Recent computational research indicates that social media polarization is a product of platform architecture rather than algorithmic manipulation, coinciding with a systemic shift toward AI-generated content and algorithmic broadcasting.

近期的計算研究指出，社群媒體的極端分化是平台架構的產物而非演算法操弄，且與系統向 AI 生成內容及演算法廣播的轉型趨勢相吻合。

Analysis conducted by Petter Törnberg of the University of Amsterdam suggests that echo chambers are an emergent property of social media's fundamental structure. Utilizing agent-based modeling and large language models, Törnberg demonstrated that ideological segregation occurs even in the absence of 'filter bubbles' when users migrate from communities where disagreement exceeds a specific threshold. Paradoxically, the research posits that limited exposure to agreeable content—a controlled filter bubble—could stabilize diversity by preventing total community collapse. Empirical evidence from the r/MensRights subreddit corroborates this, showing a correlation between ideological distance and user attrition, which subsequently accelerates the homogenization of the remaining cohort.

阿姆斯特丹大學 Petter Törnberg 的分析指出，「同溫層」是社群媒體基本結構的一種湧現特性。Törnberg 利用基於代理人的模型與大型語言模型證明，即使在沒有「過濾氣泡」的情況下，只要使用者從分歧程度超過特定門檻的社群遷移，仍會發生意識形態隔離。矛盾的是，研究假設有限度地接觸相近觀點的內容（即受控的過濾氣泡）反而能防止社群全面崩潰，進而穩定多樣性。

Concurrent data from the American National Election Studies reveals a decline in engagement on legacy platforms such as Facebook and Twitter/X, attributed to a reduction in human users and a corresponding increase in automated AI agents. A significant ideological shift was observed on Twitter/X, characterized by a 72 percentage point movement toward right-wing engagement. Furthermore, the landscape of digital interaction is transitioning from text-based social networking toward 'algorithmic broadcasting' (e.g., TikTok) and private encrypted communication (e.g., WhatsApp). Törnberg argues that these private spaces do not replicate the diversity of physical public squares, as they lack geographical constraints and remain susceptible to the same feedback loops that drive polarization.

同時，美國國家選舉研究的數據顯示，Facebook 和 Twitter/X 等傳統平台的參與度下降，這歸因於真人使用者的減少以及自動化 AI 代理人的相應增加。在 Twitter/X 上觀察到顯著的意識形態轉移，向右翼參與度偏移了 72 個百分點。此外，數位互動的格局正從文字社交網路轉向「演算法廣播」（如 TikTok）和私人加密通訊（如 WhatsApp）。Törnberg 主張，這些私人空間無法複製實體公共廣場的多樣性，因為它們缺乏地理限制，且依然容易受到驅動極端分化的相同反饋迴路影響。

Complementary research into hate community persistence identifies two primary mechanisms of sustainment: the repetition of consistent narratives and the continuous introduction of novel accusations. In Islamophobic cohorts, stability is maintained through the reiteration of narrow, religiously framed messages. Conversely, antisemitic groups utilize a diversified array of narratives, including victimization and conspiracy theories. The findings suggest that while the removal of prolific 'super-users' may mitigate engagement in some groups, communities driven by a decentralized influx of novel content are more resilient to moderation efforts.

關於仇恨社群持久性的補充研究確定了兩種主要的維持機制：重複一致的敘事以及不斷引入新的指控。在反伊斯蘭社群中，穩定性是透過重複狹隘的宗教框架訊息來維持的。相反，反猶太群體則利用多樣化的敘事，包括受害者心態與陰謀論。研究結果顯示，雖然移除高產的「超級使用者」可能會降低某些群體的參與度，但由去中心化新內容湧入所驅動的社群對審核措施更具韌性。

The digital ecosystem is transitioning from user-centric social networking to AI-mediated broadcasting and private silos, with structural feedback loops continuing to facilitate ideological extremism.

數位生態系統正從以使用者為中心的社交網路，轉向由 AI 媒介主導的廣播與私人孤島，而結構性的反饋迴路將持續助長意識形態極端化。