Identifying Insomnia From Social Media Posts: Psycholinguistic Analyses of User Tweets

Background Many people suffer from insomnia, a sleep disorder characterized by difficulty falling and staying asleep during the night. As social media have become a ubiquitous platform to share users’ thoughts, opinions, activities, and preferences with their friends and acquaintances, the shared content across these platforms can be used to diagnose different health problems, including insomnia. Only a few recent studies have examined the prediction of insomnia from Twitter data, and we found research gaps in predicting insomnia from word usage patterns and correlations between users’ insomnia and their Big 5 personality traits as derived from social media interactions. Objective The purpose of this study is to build an insomnia prediction model from users’ psycholinguistic patterns, including the elements of word usage, semantics, and their Big 5 personality traits as derived from tweets. Methods In this paper, we exploited both psycholinguistic and personality traits derived from tweets to identify insomnia patients. First, we built psycholinguistic profiles of the users from their word choices and the semantic relationships between the words of their tweets. We then determined the relationship between a users’ personality traits and insomnia. Finally, we built a double-weighted ensemble classification model to predict insomnia from both psycholinguistic and personality traits as derived from user tweets. Results Our classification model showed strong prediction potential (78.8%) to predict insomnia from tweets. As insomniacs are generally ill-tempered and feel more stress and mental exhaustion, we observed significant correlations of certain word usage patterns among them. They tend to use negative words (eg, “no,” “not,” “never”). Some people frequently use swear words (eg, “damn,” “piss,” “fuck”) with strong temperament. They also use anxious (eg, “worried,” “fearful,” “nervous”) and sad (eg, “crying,” “grief,” “sad”) words in their tweets. We also found that the users with high neuroticism and conscientiousness scores for the Big 5 personality traits likely have strong correlations with insomnia. Additionally, we observed that users with high conscientiousness scores have strong correlations with insomnia patterns, while negative correlation between extraversion and insomnia was also found. Conclusions Our model can help predict insomnia from users’ social media interactions. Thus, incorporating our model into a software system can help family members detect insomnia problems in individuals before they become worse. The software system can also help doctors to diagnose possible insomnia in patients.

Relationships between the Big-5 Model and Effectuation versus Causation Logics of Entrepreneurs in New Ventures: The Estonian IT Sector

This study aims to investigate the influence of the big-5 personality traits on causation and effectuation decision-making logics using the entrepreneurial process theory. This is an empirical study based on 113 surveys of managers/entrepreneurs from the Estonian IT sector. The questionnaire was uploaded onto the online platform of connect.ee and the participants were invited to complete it. The study reveals that only conscientiousness positively and significantly predicts causation logic. However, four of the five factors of personality traits positively and significantly predict effectuation logic, i.e., openness, conscientiousness, extraversion, agreeableness, except for neuroticism. Furthermore, only nine of 25 hypotheses are positive and significant, and of the 11 relationships of the model, three are negative and non-significant for causation logic and eight are negative and non-significant for effectuation logic. Finally, there are another five relationships of the model which are positively but non-significantly related. Managers of IT companies in Estonia should think of shifting to effectuation logic as most personality traits predict effectuation logic, and therefore, there is the possibility of a better performance for IT firms. Eight of the 51 items of the model had standardised regression weights below the threshold of 0.500, but only four were extracted from the final model. The extraction of items from the model indicates the need for the re-identification of the constructs of personality traits using, for example, the six-factor personality traits.

DO BIG-5 PERSONALITY TRAITS CONTRIBUTE TO EMPLOYEES PERFORMANCE? AN EMPIRICAL EVIDENCE FROM HEIs IN PAKISTAN

Several factors influence the employee's personality including psycho-social factors. Previously studies have conducted to investigate the influence of Big-5 traits which impact employee’s performance. This study investigated the influence of Big-5 on the employee’s performance. The study used a cross-sectional survey a 5-point Likert scale was distributed among 163 samples selected randomly. The findings report a significant relationship between the predictors and a criterion variable. The study points those two predictors i.e., openness to experience and emotional control predict 57% variance in criterion variable as compared to the extravert, agreeableness, and conscientiousness. This study concludes that teacher’s centric policies & mechanisms enhance employee trust and confidence and it overcomes the apprehensions, as result, they perform better and contribute more towards the promotion of education and research in higher educational institutions.

Metacognitive Beliefs Uniquely Contribute to Interpersonal Problems: A Test Controlling for Adult Attachment, Big-5 Personality Traits, Anxiety, and Depression

Interpersonal difficulties are common across psychological disorders and are a legitimate target of treatment. Psychotherapeutic models differ in their understanding of interpersonal problems and how these problems are formulated and treated. It has been suggested that they are both the cause and effect of emotional distress symptoms, that they result from early attachment experiences, and that they are related to personality dimensions. However, the metacognitive model of psychopathology predicts that emotion disorder symptoms and interpersonal problems are linked to a common set of factors involving dysfunctional metacognition. In support of this view, metacognitive therapy has substantially reduced interpersonal problems in patients with anxiety and depression even though interpersonal problems are not directly targeted, indicating a role for metacognitive change. Nevertheless, the relationship between interpersonal problems and metacognitive beliefs remains underexplored, and the statistical control of emotion symptoms, personality, and attachment is important in substantiating any metacognition effects. The aim of the present study was therefore to test metacognitive beliefs as statistical predictors of interpersonal problems while controlling for anxiety/depression, adult attachment, and the Big-5 personality dimensions. In a cross-sectional study, 296 participants completed a battery of self-report questionnaires. We found that positive- and negative-metacognitive beliefs, cognitive confidence, and cognitive self-consciousness accounted for significant and unique variance in interpersonal problems together with avoidant attachment and conscientiousness when the overlap between all predictors was controlled. These findings support the notion that metacognitive beliefs are relevant to interpersonal problems with the potential implication that metacognitive therapy could have particularly broad effects on both emotion disorder symptoms and interpersonal problems.

Feasibility of the Big 5—Jena eCS Protocol

Purpose The most common protocols in the initial diagnostic of acute ischemic stroke do not assess cardiogenic or aortic causes of embolism. These are usually evaluated later by transthoracic (TTE) or transesophageal (TEE) echocardiography. This study aimed to evaluate the feasibility of a diagnostic tool for thoracic cardiovascular thrombi according to the first experience with a new extended cardio-stroke protocol (Big 5—Jena eCS protocol) in acute stroke patients. Methods Retrospective analyses of the tomography scans database of the Jena University Hospital were performed. We included a total of 67 patients in the feasibility analyses, based on the evaluation of three outcomes. Results Primary outcome: the Big 5—Jena eCS protocol was able to detect thoracic cardiovascular thrombi in a total of 20 patients in different locations including the arch of the aorta, the aortic valve, the left atrium, the left atrial appendage, the left ventricle, and the pulmonary arteries. Secondary outcome: implementating the protocol did not result in a significant elevation of the radiation exposure compared to traditional protocols. Tertiary outcome: the new protocol identified seven cases that were considered negative by echocardiography. Conclusion The implementation of an extended cardio-stroke protocol is feasible, no significantly time-consuming, acquiring assessable imaging, and maintaining radiation exposure acceptable. The Big 5—Jena eCS protocol was also able to detect some thrombi not reported by TTE or TEE; however, due to our data’s explorative character, a conclusive comparison with cardiac ultrasound is not possible. A prospective pilot study and clinical trials should be conducted to assess the diagnostic accuracy of this protocol compared to echocardiography and determine the potential impact on diagnostic and treatment decisions.