This study explores the effect of unprecedented mass isolation during COVID-19 lockdowns through the lens of self-disclosure of loneliness on Twitter. Using a dataset of 30 million public tweets, we use machine learning to identify tweets that contain self-disclosure of loneliness. We find that thousands more people turned to Twitter to express their loneliness during the lockdowns; however, this effect normalized within a month, demonstrating the “ordinization” effect on a collective level. Furthermore, lockdown brought a marked shift in the weekly timings of posting and a change in the accompanying emotions, which were more positive and other-focused. Finally, based on a qualitative analysis, we propose an updated typology of loneliness that captures the possibilities offered by the affordances of social media. Our findings illustrate the profound effect lockdowns had on the societal psychological state and emphasize the importance of mental health resources during extreme and isolating events.

这项研究通过在 Twitter 上自我披露孤独感的镜头，探讨了在 COVID-19 封锁期间前所未有的大规模隔离的影响。使用包含 3000 万条公共推文的数据集，我们使用机器学习来识别包含自我披露孤独感的推文。我们发现，在封锁期间，成千上万的人转向 Twitter 表达他们的孤独感；然而，这种效应在一个月内就正常化了，证明了集体层面的“有序化”效应。此外，封锁带来了每周发帖时间的显着变化以及随之而来的情绪的变化，这些情绪更加积极和专注于其他。最后，基于定性分析，我们提出了一种更新的孤独类型学，它捕捉了社交媒体提供的可能性。