ObjectivesLoneliness is a major public health problem and an estimated 17% of adults aged 18–70 in the USA reported being lonely. We sought to characterise the (online) lives of people who mention the words ‘lonely’ or ‘alone’ in their Twitter timeline and correlate their posts with predictors of mental health.Setting and designFrom approximately 400 million tweets collected from Twitter in Pennsylvania, USA, between 2012 and 2016, we identified users whose Twitter posts contained the words ‘lonely’ or ‘alone’ and compared them to a control group matched by age, gender and period of posting. Using natural-language processing, we characterised the topics and diurnal patterns of users’ posts, their association with linguistic markers of mental health and if language can predict manifestations of loneliness. The statistical analysis, data synthesis and model creation were conducted in 2018–2019.Primary outcome measuresWe evaluated counts of language features in the users with posts including the words lonely or alone compared with the control group. These language features were measured by (a) open-vocabulary topics, (b) Linguistic Inquiry Word Count (LIWC) lexicon, (c) linguistic markers of anger, depression and anxiety, and (d) temporal patterns and number of drug words. Using machine learning, we also evaluated if expressions of loneliness can be predicted in users’ timelines, measured by area under curve (AUC).ResultsTwitter timelines of users (n=6202) with posts including the words lonely or alone were found to include themes about difficult interpersonal relationships, psychosomatic symptoms, substance use, wanting change, unhealthy eating and having troubles with sleep. Their posts were also associated with linguistic markers of anger, depression and anxiety. A random forest model predicted expressions of loneliness online with an AUC of 0.86.ConclusionsUsers’ Twitter timelines with the words lonely or alone often include psychosocial features and can potentially have associations with how individuals express and experience loneliness. This can inform low-resource online assessment for high-risk individuals experiencing loneliness and interventions focused on addressing morbidities in this condition.

number of clusters. Analyses were conducted in Stata SE 15 statistical package (StataCorp LLC). Statistical significance was defined as a 2-sided P < .05.Results | Of the 781 households (0.63%) that could not automatically reenroll in Covered California because of insurer exit, unadjusted and adjusted reenrollment rates were 21.4% and 21.5%, respectively (Table ). Both the unadjusted and adjusted reenrollment rates among the 122 463 households with the option to automatically reenroll were 51.2%. Losing the option to automatically reenroll was associated with a 30percentage point decrease in enrollment both with adjusting for household characteristics (95% CI, 9.4%-52.0%; P < .001) and without (95% CI, 14.2%-46.8%; P < .001).Discussion | Elimination of automatic reenrollment would likely be associated with decreases in the number of enrollees who remain insured through the marketplaces. As an opt-out policy 3 similar to that used in other health insurance markets such as Medicaid, 2 automatic reenrollment may be associated with increases in continuity of coverage in the marketplaces by reducing administrative barriers to reenrollment.Although we found losing an automatic reenrollment option was associated with decreases in reenrollment, this association requires further study. The group that lost the automatic reenrollment option was relatively small. Households with different demographics or different experiences with insurers may have behaved differently if they had lost the option to automatically reenroll. Losing automatic reenrollment because of policy changes rather than insurer exit also may be associated with households behaving differently. Given the magnitude of our findings, it is critical that future studies continue investigating the association between automatic reenrollment and continuity of coverage.

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