scholarly journals An Explainable Approach Based on Emotion and Sentiment Features for Detecting People with Mental Disorders on Social Networks

2021 ◽  
Vol 11 (22) ◽  
pp. 10932
Author(s):  
Leslie Marjorie Gallegos Salazar ◽  
Octavio Loyola-González ◽  
Miguel Angel Medina-Pérez
Keyword(s):  
Social Media ◽  
Mental Disorders ◽  
Regression Models ◽  
Language Use ◽  
Data Representation ◽  
Feature Representation ◽  
Science Field ◽  
Logistic Regression Models ◽  
Contrast Pattern ◽  
Statistical Results

Mental disorders are a global problem that widely affects different segments of the population. Diagnosis and treatment are difficult to obtain, as there are not enough specialists on the matter, and mental health is not yet a common topic among the population. The computer science field has proposed some solutions to detect the risk of depression, based on language use and data obtained through social media. These solutions are mainly focused on objective features, such as n-grams and lexicons, which are complicated to be understood by experts in the application area. Hence, in this paper, we propose a contrast pattern-based classifier to detect depression by using a new data representation based only on emotion and sentiment analysis extracted from posts on social media. Our proposed feature representation contains 28 different features, which are more understandable by specialists than other proposed representations. Our feature representation jointly with a contrast pattern-based classifier has obtained better classification results than five other combinations of features and classifiers reported in the literature. Our proposal statistically outperformed the Random Forest, Naive Bayes, and AdaBoost classifiers using the parser-tree, VAD (Valence, Arousal, and Dominance) and Topics, and Bag of Words (BOW) representations. It obtained similar statistical results to the logistic regression models using the Ensemble of BOWs and Handcrafted features representations. In all cases, our proposal was able to provide an explanation close to the language of experts, due to the mined contrast patterns.

Emotional talk about robotic technologies on Reddit: Sentiment analysis of life domains, motives, and temporal themes

2021 ◽  
pp. 146144482110672
Author(s):  
Nina Savela ◽  
David Garcia ◽  
Max Pellert ◽  
Atte Oksanen
Keyword(s):  
Social Sciences ◽  
Social Media ◽  
Text Analysis ◽  
Regression Models ◽  
Life Domains ◽  
Robotic Technology ◽  
Logistic Regression Models ◽  
Negative Comments ◽  
Media Platform ◽  
Robotic Technologies

This study grounded on computational social sciences and social psychology investigated sentiment and life domains, motivational, and temporal themes in social media discussions about robotic technologies. We retrieved text comments from the Reddit social media platform in March 2019 based on the following six robotic technology concepts: robot ( N = 3,433,554), AI ( N = 2,821,614), automation ( N = 879,092), bot ( N = 21,559,939), intelligent agent ( N = 15,119), and software agent ( N = 18,324). The comments were processed using VADER and LIWC text analysis tools and analyzed further with logistic regression models. Compared to the other four concepts, robot and AI were used less often in positive context. Comments addressing themes of leisure, money, and future were associated with positive and home, power, and past with negative comments. The results show how the context and terminology affect the emotionality in robotic technology conversations.

Mental health and academic failure among first-year university students in South Africa

2020 ◽  
pp. 008124632096320
Author(s):  
Jason Bantjes ◽  
Wylene Saal ◽  
Franco Gericke ◽  
Christine Lochner ◽  
Janine Roos ◽  
...  
Keyword(s):  
Mental Disorders ◽  
Regression Models ◽  
Common Mental Disorders ◽  
Population Attributable Risk ◽  
Academic Failure ◽  
Sociodemographic Factors ◽  
First Year ◽  
Major Depressive ◽  
Logistic Regression Models ◽  
Hyperactivity Disorder

Universities in South Africa face ongoing challenges with low rates of academic attainment and high rates of attrition. Our aims were to (1) investigate the extent to which common mental disorders evaluated early in the first year predict academic failure at the end of the year, controlling for sociodemographic factors and (2) establish the potential reduction in prevalence of failure that could be achieved by effectively treating associated mental disorders. Self-report data were collected from first-year students ( n = 1402) via an online survey at the end of the first semester. Participants were assessed for six common mental disorders. Academic performance data were subsequently obtained from institutional records at the end of the year. Bivariate and multivariate logistic regression models were used to identify the best sociodemographic and mental health predictors of academic failure. Population attributable risk analysis was used to assess the potential impact of treating associated mental disorders. In multivariate logistic regression models controlling for significant sociodemographic factors, the odds of failure were elevated among students with major depressive disorder (aOR = 3.69) and attention deficit hyperactivity disorder (aOR = 2.05). Population attributable risk analysis suggests that providing effective treatment to students with major depressive disorder and/or attention deficit hyperactivity disorder could yield a 6.5% absolute reduction in prevalence of academic failure (equivalent to a 23.0% proportional reduction in prevalence of academic failure). Providing effective and accessible campus-based mental healthcare services is integral to supporting students’ academic attainment and promoting transformation at South African universities.

scholarly journals Recent Social Trends Among Romanian Twitter Users

2019 ◽  
Vol 1 (1) ◽  
pp. 257-263
Author(s):  
Alexandru-Răzvan Florea
Keyword(s):  
Social Media ◽  
Online Social Networks ◽  
Regression Models ◽  
Data Sets ◽  
Proof Of Concept ◽  
Social Media Data ◽  
Logistic Regression Models ◽  
Social Trends ◽  
Twitter Users ◽  
Media Data

Abstract Online Social Networks have become a significant part of our quotidian life. In this paper, we aim to provide a proof of concept of how social media data can be effectively extracted, processed and analyzed with powerful open source tools like R. Moreover, we aim to build a reliable methodology for testing and validating social trends by using social media data. We used API routines to establish the connection between R and Twitter, Deep Learning Models to estimate the demographics of the users, Logistic Regression Models to estimate the predispositions of the users, and Propensity Score Matching to build comparable data sets. After analyzing the Romanian Twitter users, the results of our inquiry show that most of them are relatively young and the percentage of males is significantly higher than the percentage of females. Moreover, our results confirm that facial appearances play an essential role in the popularity of an individual.

scholarly journals Procrastination on social media: predictors of types, triggers and acceptance of countermeasures

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Abdulaziz Alblwi ◽  
John McAlaney ◽  
Dena Ahmed S. Al Thani ◽  
Keith Phalp ◽  
Raian Ali
Keyword(s):  
Social Media ◽  
Social Networking ◽  
Social Networking Sites ◽  
Regression Models ◽  
Binary Logistic Regression ◽  
Self Control ◽  
Negative Consequences ◽  
Logistic Regression Models ◽  
As Stress ◽  
The Uk

AbstractProcrastination refers to the voluntary delay of urgent tasks and can have several negative consequences such as stress, health issues and academic under-achievement. Several factors including personality, culture and gender have been identified as predictors of procrastination, although there are some conflicting findings within the literature. Social networking sites have been identified as a possible facilitator of procrastination, in part due to their design features that encourage immersion and continual interaction. However, social networking sites also provide the opportunity for intelligent, real-time prevention and intervention strategies to be delivered that can reduce the experience of procrastination. In this paper, we build upon our research in which we used a mixed-method approach to explore the types, triggers and acceptance of countermeasures for procrastination on social media. Following a survey of 288 participants from the UK (n = 165) and the Kingdom of Saudi Arabia (n = 123), we conducted a series of multiple regression and binary logistic regression models to determine predictors of these factors. Several predictors such as self-control and conscientiousness were found to be significant predictors, but overall, the amount of variance explained by the regression models was relatively low. The results demonstrate that participants are receptive to countermeasures for procrastination being delivered through social networking sites but suggest that the predictors of procrastination related phenomena experienced in social networking sites are different than in offline settings.

The impact of SPY angiography on intraoperative decision making and outcomes for post-mastectomy reconstruction

2019 ◽  
pp. 1-5
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Skin Necrosis ◽  
Regression Models ◽  
Multivariable Analysis ◽  
P Value ◽  
Flap Necrosis ◽  
Single Institution ◽  
Logistic Regression Models ◽  
The Impact

Objective: While the use of intraoperative laser angiography (SPY) is increasing in mastectomy patients, its impact in the operating room to change the type of reconstruction performed has not been well described. The purpose of this study is to investigate whether SPY angiography influences post-mastectomy reconstruction decisions and outcomes. Methods and materials: A retrospective analysis of mastectomy patients with reconstruction at a single institution was performed from 2015-2017.All patients underwent intraoperative SPY after mastectomy but prior to reconstruction. SPY results were defined as ‘good’, ‘questionable’, ‘bad’, or ‘had skin excised’. Complications within 60 days of surgery were compared between those whose SPY results did not change the type of reconstruction done versus those who did. Preoperative and intraoperative variables were entered into multivariable logistic regression models if significant at the univariate level. A p-value <0.05 was considered significant. Results: 267 mastectomies were identified, 42 underwent a change in the type of planned reconstruction due to intraoperative SPY results. Of the 42 breasts that underwent a change in reconstruction, 6 had a ‘good’ SPY result, 10 ‘questionable’, 25 ‘bad’, and 2 ‘had areas excised’ (p<0.01). After multivariable analysis, predictors of skin necrosis included patients with ‘questionable’ SPY results (p<0.01, OR: 8.1, 95%CI: 2.06 – 32.2) and smokers (p<0.01, OR:5.7, 95%CI: 1.5 – 21.2). Predictors of any complication included a change in reconstruction (p<0.05, OR:4.5, 95%CI: 1.4-14.9) and ‘questionable’ SPY result (p<0.01, OR: 4.4, 95%CI: 1.6-14.9). Conclusion: SPY angiography results strongly influence intraoperative surgical decisions regarding the type of reconstruction performed. Patients most at risk for flap necrosis and complication post-mastectomy are those with questionable SPY results.

Utilizing Twitter Data Analysis and Deep Learning to Identify Drug Use (Preprint)

2019 ◽  
Author(s):  
Joseph Tassone ◽  
Peizhi Yan ◽  
Mackenzie Simpson ◽  
Chetan Mendhe ◽  
Vijay Mago ◽  
...  
Keyword(s):  
Social Media ◽  
Logistic Regression ◽  
Deep Learning ◽  
Decision Tree ◽  
Semantic Meaning ◽  
Predictive Capability ◽  
Logistic Regression Models ◽  
Twitter Data ◽  
Data Points ◽  
Positive Classification

BACKGROUND The collection and examination of social media has become a useful mechanism for studying the mental activity and behavior tendencies of users. OBJECTIVE Through the analysis of a collected set of Twitter data, a model will be developed for predicting positively referenced, drug-related tweets. From this, trends and correlations can be determined. METHODS Twitter social media tweets and attribute data were collected and processed using topic pertaining keywords, such as drug slang and use-conditions (methods of drug consumption). Potential candidates were preprocessed resulting in a dataset 3,696,150 rows. The predictive classification power of multiple methods was compared including regression, decision trees, and CNN-based classifiers. For the latter, a deep learning approach was implemented to screen and analyze the semantic meaning of the tweets. RESULTS The logistic regression and decision tree models utilized 12,142 data points for training and 1041 data points for testing. The results calculated from the logistic regression models respectively displayed an accuracy of 54.56% and 57.44%, and an AUC of 0.58. While an improvement, the decision tree concluded with an accuracy of 63.40% and an AUC of 0.68. All these values implied a low predictive capability with little to no discrimination. Conversely, the CNN-based classifiers presented a heavy improvement, between the two models tested. The first was trained with 2,661 manually labeled samples, while the other included synthetically generated tweets culminating in 12,142 samples. The accuracy scores were 76.35% and 82.31%, with an AUC of 0.90 and 0.91. Using association rule mining in conjunction with the CNN-based classifier showed a high likelihood for keywords such as “smoke”, “cocaine”, and “marijuana” triggering a drug-positive classification. CONCLUSIONS Predictive analysis without a CNN is limited and possibly fruitless. Attribute-based models presented little predictive capability and were not suitable for analyzing this type of data. The semantic meaning of the tweets needed to be utilized, giving the CNN-based classifier an advantage over other solutions. Additionally, commonly mentioned drugs had a level of correspondence with frequently used illicit substances, proving the practical usefulness of this system. Lastly, the synthetically generated set provided increased scores, improving the predictive capability. CLINICALTRIAL None

Demographic factors associated with missed follow-up among solid tumor patients treated at a large multi-site academic institution

2020 ◽  
Vol 16 (32) ◽  
pp. 2635-2643
Author(s):  
Samantha L Freije ◽  
Jordan A Holmes ◽  
Saleh Rachidi ◽  
Susannah G Ellsworth ◽  
Richard C Zellars ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Odds Ratio ◽  
Regression Models ◽  
Tumor Patients ◽  
Logistic Regression Models ◽  
Factors Associated ◽  
Follow Up Care ◽  
Risk Patients ◽  
African American Race

Aim: To identify demographic predictors of patients who miss oncology follow-up, considering that missed follow-up has not been well studies in cancer patients. Methods: Patients with solid tumors diagnosed from 2007 to 2016 were analyzed (n = 16,080). Univariate and multivariable logistic regression models were constructed to examine predictors of missed follow-up. Results: Our study revealed that 21.2% of patients missed ≥1 follow-up appointment. African–American race (odds ratio [OR] 1.33; 95% CI: 1.17–1.51), Medicaid insurance (OR 1.59; 1.36–1.87), no insurance (OR 1.66; 1.32–2.10) and rural residence (OR 1.78; 1.49–2.13) were associated with missed follow-up. Conclusion: Many cancer patients miss follow-up, and inadequate follow-up may influence cancer outcomes. Further research is needed on how to address disparities in follow-up care in high-risk patients.

scholarly journals COVID-19 Preventive Behaviours in Cameroon: A Six-Month Online National Survey

2021 ◽  
Vol 18 (5) ◽  
pp. 2554
Author(s):  
Joseph Nelson Siewe Fodjo ◽  
Leonard Ngarka ◽  
Wepnyu Y. Njamnshi ◽  
Leonard N. Nfor ◽  
Michel K. Mengnjo ◽  
...  
Keyword(s):  
Regression Models ◽  
Health Personnel ◽  
Online Survey ◽  
Preventive Measures ◽  
Face Mask ◽  
Logistic Regression Models ◽  
Adherence Score ◽  
Preventive Behaviour ◽  
Predictors Of Adherence

Since March 2020, the Cameroonian government implemented nationwide measures to stall COVID-19 transmission. However, little is known about how well these unprecedented measures are being observed as the pandemic evolves. We conducted a six-month online survey to assess the preventive behaviour of Cameroonian adults during the COVID-19 outbreak. A five-point adherence score was constructed based on self-reported observance of the following preventive measures: physical distancing, face mask use, hand hygiene, not touching one’s face, and covering the mouth when coughing or sneezing. Predictors of adherence were investigated using ordinal logistic regression models. Of the 7381 responses received from all ten regions, 73.3% were from male respondents and overall mean age was 32.8 ± 10.8 years. Overall mean adherence score was 3.96 ± 1.11 on a scale of 0–5. Mean weekly adherence scores were initially high, but gradually decreased over time accompanied by increasing incidence of COVID-19 during the last study weeks. Predictors for higher adherence included higher age, receiving COVID-19 information from health personnel, and agreeing with the necessity of lockdown measures. Meanwhile, experiencing flu-like symptoms was associated with poor adherence. Continuous observance of preventive measures should be encouraged among Cameroonians in the medium- to long-term to avoid a resurgence in COVID-19 infections.

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