scholarly journals Detection of Suicidal Ideation on Twitter using Machine Learning & Ensemble Approaches

2020 ◽  
Vol 17 (4) ◽  
pp. 1328
Author(s):  
Syed Tanzeel Rabani ◽  
Qamar Rayees Khan ◽  
Akib Mohi UD Din Khanday
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Suicidal Ideation ◽  
Social Networking ◽  
Social Networking Sites ◽  
Social Stigma ◽  
Ensemble Methods ◽  
Health Issues ◽  
Mental Health Issues ◽  
The People

Suicidal ideation is one of the most severe mental health issues faced by people all over the world. There are various risk factors involved that can lead to suicide. The most common & critical risk factors among them are depression, anxiety, social isolation and hopelessness. Early detection of these risk factors can help in preventing or reducing the number of suicides. Online social networking platforms like Twitter, Redditt and Facebook are becoming a new way for the people to express themselves freely without worrying about social stigma. This paper presents a methodology and experimentation using social media as a tool to analyse the suicidal ideation in a better way, thus helping in preventing the chances of being the victim of this unfortunate mental disorder. The data is collected from Twitter, one of the popular Social Networking Sites (SNS). The Tweets are then pre-processed and annotated manually. Finally, various machine learning and ensemble methods are used to automatically distinguish Suicidal and Non-Suicidal tweets. This experimental study will help the researchers to know and understand how SNS are used by the people to express their distress related feelings and emotions. The study further confirmed that it is possible to analyse and differentiate these tweets using human coding and then replicate the accuracy by machine classification. However, the power of prediction for detecting genuine suicidality is not confirmed yet, and this study does not directly communicate and intervene the people having suicidal behaviour.

scholarly journals Public Trolling Detection on Twitter Using Machine Learning

2021 ◽  
Vol 9 (VII) ◽  
pp. 3097-3100
Author(s):  
Miss. Pooja Dilip Dhotre
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Social Network ◽  
Social Networking ◽  
Social Networking Sites ◽  
Machine Learning Algorithms ◽  
The People ◽  
The World ◽  
Added Benefit ◽  
Current Popularity

Social media websites are among the internet's most far-reaching digital sites. Billions of social network users exist Users' frequent interactions with social networking sites, like Twitter, have a widespread and sometimes unfortunate effect on day-to-day life. Social networking sites make it easy for large amounts of unwanted and unrelated information to spread around the world. Twitter is a popular micro blogging service where users connect with others with similar interests. Because of the current popularity of Twitter, it is vulnerable to public shaming. Recently, Twitter has emerged as a rich source of human-generated information, with the added benefit of connecting you with customers and enabling two-way communication. It is generally accepted that when someone posts a comment in an occurrence, it is likely to humiliate the victim. The fact that shaming users' follower counts increase faster than that of the people who don't use shame is interesting. Using machine learning algorithms, users will be able to identify disrespectful words, as well as the overall negativity of those words, which is displayed in a percentage.

scholarly journals Rumour Detection Models & Tools for Social Networking Sites

2019 ◽  
Vol 9 (2) ◽  
pp. 3291-3296
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Social Networking ◽  
Social Networking Sites ◽  
Early Stage ◽  
Economic Losses ◽  
Learning Approaches ◽  
The People ◽  
Social Media Platforms ◽  
Fuzzy Logic Technique

Efficient utilization of social networking sites (SNS) had reduced communication delays, at the same time increased rumour messages. Subsequently, mischievous people started sharing of rumours via social networking sites for gaining personal benefits. This falsified information (i.e., rumour) creates misconception among the people of society influencing socio-economic losses by disrupting the routine businesses of private and government sectors. Communication of rumour information requires rigorous surveillance, before they become viral through social media platforms. Detecting these rumour words in an early stage from messaging applications needs to be predicted using robust Rumour Detection Models (RDM) and succinct tools. RDM are effectively used in detecting the rumours from social media platforms (Twitter, Linkedln, Instagram, WhatsApp, Weibo sena and others) with the help of bag of words and machine learning approaches to a limited extent. RDM fails in detecting the emerging rumours that contains linguistic words of a specific language during the chatting session. This survey compares the various RDM strategies and Tools that were proposed earlier for identifying the rumour words in social media platforms. It is found that many of earlier RDM make use of Deep learning approaches, Machine learning, Artificial Intelligence, Fuzzy logic technique, Graph theory and Data mining techniques. Finally, an improved RDM model is proposed in Figure 2, efficiency of this proposed RDM models is improved by embedding of Pre-defined rumour rules, WordNet Ontology and NLP/machine learning approach giving the precision rate of 83.33% when compared with other state-of-art systems.

scholarly journals Why Social Media Is Ruining Your Life

2020 ◽  
Vol 4 (3) ◽  
pp. 14
Author(s):  
Sarah Grace Glover
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Social Networking ◽  
Social Networking Sites ◽  
Academic Research ◽  
Self Esteem ◽  
Health Issues ◽  
Mental Health Issues ◽  
Personal Accounts ◽  
The Brain

Katherine Omerod’s Why Social Media Is Ruining Your Life takes a cursory look into social networks and their effects on mental health and day-to-day life. As a fashion blogger who uses Instagram as the main source of her business, Omerod uses both personal accounts and academic research to address current issues and bad behaviors developed through frequent social network use. Omerod’s main argument is that social media exaggerates self-esteem and mental health issues. She discusses how social networking sites such as Facebook and Instagram interact with the brain like an addiction.

scholarly journals The Use of Social Networking Sites in Mental Health Interventions for Young People: Systematic Review (Preprint)

2018 ◽  
Author(s):  
Brad Ridout ◽  
Andrew Campbell
Keyword(s):  
Mental Health ◽  
Young People ◽  
Social Networking ◽  
Social Networking Sites ◽  
Youth Mental Health ◽  
Health Interventions ◽  
Health Issues ◽  
Quantitative Studies ◽  
Mental Health Issues ◽  
Mental Health Interventions

BACKGROUND The onset of mental health problems peaks between adolescence and young adulthood; however, young people face barriers to treatment and are often reluctant to seek professional help. Many are instead seeking support and information regarding their mental health via the Web, especially via social networking sites (SNSs), and hence, there is a promising opportunity to use SNSs to deliver or integrate with youth-focused online mental health interventions. Previous reviews have evaluated the effectiveness of SNSs for specific disorders in young people; however, none of the reviews have covered the breadth of SNS–based youth mental health interventions available across all mental health issues. OBJECTIVE This review aimed to systematically identify available evidence regarding the use of SNS–based interventions to support the mental health of young people aged up to 25 years, to evaluate their effectiveness, suitability, and safety, and identify gaps and opportunities for future research. METHODS The PubMed and PsycINFO databases were searched using Medical Subject Headings terms and exploded keywords and phrases. Retrieved abstracts (n=974) were double screened, yielding 235 articles for screening at the full-text level. Of these, 9 articles met the review inclusion criteria. Given the small number of studies, and the variety of outcome measures used, a quantitative meta-analysis was not possible. RESULTS The 9 articles (quantitative studies, qualitative studies, and descriptions of the iterative design process) covered 5 separate interventions. Of the 5 interventions, 2 interventions used purpose-built platforms based on the moderated online social therapy (MOST) model, 2 used Facebook, and 1 evaluated a purpose-built mobile app. The 2 MOST interventions targeted specific mental health issues (depression and psychosis), whereas the others focused on improving mental health literacy, social support, and general well-being. Only 3 quantitative studies were identified, and all used a pre-post design (without a control group) to establish proof of concept. Of the outcome variables assessed, there were significant improvements in mental health knowledge and number of depressive symptoms but no improvement in anxiety or psychosis symptoms. Acceptability of and engagement with the SNS platforms were generally high, as were perceptions of usefulness and safety. Moderation by clinical experts was identified as a key component of the more successful interventions. When offered a choice, users showed a preference for mobile apps over Web-based interfaces. CONCLUSIONS The evidence reviewed suggests young people find SNS–based interventions highly usable, engaging, and supportive. However, future studies need to address the current lack of high-quality evidence for their efficacy in reducing mental health symptoms. Given young people are already turning to SNSs to engage in knowledge seeking and peer-to-peer support, SNS–based youth mental health interventions provide an opportunity to address some of the barriers young people face in accessing qualified mental health support and information.

Novel Machine-Learned Approach for COVID-19 Resource Allocation: A Tool for Evaluating Community Susceptibility (Preprint)

2020 ◽  
Author(s):  
Neil Kale
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Insurance Coverage ◽  
Premature Mortality ◽  
Health Insurance Coverage ◽  
Heart Attacks ◽  
Lifestyle Risk Factors ◽  
The People ◽  
Potential Risk Factors ◽  
Lifestyle Risk

BACKGROUND Despite worldwide efforts to develop an effective COVID vaccine, it is quite evident that initial supplies will be limited. Therefore, it is important to develop methods that will ensure that the COVID vaccine is allocated to the people who are at major risk until there is a sufficient global supply. OBJECTIVE The purpose of this study was to develop a machine-learning tool that could be applied to assess the risk in Massachusetts towns based on community-wide social, medical, and lifestyle risk factors. METHODS I compiled Massachusetts town data for 29 potential risk factors, such as the prevalence of preexisting comorbid conditions like COPD and social factors such as racial composition, and implemented logistic regression to predict the amount of COVID cases in each town. RESULTS Of the 29 factors, 14 were found to be significant (p < 0.1) indicators: poverty, food insecurity, lack of high school education, lack of health insurance coverage, premature mortality, population, population density, recent population growth, Asian percentage, high-occupancy housing, and preexisting prevalence of cancer, COPD, overweightness, and heart attacks. The machine-learning approach is 80% accurate in the state of Massachusetts and finds the 9 highest risk communities: Lynn, Brockton, Revere, Randolph, Lowell, New Bedford, Everett, Waltham, and Fitchburg. The 5 most at-risk counties are Suffolk, Middlesex, Bristol, Norfolk, and Plymouth. CONCLUSIONS With appropriate data, the tool could evaluate risk in other communities, or even enumerate individual patient susceptibility. A ranking of communities by risk may help policymakers ensure equitable allocation of limited doses of the COVID vaccine.

YouTube based religious hate speech and extremism detection dataset with machine learning baselines

2021 ◽  
pp. 1-9
Author(s):  
Noman Ashraf ◽  
Abid Rafiq ◽  
Sabur Butt ◽  
Hafiz Muhammad Faisal Shehzad ◽  
Grigori Sidorov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Networking ◽  
Social Networking Sites ◽  
Nearest Neighbor ◽  
Hate Speech ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Speech Detection ◽  
Supervised Learning Algorithms ◽  
Youtube Videos

On YouTube, billions of videos are watched online and millions of short messages are posted each day. YouTube along with other social networking sites are used by individuals and extremist groups for spreading hatred among users. In this paper, we consider religion as the most targeted domain for spreading hate speech among people of different religions. We present a methodology for the detection of religion-based hate videos on YouTube. Messages posted on YouTube videos generally express the opinions of users’ related to that video. We provide a novel dataset for religious hate speech detection on Youtube comments. The proposed methodology applies data mining techniques on extracted comments from religious videos in order to filter religion-oriented messages and detect those videos which are used for spreading hate. The supervised learning algorithms: Support Vector Machine (SVM), Logistic Regression (LR), and k-Nearest Neighbor (k-NN) are used for baseline results.

scholarly journals Emotion Detection using Social Media and Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 4491-4494
Author(s):  
Mr. Bhavar Shivam S.
Keyword(s):  
Machine Learning ◽  
Social Networking ◽  
Social Networking Sites ◽  
Training Dataset ◽  
Emotion Detection ◽  
Pos Tagging ◽  
Testing Dataset ◽  
Svm Algorithm ◽  
The Right ◽  
Negative Sentiment

Today we do a lot of things online from shopping to data sharing on social networking sites. Social networking (SNS) is good for releasing stress and depression by sharing one’s thoughts. Thus, emotion detection has become a hot trend to day. But there is a problem in analyzing emotions on a SNS like twitter as it generates lakhs of tweets each day and it is hard to keep track of the emotion behind each tweet as it is impossible for a human being to read and decide the emotions behind tweets. So, to help understand behind the texts in a SNS site we thought of designing a project which will keep track of the tweets and predict the right emotion behind the tweets whether they have a positive or a negative sentiment behind them. This thought of project can be achieved by a integration of SNS with NLP and machine learning together. For SNS we will use Twitter as it generates a lot of data which is accessible freely using an API. First, we will enter a keyword and fetch tweets from the twitter. Then stop words will be removed from these tweets using NLTK stop words database. Then the tweets will be passed for POS tagging and only right form of grammatical words will be kept and others will be removed. Then we create a training dataset with two types positive and negative. Then SVM algorithm will be trained using this training dataset. Then each tweet will be passed to the SVM as testing dataset which in turn will return classification of each tweet as a whole in two classes positive and negative. Thus, our application will be helpful in recognizing emotion behind a tweet.

scholarly journals Forecasting the Suitability of Online Mental Health Information for Effective Self-Care Developing Machine Learning Classifiers Using Natural Language Features

2021 ◽  
Vol 18 (19) ◽  
pp. 10048
Author(s):  
Meng Ji ◽  
Wenxiu Xie ◽  
Riliu Huang ◽  
Xiaobo Qian
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Health Information ◽  
Self Care ◽  
Health Resources ◽  
Health Issues ◽  
Mental Health Information ◽  
Learning Classifier ◽  
Mental Health Issues ◽  
Bayesian Machine Learning

Background: Online mental health information represents important resources for people living with mental health issues. Suitability of mental health information for effective self-care remains understudied, despite the increasing needs for more actionable mental health resources, especially among young people. Objective: We aimed to develop Bayesian machine learning classifiers as data-based decision aids for the assessment of the actionability of credible mental health information for people with mental health issues and diseases. Methods: We collected and classified creditable online health information on mental health issues into generic mental health (GEN) information and patient-specific (PAS) mental health information. GEN and PAS were both patient-oriented health resources developed by health authorities of mental health and public health promotion. GENs were non-classified online health information without indication of targeted readerships; PASs were developed purposefully for specific populations (young, elderly people, pregnant women, and men) as indicated by their website labels. To ensure the generalisability of our model, we chose to develop a sparse Bayesian machine learning classifier using Relevance Vector Machine (RVM). Results: Using optimisation and normalisation techniques, we developed a best-performing classifier through joint optimisation of natural language features and min-max normalisation of feature frequencies. The AUC (0.957), sensitivity (0.900), and specificity (0.953) of the best model were statistically higher (p < 0.05) than other models using parallel optimisation of structural and semantic features with or without feature normalisation. We subsequently evaluated the diagnostic utility of our model in the clinic by comparing its positive (LR+) and negative likelihood ratios (LR−) and 95% confidence intervals (95% C.I.) as we adjusted the probability thresholds with the range of 0.1 and 0.9. We found that the best pair of LR+ (18.031, 95% C.I.: 10.992, 29.577) and LR− (0.100, 95% C.I.: 0.068, 0.148) was found when the probability threshold was set to 0.45 associated with a sensitivity of 0.905 (95%: 0.867, 0.942) and specificity of 0.950 (95% C.I.: 0.925, 0.975). These statistical properties of our model suggested its applicability in the clinic. Conclusion: Our study found that PAS had significant advantage over GEN mental health information regarding information actionability, engagement, and suitability for specific populations with distinct mental health issues. GEN is more suitable for general mental health information acquisition, whereas PAS can effectively engage patients and provide more effective and needed self-care support. The Bayesian machine learning classifier developed provided automatic tools to support decision making in the clinic to identify more actionable resources, effective to support self-care among different populations.

How Is the Personality of Facebook Customers?

2017 ◽  
pp. 191-229
Author(s):  
Juan Jose Delgado
Keyword(s):  
Social Networking ◽  
Social Networking Sites ◽  
Online Survey ◽  
Primary Data ◽  
Clear Correlation ◽  
The People ◽  
Research Gap ◽  
Secondary Information ◽  
Almost All ◽  
The Way

This chapter claims to understand the implications of Cloninger´s psychobiological model of temperament in the people behaviour on social networking sites. This study uses secondary information as well as primary data. The author collected and analyzed an online survey with a sample of 365 individuals. The study contributed to bridging the research gap of the correlation between the Cloninger´s psychobiological model of temperament and people behaviour on SNSs. The main findings show that clear correlation exists between Cloninger´s psychobiological temperament dimensions and the way people interacts on Facebook. Mainly, temperament is affecting in almost all the areas that have been tested; Facebook affinity uses of Facebook, motives and barriers of using Facebook.

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