Anxiety and Depression are Associated with More Distorted Thinking on Social Media: A Longitudinal Multi-Method Study.

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Title: Anxiety and Depression are Associated with More Distorted Thinking on Social Media: A Longitudinal Multi-Method Study.
Authors: A. Rutter, Lauren (AUTHOR), Edinger, Andy (AUTHOR), Lorenzo-Luaces, Lorenzo (AUTHOR), ten Thij, Marijn (AUTHOR), Valdez, Danny (AUTHOR), Bollen, Johan (AUTHOR)
Source: Cognitive Therapy & Research. Aug2025, Vol. 49 Issue 4, p712-720. 9p.
Subjects: Anxiety, Mental depression, Social media, Longitudinal method, Cognitive therapy, Mental health, Content analysis, Cognitive bias
Abstract: Background: Depression and anxiety are associated with patterns of negative thinking that can be targeted through cognitive restructuring as a part of cognitive therapy (CT) or cognitive behavioral therapy (CBT). Our team has created a set of cognitive distortion schemata (CDS) n-grams based on theories underlying CT to measure the linguistic markers that indicate cognitive vulnerability to depression. These CDS were specifically designed to examine online language. Our prior work supports a relationship between CDS and a diagnosis of depression, but less is known about the relationship between online language, CDS, and anxiety. The current study measures if CDS can be detected in people who report anxiety symptoms, and whether CDS increase with symptom severity. Methods: 1,377 participants were recruited from a study assessing social media use and mental health symptoms, the Studies of Online Cohorts of Internalizing Symptoms and Language (SOCIAL). From this, 804 timelines were harvested, and after removing missing data and bots, our final sample was 537 respondents who posted 999,859 tweets. This is a longitudinal, multi-method design, using surveys and text-based analysis of social media timelines. We used bootstrap resampling to compare differences in CDS prevalence in anxious and depressed participants. Results: CDS can be observed in anxiety disorders, significantly increase as a function of anxiety symptom severity, and are related to depression and anxiety comorbidity. Conclusions: Using behavioral, affective, and cognitive indicators of distorted thinking from social media may yield new insight into the trajectories of depression and anxiety. This work has implications for the future of CT/CBT and other online interventions that target distorted thinking styles. [ABSTRACT FROM AUTHOR]
Copyright of Cognitive Therapy & Research is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Anxiety and Depression are Associated with More Distorted Thinking on Social Media: A Longitudinal Multi-Method Study.
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  Data: <searchLink fieldCode="JN" term="%22Cognitive+Therapy+%26+Research%22">Cognitive Therapy & Research</searchLink>. Aug2025, Vol. 49 Issue 4, p712-720. 9p.
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  Data: <searchLink fieldCode="DE" term="%22Anxiety%22">Anxiety</searchLink><br /><searchLink fieldCode="DE" term="%22Mental+depression%22">Mental depression</searchLink><br /><searchLink fieldCode="DE" term="%22Social+media%22">Social media</searchLink><br /><searchLink fieldCode="DE" term="%22Longitudinal+method%22">Longitudinal method</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+therapy%22">Cognitive therapy</searchLink><br /><searchLink fieldCode="DE" term="%22Mental+health%22">Mental health</searchLink><br /><searchLink fieldCode="DE" term="%22Content+analysis%22">Content analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+bias%22">Cognitive bias</searchLink>
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  Data: Background: Depression and anxiety are associated with patterns of negative thinking that can be targeted through cognitive restructuring as a part of cognitive therapy (CT) or cognitive behavioral therapy (CBT). Our team has created a set of cognitive distortion schemata (CDS) n-grams based on theories underlying CT to measure the linguistic markers that indicate cognitive vulnerability to depression. These CDS were specifically designed to examine online language. Our prior work supports a relationship between CDS and a diagnosis of depression, but less is known about the relationship between online language, CDS, and anxiety. The current study measures if CDS can be detected in people who report anxiety symptoms, and whether CDS increase with symptom severity. Methods: 1,377 participants were recruited from a study assessing social media use and mental health symptoms, the Studies of Online Cohorts of Internalizing Symptoms and Language (SOCIAL). From this, 804 timelines were harvested, and after removing missing data and bots, our final sample was 537 respondents who posted 999,859 tweets. This is a longitudinal, multi-method design, using surveys and text-based analysis of social media timelines. We used bootstrap resampling to compare differences in CDS prevalence in anxious and depressed participants. Results: CDS can be observed in anxiety disorders, significantly increase as a function of anxiety symptom severity, and are related to depression and anxiety comorbidity. Conclusions: Using behavioral, affective, and cognitive indicators of distorted thinking from social media may yield new insight into the trajectories of depression and anxiety. This work has implications for the future of CT/CBT and other online interventions that target distorted thinking styles. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Cognitive Therapy & Research is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Aug2025
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