scholarly journals Automatic Construction of a Depression-Domain Lexicon Based on Microblogs: Text Mining Study

10.2196/17650 ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. e17650
Author(s):  
Genghao Li ◽  
Bing Li ◽  
Langlin Huang ◽  
Sibing Hou
Keyword(s):  
Social Media ◽  
Social Networking Sites ◽  
Label Propagation ◽  
World Health ◽  
Support Vector ◽  
Depression Detection ◽  
Text Information ◽  
The Rich ◽  
Health Organization ◽  
Future Work

Background According to a World Health Organization report in 2017, there was almost one patient with depression among every 20 people in China. However, the diagnosis of depression is usually difficult in terms of clinical detection owing to slow observation, high cost, and patient resistance. Meanwhile, with the rapid emergence of social networking sites, people tend to share their daily life and disclose inner feelings online frequently, making it possible to effectively identify mental conditions using the rich text information. There are many achievements regarding an English web-based corpus, but for research in China so far, the extraction of language features from web-related depression signals is still in a relatively primary stage. Objective The purpose of this study was to propose an effective approach for constructing a depression-domain lexicon. This lexicon will contain language features that could help identify social media users who potentially have depression. Our study also compared the performance of detection with and without our lexicon. Methods We autoconstructed a depression-domain lexicon using Word2Vec, a semantic relationship graph, and the label propagation algorithm. These two methods combined performed well in a specific corpus during construction. The lexicon was obtained based on 111,052 Weibo microblogs from 1868 users who were depressed or nondepressed. During depression detection, we considered six features, and we used five classification methods to test the detection performance. Results The experiment results showed that in terms of the F1 value, our autoconstruction method performed 1% to 6% better than baseline approaches and was more effective and steadier. When applied to detection models like logistic regression and support vector machine, our lexicon helped the models outperform by 2% to 9% and was able to improve the final accuracy of potential depression detection. Conclusions Our depression-domain lexicon was proven to be a meaningful input for classification algorithms, providing linguistic insights on the depressive status of test subjects. We believe that this lexicon will enhance early depression detection in people on social media. Future work will need to be carried out on a larger corpus and with more complex methods.

scholarly journals Automatic Construction of a Depression-Domain Lexicon Based on Microblogs: Text Mining Study (Preprint)

2019 ◽  
Author(s):  
Genghao Li ◽  
Bing Li ◽  
Langlin Huang ◽  
Sibing Hou
Keyword(s):  
Social Media ◽  
Social Networking Sites ◽  
Label Propagation ◽  
World Health ◽  
Support Vector ◽  
Depression Detection ◽  
Text Information ◽  
The Rich ◽  
Health Organization ◽  
Future Work

BACKGROUND According to a World Health Organization report in 2017, there was almost one patient with depression among every 20 people in China. However, the diagnosis of depression is usually difficult in terms of clinical detection owing to slow observation, high cost, and patient resistance. Meanwhile, with the rapid emergence of social networking sites, people tend to share their daily life and disclose inner feelings online frequently, making it possible to effectively identify mental conditions using the rich text information. There are many achievements regarding an English web-based corpus, but for research in China so far, the extraction of language features from web-related depression signals is still in a relatively primary stage. OBJECTIVE The purpose of this study was to propose an effective approach for constructing a depression-domain lexicon. This lexicon will contain language features that could help identify social media users who potentially have depression. Our study also compared the performance of detection with and without our lexicon. METHODS We autoconstructed a depression-domain lexicon using Word2Vec, a semantic relationship graph, and the label propagation algorithm. These two methods combined performed well in a specific corpus during construction. The lexicon was obtained based on 111,052 Weibo microblogs from 1868 users who were depressed or nondepressed. During depression detection, we considered six features, and we used five classification methods to test the detection performance. RESULTS The experiment results showed that in terms of the F1 value, our autoconstruction method performed 1% to 6% better than baseline approaches and was more effective and steadier. When applied to detection models like logistic regression and support vector machine, our lexicon helped the models outperform by 2% to 9% and was able to improve the final accuracy of potential depression detection. CONCLUSIONS Our depression-domain lexicon was proven to be a meaningful input for classification algorithms, providing linguistic insights on the depressive status of test subjects. We believe that this lexicon will enhance early depression detection in people on social media. Future work will need to be carried out on a larger corpus and with more complex methods.

The evolution of rumors on a closed platform during COVID-19 (Preprint)

2021 ◽  
Author(s):  
Andrea Wen-Yi Wang ◽  
Jo-Yu Lan ◽  
Ming-Hung Wang ◽  
Chihhao Yu
Keyword(s):  
Social Media ◽  
World Health Organization ◽  
Psychological Health ◽  
Instant Messaging ◽  
Medical Personnel ◽  
Text Messages ◽  
World Health ◽  
The World ◽  
Physical And Psychological Health ◽  
Health Organization

BACKGROUND In 2020, the COVID-19 pandemic put the world in crisis on both physical and psychological health. Simultaneously, a myriad of unverified information flowed on social media and online outlets. The situation was so severe that the World Health Organization identified it an infodemic on February 2020. OBJECTIVE We want to study the propagation patterns and textual transformation of COVID-19 related rumors on a closed-platform. METHODS We obtained a dataset of 114 thousand suspicious text messages collected on Taiwan’s most popular instant messaging platform, LINE. We also proposed an algorithm that efficiently cluster text messages into groups, where each group contains text messages within limited difference in content. Each group then represents a rumor and elements in each group is a message about the rumor. RESULTS 114 thousand messages were separated into 937 groups with at least 10 elements. Of the 936 rumors, 44.5% (417) were related to COVID-19. By studying 3 popular false COVID-19 rumors, we identified that key authoritative figures, mostly medical personnel, were often quoted in the messages. Also, rumors resurfaced multiple times after being fact-checked, and the resurfacing pattern were influenced by major societal events and successful content alterations, such as changing whom to quote in a message. CONCLUSIONS To fight infodemic, it is crucial that we first understand why and how a rumor becomes popular. While social media gives rise to unprecedented number of unverified rumors, it also provides a unique opportunity for us to study rumor propagations and the interactions with society. Therefore, we must put more effort in the areas.

scholarly journals Practices suggested in social media for birth plans

2020 ◽  
Vol 73 (5) ◽  
Author(s):  
Victória Prates Pasqualotto ◽  
Mariene Jaeger Riffel ◽  
Virgínia Leismann Moretto
Keyword(s):  
Social Media ◽  
World Health Organization ◽  
Thematic Analysis ◽  
Descriptive Study ◽  
World Health ◽  
Production Of Knowledge ◽  
The World ◽  
Means Of Transmission ◽  
Health Organization ◽  
E Mail

ABSTRACT Objective: To describe and analyze the practices suggested in social media for the elaboration of Birth Plans, available on Blogs/Sites and not included in the WHO recommendations. Method: Qualitative, exploratory, descriptive study with thematic analysis. A total of 41 e-mail addresses were selected for analysis among the 200 web addresses previously identified between March and July 2016. Three web addresses were in Portugal and the others in Brazil. Results: 48 practices not included in the recommendations of the World Health Organization (WHO) were identified. Conclusion: Blogs/Websites, as means of transmission, circulation and production of knowledge, enable the horizontal expression of values, encourage women to plan the events considered important for their deliveries and put childbirth decisions on the hands of women, which has caused controversy in the discourse of humanization of childbirth.

scholarly journals Knowledge, Attitude, and Perception of COVID-19 Pandemic in Enugu, Southeast Nigeria- The Initial Experience

2021 ◽  
pp. 27-37
Author(s):  
E. K. Mgbe ◽  
C. G. Mgbe ◽  
S. N. Ezeofor ◽  
J. F. Etiki
Keyword(s):  
Social Media ◽  
Descriptive Analysis ◽  
World Health ◽  
Validity And Reliability ◽  
Cross Sectional Survey ◽  
Chi Square ◽  
Cross Sectional ◽  
The World ◽  
Enugu State ◽  
Health Organization

Background: The world is experiencing a global corona virus (COVID-19) pandemic. As of 9th June 2020, over 7 million confirmed cases of coronavirus disease (COVID-19) and more than 400,000 deaths had been reported in more than 30 countries of the world according to World Health Organization. Aim: We aimed to assess the knowledge, attitudes, and vulnerability perception of Enugu state residents during the coronavirus outbreak in order to facilitate better health care outcomes. Methodology: A prospective Web-based cross-sectional survey was designed for this study which was conducted in March 2020 among Enugu state residents. The obtained data were coded, validated, and analyzed using Statistical Package for the Social Sciences SPSS software, version 24. Descriptive analysis was applied to calculate the frequencies and proportions and Chi-Square Test was also used. A preliminary phase was conducted to assess the validity and reliability of the questionnaire before its use.  Results: The study showed that significant number (99.6%) of the respondents had heard about Covid-19 and the most stated source of knowledge was social media (57.6%), followed by Newspaper and television shows (50.2%) while the least was from General Practitioner (GP) (8.9%). There was over 75% agreement with, and practice, of all known covid precautionary measures and less than 35% responses for wrong claims and practices about covid -19. Conclusion: The overall knowledge, attitude, and perception are high in Enugu state population although few still has background combined superstitious believes. Social media and internet are the highest used facility for acquisition of knowledge and information in Enugu, Nigeria.

scholarly journals Machine Learning based Human Fall Detection System

2021 ◽  
Vol 9 (VI) ◽  
pp. 2677-2682
Author(s):  
Nishanth P
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Wearable Sensors ◽  
Fall Detection ◽  
The Elderly ◽  
World Health ◽  
Support Vector ◽  
Data Output ◽  
Health Organization ◽  
Context Features

Falls have become one of the reasons for death. It is common among the elderly. According to World Health Organization (WHO), 3 out of 10 living alone elderly people of age 65 and more tend to fall. This rate may get higher in the upcoming years. In recent years, the safety of elderly residents alone has received increased attention in a number of countries. The fall detection system based on the wearable sensors has made its debut in response to the early indicator of detecting the fall and the usage of the IoT technology, but it has some drawbacks, including high infiltration, low accuracy, poor reliability. This work describes a fall detection that does not reliant on wearable sensors and is related on machine learning and image analysing in Python. The camera's high-frequency pictures are sent to the network, which uses the Convolutional Neural Network technique to identify the main points of the human. The Support Vector Machine technique uses the data output from the feature extraction to classify the fall. Relatives will be notified via mobile message. Rather than modelling individual activities, we use both motion and context information to recognize activities in a scene. This is based on the notion that actions that are spatially and temporally connected rarely occur alone and might serve as background for one another. We propose a hierarchical representation of action segments and activities using a two-layer random field model. The model allows for the simultaneous integration of motion and a variety of context features at multiple levels, as well as the automatic learning of statistics that represent the patterns of the features.

scholarly journals Misinformation, chiropractic, and the COVID-19 pandemic

2020 ◽  
Vol 28 (1) ◽  
Author(s):  
Iben Axén ◽  
Cecilia Bergström ◽  
Marc Bronson ◽  
Pierre Côté ◽  
Casper Glissmann Nim ◽  
...  
Keyword(s):  
Public Health ◽  
Social Media ◽  
Scientific Evidence ◽  
Collective Responsibility ◽  
Economic Consequences ◽  
Best Interests ◽  
World Health ◽  
Chiropractic Treatment ◽  
Health Organization ◽  
The Impact

Abstract Background In March 2020, the World Health Organization elevated the coronavirus disease (COVID-19) epidemic to a pandemic and called for urgent and aggressive action worldwide. Public health experts have communicated clear and emphatic strategies to prevent the spread of COVID-19. Hygiene rules and social distancing practices have been implemented by entire populations, including ‘stay-at-home’ orders in many countries. The long-term health and economic consequences of the COVID-19 pandemic are not yet known. Main text During this time of crisis, some chiropractors made claims on social media that chiropractic treatment can prevent or impact COVID-19. The rationale for these claims is that spinal manipulation can impact the nervous system and thus improve immunity. These beliefs often stem from nineteenth-century chiropractic concepts. We are aware of no clinically relevant scientific evidence to support such statements. We explored the internet and social media to collect examples of misinformation from Europe, North America, Australia and New Zealand regarding the impact of chiropractic treatment on immune function. We discuss the potential harm resulting from these claims and explore the role of chiropractors, teaching institutions, accrediting agencies, and legislative bodies. Conclusions Members of the chiropractic profession share a collective responsibility to act in the best interests of patients and public health. We hope that all chiropractic stakeholders will view the COVID-19 pandemic as a call to action to eliminate the unethical and potentially dangerous claims made by chiropractors who practise outside the boundaries of scientific evidence.

scholarly journals The “Obiettivo Antibiotico” Campaign on Prudent Use of Antibiotics in Sicily, Italy: The Pilot Phase

2020 ◽  
Vol 17 (9) ◽  
pp. 3077 ◽  
Author(s):  
Martina Barchitta ◽  
Annalisa Quattrocchi ◽  
Andrea Maugeri ◽  
Maria Clara La Rosa ◽  
Claudia La Mastra ◽  
...  
Keyword(s):  
Social Media ◽  
Antimicrobial Resistance ◽  
General Public ◽  
Healthcare Professionals ◽  
Educational Interventions ◽  
World Health ◽  
Pilot Phase ◽  
The Public ◽  
The World ◽  
Health Organization

The issue of antimicrobial resistance (AMR) is a focus of the World Health Organization, which proposes educational interventions targeting the public and healthcare professionals. Here, we present the first attempt at a regionwide multicomponent campaign in Sicily (Italy), called “Obiettivo Antibiotico”, which aims to raise the awareness of prudent use of antibiotics in the public and in healthcare professionals. The campaign was designed by an interdisciplinary academic team, and an interactive website was populated with different materials, including key messages, letters, slogans, posters, factsheets, leaflets, and videos. The campaign was launched in November 2018 and, as of 21 December 2018, the website had a total of 1159 unique visitors, of which 190 became champions by pledging to take simple actions to support the fight against AMR. Data from social media showed that the audience was between 18 and 54 years of age, with a high proportion of female participants (64%). Interestingly, the LinkedIn page received more than 1200 followers, and Facebook 685 followers. The number of actions taken (pledges) by the audience was 458, evenly divided between experts (53%) and the general public (47%). Additional efforts are needed to reach more people, thus future efforts should focus on further promotion within the Sicilian region to sustain the engagement with the campaign.

scholarly journals Machine learning versus logistic regression methods for 2-year mortality prognostication in a small, heterogeneous glioma database

2018 ◽  
Author(s):  
Sandip S Panesar ◽  
Rhett N D’Souza ◽  
Fang-Cheng Yeh ◽  
Juan C Fernandez-Miranda
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Machine Learning Techniques ◽  
World Health ◽  
Support Vector ◽  
Molecular Characteristics ◽  
Regression Methods ◽  
Learning Techniques ◽  
The World ◽  
Health Organization

AbstractBackgroundMachine learning (ML) is the application of specialized algorithms to datasets for trend delineation, categorization or prediction. ML techniques have been traditionally applied to large, highly-dimensional databases. Gliomas are a heterogeneous group of primary brain tumors, traditionally graded using histopathological features. Recently the World Health Organization proposed a novel grading system for gliomas incorporating molecular characteristics. We aimed to study whether ML could achieve accurate prognostication of 2-year mortality in a small, highly-dimensional database of glioma patients.MethodsWe applied three machine learning techniques: artificial neural networks (ANN), decision trees (DT), support vector machine (SVM), and classical logistic regression (LR) to a dataset consisting of 76 glioma patients of all grades. We compared the effect of applying the algorithms to the raw database, versus a database where only statistically significant features were included into the algorithmic inputs (feature selection).ResultsRaw input consisted of 21 variables, and achieved performance of (accuracy/AUC): 70.7%/0.70 for ANN, 68%/0.72 for SVM, 66.7%/0.64 for LR and 65%/0.70 for DT. Feature selected input consisted of 14 variables and achieved performance of 73.4%/0.75 for ANN, 73.3%/0.74 for SVM, 69.3%/0.73 for LR and 65.2%/0.63 for DT.ConclusionsWe demonstrate that these techniques can also be applied to small, yet highly-dimensional datasets. Our ML techniques achieved reasonable performance compared to similar studies in the literature. Though local databases may be small versus larger cancer repositories, we demonstrate that ML techniques can still be applied to their analysis, though traditional statistical methods are of similar benefit.

scholarly journals Preparedness and Mitigation by projecting the risk against COVID-19 transmission using Machine Learning Techniques

2020 ◽  
Author(s):  
Akshay Kumar ◽  
Farhan Mohammad Khan ◽  
Rajiv Gupta ◽  
Harish Puppala
Keyword(s):  
Machine Learning ◽  
Gaussian Process Regression ◽  
Machine Learning Techniques ◽  
World Health ◽  
Support Vector ◽  
Learning Tools ◽  
Learning Techniques ◽  
Health Organization ◽  
Respiratory Coronavirus ◽  
Criticality Index

AbstractThe outbreak of COVID-19 is first identified in China, which later spread to various parts of the globe and was pronounced pandemic by the World Health Organization (WHO). The disease of transmissible person-to-person pneumonia caused by the extreme acute respiratory coronavirus 2 syndrome (SARS-COV-2, also known as COVID-19), has sparked a global warning. Thermal screening, quarantining, and later lockdown were methods employed by various nations to contain the spread of the virus. Though exercising various possible plans to contain the spread help in mitigating the effect of COVID-19, projecting the rise and preparing to face the crisis would help in minimizing the effect. In the scenario, this study attempts to use Machine Learning tools to forecast the possible rise in the number of cases by considering the data of daily new cases. To capture the uncertainty, three different techniques: (i) Decision Tree algorithm, (ii) Support Vector Machine algorithm, and (iii) Gaussian process regression are used to project the data and capture the possible deviation. Based on the projection of new cases, recovered cases, deceased cases, medical facilities, population density, number of tests conducted, and facilities of services, are considered to define the criticality index (CI). CI is used to classify all the districts of the country in the regions of high risk, low risk, and moderate risk. An online dashpot is created, which updates the data on daily bases for the next four weeks. The prospective suggestions of this study would aid in planning the strategies to apply the lockdown/ any other plan for any country, which can take other parameters to define the CI.

scholarly journals Anxiety disorder and consumption of social media in Brazil

2020 ◽  
Vol 8 (5) ◽  
pp. 316-326
Author(s):  
Tarcisio Torres Silva
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Anxiety Disorder ◽  
World Health ◽  
Political Polarization ◽  
The Political ◽  
Social Changes ◽  
The World ◽  
Health Organization ◽  
The City

Brazilian population spends a lot of time on social media. The average access from any device is 3 hours and 39 minutes (The Global, 2018). On the other hand, the country leads the numbers of anxiety disorder among the population. According to the World Health Organization, the incidence in the country is 9.3%, while the world average is 3.5%. This number is even higher in big cities, reaching 19.9% in the city of São Paulo (Horta, 2019). Possible causes are economic instability, social changes and violence (Horta, 2019). Add to that the political polarization in recent years and the intensive use of gadgets, private chat applications, such as Whatsapp, and social networks. In this work, we focus on the influence of social networks in the development of Brazilian anxiety. Our hypothesis is that the intensity of use reinforces the existence of other factors of anxiety increase (economy, violence, political division, etc.) through the sharing of news, besides adding others, such as self-display, performativity and the need of always being in evidence in social networks. As a method, we will work with content analysis (news and images) from the main social networking platforms used in Brazil.

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