Why is Threads recommending these weird spammy posts from people looking for 'friends'?

Engadget - May 9th, 2025

Threads, Meta's social media platform, is experiencing a surge in spam posts appearing in its recommendation algorithm. Users have reported strange posts from individuals seeking 'friends', often accompanied by selfies and WhatsApp links, appearing in the 'related threads' feature. Despite Meta's acknowledgment of increased 'spam attacks', the frequency and nature of these posts are raising concerns about the platform's content moderation capabilities. Engadget's observations, along with user feedback, highlight a pattern of repetitive and unsolicited content disrupting the user experience.

The broader context reveals that as Threads' user base surpasses 350 million, the challenge of managing spam becomes more pronounced. Meta's prior attempts to address engagement bait and spam seem insufficient in preventing these bizarre posts from being surfaced as recommended content. This issue extends beyond Threads, as similar incidents have been reported on Instagram, suggesting a systemic problem across Meta's platforms. The implications of such pervasive spam are significant, potentially affecting user trust and engagement on Threads and other Meta services.

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RATING

6.4

Moderately Fair

Read with skepticism

The article effectively highlights the issue of spam posts on Threads, providing a timely and relevant discussion for users and those interested in social media dynamics. It uses specific examples and user observations to support its claims, though it could benefit from more authoritative sources and a deeper exploration of Meta's perspective. The clarity and readability of the article are strong, making it accessible to a broad audience. However, its impact and potential to provoke significant debate are limited by the lack of diverse perspectives and in-depth analysis of the algorithmic issues at play. Overall, the article serves as a useful starting point for understanding the challenges of content moderation on Threads, but it could be strengthened with more comprehensive sourcing and transparency.

Accuracy

The article presents several factual claims about the appearance of spam posts on Threads, which are generally supported by user observations and examples. However, there are areas that require further verification, such as the specific mechanisms of the algorithm that lead to these recommendations. The article accurately notes that Meta confirmed spammy posts shouldn't be featured as recommended content, which aligns with the company's public stance on managing spam. However, the lack of direct quotes from Meta or detailed explanations of the algorithm's functioning slightly detracts from the precision of the reporting.

Balance

The article primarily focuses on the issue of spam posts on Threads, largely from the perspective of users and the platform's challenges in managing such content. While it mentions Meta's acknowledgment of spam issues, it does not provide a detailed exploration of Meta's efforts or perspectives from the company, which could offer a more balanced view. The absence of counterpoints or alternative explanations for the algorithm's behavior suggests a slight imbalance in the presentation.

Clarity

The article is generally clear and well-structured, with a logical flow that guides the reader through the issue of spam posts on Threads. The language is straightforward, and the examples provided are easy to understand. However, the article could benefit from more detailed explanations of technical terms related to algorithmic recommendations to ensure full comprehension by a general audience.

Source quality

Engadget, as a source, is generally reliable for technology news, and the article includes observations from its own editor-in-chief. However, the reliance on user reports and the lack of direct quotes from Meta representatives or other authoritative sources on algorithmic behavior limits the depth of the source quality. The article could benefit from more diverse and authoritative sources to strengthen its claims.

Transparency

The article lacks detailed explanations of the methodology behind its observations, such as how widespread the spam issue is or how the examples were selected. It does not disclose any potential conflicts of interest, which could affect impartiality. Providing more context about how the information was gathered and any limitations encountered would enhance transparency.