scholarly journals Schizophrenia Detection Using Machine Learning Approach from Social Media Content

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5924
Author(s):  
Yi Ji Bae ◽  
Midan Shim ◽  
Won Hee Lee
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Social Media ◽  
Mental Health Problems ◽  
Negative Emotion ◽  
Supervised Machine Learning ◽  
Control Group ◽  
Learning Approaches ◽  
Linguistic Features ◽  
Media Texts

Schizophrenia is a severe mental disorder that ranks among the leading causes of disability worldwide. However, many cases of schizophrenia remain untreated due to failure to diagnose, self-denial, and social stigma. With the advent of social media, individuals suffering from schizophrenia share their mental health problems and seek support and treatment options. Machine learning approaches are increasingly used for detecting schizophrenia from social media posts. This study aims to determine whether machine learning could be effectively used to detect signs of schizophrenia in social media users by analyzing their social media texts. To this end, we collected posts from the social media platform Reddit focusing on schizophrenia, along with non-mental health related posts (fitness, jokes, meditation, parenting, relationships, and teaching) for the control group. We extracted linguistic features and content topics from the posts. Using supervised machine learning, we classified posts belonging to schizophrenia and interpreted important features to identify linguistic markers of schizophrenia. We applied unsupervised clustering to the features to uncover a coherent semantic representation of words in schizophrenia. We identified significant differences in linguistic features and topics including increased use of third person plural pronouns and negative emotion words and symptom-related topics. We distinguished schizophrenic from control posts with an accuracy of 96%. Finally, we found that coherent semantic groups of words were the key to detecting schizophrenia. Our findings suggest that machine learning approaches could help us understand the linguistic characteristics of schizophrenia and identify schizophrenia or otherwise at-risk individuals using social media texts.

scholarly journals Towards Automatic Fake News Detection in Digital Platforms: Properties, Limitations, and Applications

2021 ◽  
Author(s):  
Julio C. S. Reis ◽  
Fabrício Benevenuto
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Prediction Performance ◽  
Supervised Machine Learning ◽  
Learning Approaches ◽  
Fake News ◽  
Digital Platforms ◽  
Worldwide Phenomenon ◽  
Fact Checking ◽  
Media Systems

Digital platforms, including social media systems and messaging applications, have become a place for campaigns of misinformation that affect the credibility of the entire news ecosystem. The emergence of fake news in these environments has quickly evolved into a worldwide phenomenon, where the lack of scalable fact-checking strategies is especially worrisome. In this context, this thesis aim at investigating practical approaches for the automatic detection of fake news disseminated in digital platforms. Particularly, we explore new datasets and features for fake news detection to assess the prediction performance of current supervised machine learning approaches. We also propose an unbiased framework for quantifying the informativeness of features for fake news detection, and present an explanation of factors contributing to model decisions considering data from different scenarios. Finally, we propose and implement a new mechanism that accounts for the potential occurrence of fake news within the data, significantly reducing the number of content pieces journalists and fact-checkers have to go through before finding a fake story.

scholarly journals Cyber Bullying Detection on Social Media using Machine Learning

2021 ◽  
Vol 40 ◽  
pp. 03038
Author(s):  
Aditya Desai ◽  
Shashank Kalaskar ◽  
Omkar Kumbhar ◽  
Rashmi Dhumal
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Social Media ◽  
Mental Health Problems ◽  
Self Esteem ◽  
Young Generation ◽  
Health Issues ◽  
The Past ◽  
The Individual ◽  
Deep Learning Model

Usage of internet and social media backgrounds tends in the use of sending, receiving and posting of negative, harmful, false or mean content about another individual which thus means Cyberbullying. Bullying over social media also works the same as threatening, calumny, and chastising the individual. Cyberbullying has led to a severe increase in mental health problems, especially among the young generation. It has resulted in lower self-esteem, increased suicidal ideation. Unless some measure against cyberbullying is taken, self-esteem and mental health issues will affect an entire generation of young adults. Many of the traditional machine learning models have been implemented in the past for the automatic detection of cyberbullying on social media. But these models have not considered all the necessary features that can be used to identify or classify a statement or post as bullying. In this paper, we proposed a model based on various features that should be considered while detecting cyberbullying and implement a few features with the help of a bidirectional deep learning model called BERT.

scholarly journals Mental Health Prediction Using Machine Learning: Taxonomy, Applications, and Challenges

2022 ◽  
Vol 2022 ◽  
pp. 1-19
Author(s):  
Jetli Chung ◽  
Jason Teo
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Systematic Review ◽  
Mental Health Problems ◽  
Health Problems ◽  
Mental Health Field ◽  
Research Articles ◽  
Future Research ◽  
Learning Approaches ◽  
Health Field

The increase of mental health problems and the need for effective medical health care have led to an investigation of machine learning that can be applied in mental health problems. This paper presents a recent systematic review of machine learning approaches in predicting mental health problems. Furthermore, we will discuss the challenges, limitations, and future directions for the application of machine learning in the mental health field. We collect research articles and studies that are related to the machine learning approaches in predicting mental health problems by searching reliable databases. Moreover, we adhere to the PRISMA methodology in conducting this systematic review. We include a total of 30 research articles in this review after the screening and identification processes. Then, we categorize the collected research articles based on the mental health problems such as schizophrenia, bipolar disorder, anxiety and depression, posttraumatic stress disorder, and mental health problems among children. Discussing the findings, we reflect on the challenges and limitations faced by the researchers on machine learning in mental health problems. Additionally, we provide concrete recommendations on the potential future research and development of applying machine learning in the mental health field.

Sentiment Analysis Using Machine Learning Algorithms and Text Mining to Detect Symptoms of Mental Difficulties Over Social Media

2021 ◽  
Vol 12 (2) ◽  
pp. 1-15
Author(s):  
Hadj Ahmed Bouarara
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Social Media ◽  
Text Mining ◽  
Mental Health Problems ◽  
Negative Impact ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Nearest Neighbours ◽  
Twitter Users

A recent British study of people between the ages of 14 and 35 has shown that social media has a negative impact on mental health. The purpose of the paper is to detect people with mental disorders' behavior in social media in order to help Twitter users in overcoming their mental health problems such as anxiety, phobia, depression, paranoia, etc. For this, the author used text mining and machine learning algorithms (naïve Bayes, k-nearest neighbours) to analyse tweets. The obtained results were validated using different evaluation measures such as f-measure, recall, precision, entropy, etc.

Mental Health Among Adolescents from Returned Portuguese Immigrant Families

2010 ◽  
Vol 69 (3) ◽  
pp. 131-139 ◽  
Author(s):  
Félix Neto
Keyword(s):  
Mental Health ◽  
Health Outcomes ◽  
Mental Health Problems ◽  
Immigrant Families ◽  
Health Problems ◽  
Mental Health Outcomes ◽  
Control Group ◽  
Older Adolescents ◽  
The Mean ◽  
Portuguese Adolescents

This study investigated mental health problems and their predictors among adolescents from returned immigrant families. The sample consisted of 360 returned adolescents (mean age = 16.8 years; SD = 1.9). The mean duration of a sojourn in Portugal for the sample was 8.2 years (SD = 4.5). A control group of 217 Portuguese youths were also included in the study. Adolescents from immigrant families reported mental health levels similar to those of Portuguese adolescents who have never migrated. Girls showed more mental health problems than boys. Younger adolescents showed fewer mental health problems than older adolescents. Adaptation variables contributed to mental health outcomes even after acculturation variables were accounted for. Implications of the study for counselors are discussed.

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

Exploring fake news identification using word and sentence embeddings

2021 ◽  
pp. 1-8
Author(s):  
V.T Priyanga ◽  
J.P Sanjanasri ◽  
Vijay Krishna Menon ◽  
E.A Gopalakrishnan ◽  
K.P Soman
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Network Analysis ◽  
Supervised Machine Learning ◽  
Breeding Ground ◽  
Fake News ◽  
Data Set ◽  
Highly Correlated ◽  
Use Of Social Media ◽  
The Liar

The widespread use of social media like Facebook, Twitter, Whatsapp, etc. has changed the way News is created and published; accessing news has become easy and inexpensive. However, the scale of usage and inability to moderate the content has made social media, a breeding ground for the circulation of fake news. Fake news is deliberately created either to increase the readership or disrupt the order in the society for political and commercial benefits. It is of paramount importance to identify and filter out fake news especially in democratic societies. Most existing methods for detecting fake news involve traditional supervised machine learning which has been quite ineffective. In this paper, we are analyzing word embedding features that can tell apart fake news from true news. We use the LIAR and ISOT data set. We churn out highly correlated news data from the entire data set by using cosine similarity and other such metrices, in order to distinguish their domains based on central topics. We then employ auto-encoders to detect and differentiate between true and fake news while also exploring their separability through network analysis.

scholarly journals Supervised learning for the detection of negation and of its scope in French and Brazilian Portuguese biomedical corpora

2020 ◽  
pp. 1-21 ◽  
Author(s):  
Clément Dalloux ◽  
Vincent Claveau ◽  
Natalia Grabar ◽  
Lucas Emanuel Silva Oliveira ◽  
Claudia Maria Cabral Moro ◽  
...  
Keyword(s):  
Machine Learning ◽  
Information Extraction ◽  
State Of The Art ◽  
Automatic Detection ◽  
Brazilian Portuguese ◽  
Supervised Machine Learning ◽  
Biomedical Domain ◽  
Learning Approaches ◽  
Cross Domain ◽  
Automatic Methods

Abstract Automatic detection of negated content is often a prerequisite in information extraction systems in various domains. In the biomedical domain especially, this task is important because negation plays an important role. In this work, two main contributions are proposed. First, we work with languages which have been poorly addressed up to now: Brazilian Portuguese and French. Thus, we developed new corpora for these two languages which have been manually annotated for marking up the negation cues and their scope. Second, we propose automatic methods based on supervised machine learning approaches for the automatic detection of negation marks and of their scopes. The methods show to be robust in both languages (Brazilian Portuguese and French) and in cross-domain (general and biomedical languages) contexts. The approach is also validated on English data from the state of the art: it yields very good results and outperforms other existing approaches. Besides, the application is accessible and usable online. We assume that, through these issues (new annotated corpora, application accessible online, and cross-domain robustness), the reproducibility of the results and the robustness of the NLP applications will be augmented.

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