From an engineering science perspective, all the problems listed above are solvable issues. The necessary algorithms exist, and processing power is capable of performing these analyses in seconds.

However, platform managements operate the social media version of “Gresham’s Law”: “Bad users drive out good users.” Companies continue to view this “black and gray” crowd as “customers” and “benefactors” in order not to experience a decline in the “active user” and “traffic” reports they present to investors.

In conclusion; the pollution on X and similar platforms is not the result of technical inadequacy, but of a commercial and political choice. Unless these platforms risk their own “suicide” to perform this cleanup, they are doomed to cease being communication tools and transform into propaganda machines where only those who pay are heard.

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A Note on Methods and Tools: All observations, ideas, and solution proposals in this study are the author’s own. AI was utilized as an information source for researching and compiling relevant topics strictly based on the author’s inquiries, requests, and directions; additionally, it provided writing assistance during the drafting process. (The research-based compilation and English writing process of this text were supported by AI as a specialized assistant.)