Individuals with depression express more distorted thinking on social media

Individuals with depression express more distorted thinking on social media. Krishna C. Bathina, Marijn ten Thij, Lorenzo Lorenzo-Luaces, Lauren A. Rutter & Johan Bollen. Nature Human Behaviour, February 11 2021. https://www.nature.com/articles/s41562-021-01050-7

Abstract: Depression is a leading cause of disability worldwide, but is often underdiagnosed and undertreated. Cognitive behavioural therapy holds that individuals with depression exhibit distorted modes of thinking, that is, cognitive distortions, that can negatively affect their emotions and motivation. Here, we show that the language of individuals with a self-reported diagnosis of depression on social media is characterized by higher levels of distorted thinking compared with a random sample. This effect is specific to the distorted nature of the expression and cannot be explained by the presence of specific topics, sentiment or first-person pronouns. This study identifies online language patterns that are indicative of depression-related distorted thinking. We caution that any future applications of this research should carefully consider ethical and data privacy issues.

Discussion

In a sample of online individuals, we used a theory-driven approach to measure the prevalence of linguistic markers that may indicate cognitive vulnerability to depression, according to CBT theory. We defined a set of CDS that we grouped along 12 widely accepted types of distorted thinking and compared their prevalence between two cohorts of Twitter users—the first included individuals who reported that they received a clinical diagnosis of depression and the second was a similar random sample.

As hypothesized, the individuals in the D cohort use significantly more CDS in their online language compared with individuals in the R cohort, particularly schemata associated with ‘personalizing’ and ‘emotional reasoning’. We observed significantly increased levels of CDS across nearly all cognitive distortion types, sometimes more than twice as much, but did not find a statistically significant increase in prevalence among the D cohort for two specific types, namely ‘fortune-telling’ and ‘catastrophizing’. This may be due to the difficulty of capturing these specific cognitive distortions in the form of a set of 1–5-grams—their expression in language can involve an interactive process of conversation and interpretation. Notably, our findings are not explained by the use of FPPs or more negatively loaded language. These results shed a light on the degree to which depression-related language of cognitive distortions are manifested in the colloquial language of social media platforms. This is of social relevance given that these platforms are specifically designed to propagate information through the social ties that connect individuals on a global scale.

An advantage of studying theory-driven differences between the language of individuals with and without depression, in contrast to a purely data-driven or machine learning approach, is that we can explicitly use the principles underpinning CBT to understand the cognitive and lexical components that may shape depression. Cognitive behavioural therapists have developed a set of strategies to challenge the distorted thinking patterns that are characteristic of depression. Preliminary findings suggest that specific language can be related to specific therapeutic practices and seems to be related to outcomes48. However, these practices have been largely shaped by a clinical understanding and not necessarily informed by objective measures of how patterns of language reflect cognitive distortions, which could be harnessed to facilitate the path of recovery.

Our results suggest a path for mitigation and intervention, including applications that engage individuals with mood disorders, such as major depressive disorder, through social media platforms and that challenge particular expressions and types of depression-related language. Future characterization of the relationship between depression-related language and mood may help in the development of automated interventions (such as ‘chatbots’) or suggest promising targets for psychotherapy. Another approach that has shown promise in leveraging social media for the treatment of mental health problems involves crowdsourcing the responses to cognitively distorted content49. These types of applications have the potential to be more-scalable mental health interventions compared with existing approaches such as face-to-face psychotherapy50. The extent to which user CDS prevalence can be used as a passive index of vulnerability to depression that may be expected to change with treatment could also be explored. Insofar as online language can be considered to be an index of cognitive vulnerability to depression, a better understanding of online language may help to tailor treatments, especially internet-based treatments, to the more-specific needs of individuals. For example, interventions that target depression-related thinking and language may be well-suited for individuals with depression who express relatively higher levels of these distortions, whereas interventions that target other mechanisms (such as physical activity, circadian rhythm) may be better suited for individuals who do not show relatively higher levels of CDS. More research towards understanding differences in language patterns in depression and related disorders, such as anxiety disorders, is recommended. However, when implementing these types of approaches, ethical considerations and privacy issues have to be adequately addressed38,39.

Several limitations of our theory-driven approach should be considered. First, we relied on individuals reporting their personal clinical depression diagnoses on social media. Although we verified that the statement indeed pertains to a clinical diagnosis, we do not have verification of the diagnosis itself nor of its accuracy. This may introduce individuals into the D cohort who might not have been diagnosed with depression or accurately diagnosed. Vice versa, we have no verification that individuals in our random sample do not suffer from depression. However, the potential inaccuracy of this inclusion criterion will probably reduce the difference in depression rates between the two cohorts and, therefore, reduce the observed effect sizes (PR values between cohorts) due to the larger heterogeneity of our sample. As a consequence, our results are probably not an artefact of the accuracy of our inclusion criterion. Second, our approach is limited to discovering only individuals who are willing to disclose their diagnosis on social media. As this might skew our D cohort to a subgroup of individuals suffering from depression, we recommend caution when generalizing our findings to the level of all individuals who have depression. Third, our lexicon of CDS was composed and approved by a panel of ten experts who may have been only partially successful in capturing all of the n-grams used to express distorted ways of thinking. On a related note, the use of CDS n-grams implies that we measure distorted thinking by proxy, namely through language, and our observations may be therefore be affected by linguistic and cultural factors. Common idiosyncratic or idiomatic expressions may syntactically represent a distorted form of thinking, but no longer do so in practice. For example, an expression such as ‘literally the worst’ may be commonly used to express dismay, without necessarily involving the speaker experiencing a distorted mode of thinking. Thus, the presence of a CDS does not point to a cognitive distortion per se. Fourth, both cohorts were sampled from Twitter, one of the leading social media platforms, the use of which may be associated with higher levels of psychopathology and reduced well-being51,52,53. We may therefore be observing increased or biased rates of distorted thinking in both cohorts as a result of platform effects. However, we report relative prevalence numbers with respect to a carefully construed random sample also taken from Twitter, which probably compensates for this effect and the effect that individuals with depression might be more active than their random counterparts. Furthermore, recent analysis indicates that representative samples with respect to psychological phenomena can be obtained from social media content54. This is an important discussion in computational social science that will continue to be investigated. Data-driven approaches that analyse natural language in real-time will continue to complement theory-driven work such as ours.

As we analysed individuals on the basis of inferred health-related information, we want to stress some additional considerations regarding ethical research practices and data privacy30,38,39. We limited our investigation strictly to comparing, in the aggregate, the publicly shared language of two deidentified cohorts of individuals (individuals who report that they have been diagnosed with depression and a random sample). We carefully deidentified all obtained data to protect user privacy and performed our analysis under the constraints of two IRB protocols (IU IRB Protocols 2010371843 and 1707249405). Whereas the outcomes of our analysis could contribute to a better understanding of depression as a mental health disorder, they could also inform approaches that detect traces of mental health issues in the online language of individuals, and as such contribute to future detection, diagnostics and intervention efforts. This may raise important ethical and user privacy concerns as well as risk of harm, including but not limited to the right to privacy, data ownership and transparency. For example, even though social media data are technically public, individuals do not necessarily realize nor consent to particular retrospective analysis when they share information on their public accounts55 nor can they consent to how these data may be leveraged in future approaches that may involve individualized interactions and inventions. Considering existing evidence that individuals are more willing to share biomedical data than social media data56, in future research, we hope to reach a larger sample of individuals who understand public data availability and increase transparency through a carefully managed consent process. We acknowledge that these considerations are part of an active and ongoing discussion in our community that we encourage and that we hope our research may contribute to.

We emphasize that not all use of CDS n-grams reflects depressive thinking, as these phrases are part of normal English usage, and it would therefore be wrong to try to diagnose depression merely on the basis of use of one or more such phrases. Such an approach would, as well as being inaccurate, potentially lead to harm in terms of stigmatizing individuals.