Does X (Formerly Twitter) Shadowban Liberal Political Content or Small Businesses? An OSINT Investigation for WPS.news

Does Shadowbanning? Examining Claims of Bias

This report investigates allegations that X (formerly known as Twitter) engages in shadowbanning, a practice where content is restricted from user feeds or search results without explicit bans. The report specifically examines claims of bias against liberal political content and small businesses.

Understanding Shadowbanning:

Shadowbanning is a controversial practice. Platforms like X rely on algorithms to rank content based on factors like relevance and user engagement. Critics argue that these algorithms can unintentionally or intentionally disadvantage certain content, creating an environment of “shadowbanning.”

Allegations of Bias:

There have been persistent claims from users and some researchers that X shadowbans liberal political viewpoints and content from small businesses. These claims suggest an algorithmic bias that unfairly suppresses the reach of certain voices.

X’s Response:

X has consistently denied any intentional shadowbanning. They emphasize that their algorithms focus on user engagement and relevance. X has also made efforts towards transparency by releasing some information on how their algorithms work.

Challenges and Limitations:

The exact workings of X’s algorithm remain largely opaque, making it difficult to definitively assess claims of shadowbanning. Without complete transparency, verifying or refuting such allegations is a significant challenge.

Investigating the Issue with OSINT:

Open-Source Intelligence (OSINT) can be used to explore this issue further. Here are some resources for further research:

• X Transparency Center: While not as detailed as some might like, the X Transparency Center offers information on X’s content moderation policies and algorithmic ranking. https://transparency.x.com/en [see current report below]
• Academic Research: Searching Google Scholar with keywords like “X shadowbanning,” “algorithmic bias,” and “content moderation” may reveal relevant academic studies that explore the technical aspects of platform algorithms and potential biases.
• News Articles: Analyzing news articles discussing shadowbanning claims on X can offer insights into user experiences and current discussions surrounding platform practices.

The Name “X” and Elon Musk:

The name “X” is associated with the concept of an “everything app” that aims to encompass various functionalities beyond social media. Elon Musk, the owner of X, has not publicly explained the specific meaning behind the name. There is no evidence to suggest the name “X” holds any illegitimate connotations in itself.

Conclusion: Empowering Informed Opinions

The issue of shadowbanning on X remains complex and unresolved. While some users allege bias, a lack of conclusive evidence necessitates further investigation. Utilizing OSINT resources and critical analysis can help shed light on this issue.

Recommendations for Readers:

• To form your own informed conclusion, consider both sides of the argument: allegations of shadowbanning and X’s denials.
• Be aware of the limitations in knowledge regarding the algorithm’s inner workings.
• Consider the potential for algorithmic bias in the absence of clear public oversight.

By providing a comprehensive overview of the issue, we can empower readers to form their own informed conclusions about potential shadowbanning practices on X.

Publishers note: Our investigation into X’s shadowbanning practices led us to their Transparency Center. While the information presented aims to shed light on content moderation and algorithmic ranking, it left us with more questions than answers. The documentation relies heavily on corporate speak, offering vague explanations for content moderation actions. Specific details about what triggers certain actions and the reasoning behind them are noticeably absent. This lack of transparency makes it difficult to assess potential bias within the algorithm and hold X accountable for its content moderation practices.

In our view, X’s transparency efforts are a thinly veiled attempt to obfuscate its true intentions. Their documentation is rife with corporate jargon, lacking the specificity required to truly understand their content moderation practices. This deliberate vagueness raises serious concerns about potential bias and censorship on the platform. X must be held accountable for its actions and provide concrete answers to the public’s questions.