Detecting Cognitive Distortions Through Machine Learning Text Analytics

Simms, Tara; Ramstedt, C.; Rich, Marshall S.; Richard’s, María Marta; Martinez, Tony; Giraud‐Carrier, Christophe

doi:10.1109/ichi.2017.39

Cited by 31 publications

(28 citation statements)

References 22 publications

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“…The ability to leverage powerful and efficient tools for analysis has led many researchers to deploy automated text analysis tools to examine online interactions (Donohue, Liang, & Druckman, 2014).We selected the extensively validated Linguistic Inquiry Word Count (LIWC) tool for analyzing our dataset (Fast, Chen, & Bernstein, 2017;Tausczik & Pennebaker, 2010) (pronounced "Luke"). This validated tool uses dictionaries to categorize and quantify language used in text and provides a calculation of the percentage of words within defined categories (Khazaei, Lu, & Mercer, 2017;Simms et al, 2017).…”

Section: )mentioning

confidence: 99%

“…LIWC then reports a value, expressed as a percentage, for those words. For example, a score of 8.3 for a post's cognitive processing means that 8.3 percent of the words used in the post were in the cognitive processing dictionary (Simms et al, 2017). This use of a top-down dictionary analysis approach allows for more consistent measurement of words, and the use of a validated tool allows for concurrent validity (Humphreys & Wang, 2018).…”

Section: )mentioning

confidence: 99%

“…Analytical thinking is one of four global high-level text properties in LIWC (Simms et al, 2017) and is one of the summary variables output by LIWC. This summary variable measures the degree to which a post contains words that demonstrate formal, logical, and hierarchical thinking patterns-something that is a component of cognitive presence (Tausczik & Pennebaker, 2010).…”

Section: Analytical Thinkingmentioning

confidence: 99%

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Setting the pace: examining cognitive processing in MOOC discussion forums with automatic text analysis

Moore

Oliver

Wang

2019

Interactive Learning Environments

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Learning analytics focuses on extracting meaning from large amounts of data. One of the largest datasets in education comes from Massive Open Online Courses (MOOCs) that typically feature enrollments in the tens of thousands. Analyzing MOOC discussion forums presents logistical issues, resulting chiefly from the size of the dataset, which can create challenges for understanding and adequately describing student behaviors. Utilizing automatic text analysis, this study built a hierarchical linear model that examines the influence of the pacing condition of a massive open online course (MOOC), whether it is self-paced or instructor-paced, on the demonstration of cognitive processing in a HarvardX MOOC. The analysis of 2,423 discussion posts generated by 671 students revealed the number of dictionary words used were positively associated with cognitive processing while analytical thinking and clout was negatively associated. We found that none of the student background information (gender, education), status of the course engagement (explored or completed), or the course pace (self-paced versus instructor paced) significantly influenced the cognitive processing of the postings.

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confidence: 99%

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confidence: 99%

Section: Analytical Thinkingmentioning

confidence: 99%

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Setting the pace: examining cognitive processing in MOOC discussion forums with automatic text analysis

Moore

Oliver

Wang

2019

Interactive Learning Environments

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“…Some examples include comparing the happiness of users to their online social networks 11,12 , identifying detailed predictors of mood through social media feeds 7 , predicting cognitive distortions expressed among groups at-risk of mental health disorders 13 , tracking the emotions of social media users at high resolution 14,15 , and mapping negative affectivity among users with internalizing disorders 16 . Collectively, these studies demonstrate the feasibility and value of using sentiment analysis on social media data to study societal mood and well-being, as well as biomedical signals among social media users that can provide useful proxies for mental health 13,[17][18][19] . In fact, these approaches may be especially useful considering the speed with which the pandemic became an acute socio-economic phenomenon, the pervasiveness of COVID-19 related content available online, and the natural reaction of many to post on social media about pandemic-related events.…”

Section: Introductionmentioning

confidence: 99%

Social Media Insights Into US Mental Health Amid the COVID-19 Pandemic. A Longitudinal Twitter Analysis (JANUARY-APRIL 2020)

Valdez

Thij

Bathina

et al. 2020

Preprint

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BackgroundThe COVID-19 pandemic led to unprecedented mitigation efforts that disrupted the daily lives of millions. Beyond the general health repercussions of the pandemic itself, these measures also present a significant challenge to the world’s mental health and healthcare systems. Considering traditional survey methods are time-consuming and expensive, we need timely and proactive data sources to respond to the rapidly evolving effects of health policy on our population’s mental health. Significant pluralities of the US population now use social media platforms, such as Twitter, to express the most minute details of their daily lives and social relations. This behavior is expected to increase during the COVID-19 pandemic, rendering social media data a rich field from which to understand personal wellbeing.PurposeBroadly, this study answers three research questions: RQ1: What themes emerge from a corpus of US tweets about COVID-19?; RQ2: To what extent does social media use increase during the onset of the COVID-19 pandemic?; and RQ3: Does sentiment change in response to the COVID-19 pandemic?MethodsWe analyzed 86,581,237 public domain English-language US tweets collected from an open-access public repository in three steps1. First, we characterized the evolution of hashtags over time using Latent Dirichlet Allocation (LDA) topic modeling. Second, we increased the granularity of this analysis by downloading Twitter timelines of a large cohort of individuals (n = 354,738) in 20 major US cities to assess changes in social media use. Finally, using this timeline data, we examined collective shifts in public mood in relation to evolving pandemic news cycles by analyzing the average daily sentiment of all timeline tweets with the Valence Aware Dictionary and sEntiment Reasoner (VADER) sentiment tool2.ResultsLDA topics generated in the early months of the dataset corresponded to major COVID-19 specific events. However, as state and municipal governments began issuing stay-at-home orders, latent themes shifted towards US-related lifestyle changes rather than global pandemic-related events. Social media volume also increased significantly, peaking during stay-at-home mandates. Finally, VADER sentiment analysis sentiment scores of user timelines were initially high and stable, but decreased significantly, and continuously, by late March.Discussion & ConclusionOur findings underscore the negative effects of the pandemic on overall population sentiment. Increased usage rates suggest that, for some, social media may be a coping mechanism to combat feelings of isolation related to long-term social distancing. However, in light of the documented negative effect of heavy social media usage on mental health, for many social media may further exacerbate negative feelings in the long-term. Thus, considering the overburdened US mental healthcare structure, these findings have important implications for ongoing mitigation efforts.

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“…Anxiety SVM [276], Linear discriminant analysis [276], RF [276] Electronic Health Records [276] Cognitive Distortions DT [277], Regression [277], NB [277], NN [277], kNN [277], RELIEF [277] Social Media [277] Dementia SVM [272] Electronic Health Records [272] Depression DT [278], Gradient boosting [279], kNN [278], LIWC [280], LDA [280], Linear discriminant analysis [276], NB [278], NN [274], RF [276], Regression [274], SVM [276,278] Electronic Health Records [276], Social Media [278,280], Survey [274,279] Grief LIWC [266], SVM [266] Social Media [266] Mental Health Service Usage…”

Section: Technique(s) Data Typementioning

confidence: 99%

Machine learning in mental health: A systematic scoping review of methods and applications

Shatte¹,

Hutchinson²,

Teague³

2018

Preprint

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ObjectiveThis paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice. Materials and MethodsEight health and information technology research databases were searched using the terms "big data" or "machine learning" and "mental health". Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type and size, and study results. Articles were then synthesised via narrative review. ResultsThree hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis; (ii) prognosis, treatment and support; (iii) public health; and, (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's Disease. ML techniques used included support vector machines, decision trees, neural networks, latent dirichlet allocation, and clustering. Discussion and ConclusionOverall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration.With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to improve other areas of psychological functioning. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field. BACKGROUND AND SIGNIFICANCE

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Detecting Cognitive Distortions Through Machine Learning Text Analytics

Cited by 31 publications

References 22 publications

Setting the pace: examining cognitive processing in MOOC discussion forums with automatic text analysis

Setting the pace: examining cognitive processing in MOOC discussion forums with automatic text analysis

Social Media Insights Into US Mental Health Amid the COVID-19 Pandemic. A Longitudinal Twitter Analysis (JANUARY-APRIL 2020)

Machine learning in mental health: A systematic scoping review of methods and applications

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