Deteccion de Ideas Suicidas en Twitter

Sentiment analysis is a new trend' nowadays to understand how people' feel in different situations of their daily life. Social network data is used during the whole process of analysis and' classification, which consists of text data. Using social networks, the emotional level can be monitored or analyzed. In this research work we will classify data from social networks such as twitter regarding suicide and classify it as: active suicidal thinking, passive suicidal thinking, sarcasm related to suicidal thinking, tweets related to suicide (suicide awareness, news, suicide talk) and others.El análisis de sentimientos es una nueva tendencia en la actualidad para comprender como se sienten las personas en diferentes situaciones de su vida diaria. Los datos de las redes sociales se utilizan durante todo el proceso de análisis y clasificación, que consiste en datos de texto. Usando las redes sociales, el nivel emocional puede ser monitoreado o analizado. En este trabajo de investigacion se clasificarán datos de las redes sociales como twitter respecto al suicidio y lo clasificara como:  pensamiento suicida activo, pensamiento suicida pasiva, sarcasmo relacionado con el pensamiento suicida, tweets relacionados con el suicidio (concienciación, noticias, charlas sobre el suicidio) y otros

An Optimized Machine Learing Framework For Extracting Suicide Factors Using K-Means++ Clustering

Suicide has emerged as one of the serious problems which should be eradicated from the society. People with suicidal thoughts restrict themselves by not expressing thoughts to the people around them. Studies have shown that people show more interest in expressing their thoughts over social media platforms. So, research has been conducted to identify people with suicidal ideation by analyzing the posts which they posted in social media platforms. Certain studies mined out new factors which influenced people to commit suicide, but those factors had certain drawbacks in it. This paper mainly focuses on overcoming those drawbacks in the factors. A new modified approach for extracting those risk factors is introduced as it can be used for future works related to suicidal ideation detection tasks. Statistical methods were imposed on the data to mine out the underlying characteristics of the features. K-Means++ clustering algorithm was implemented to extract the modified features. The modified features were given as an input for a testing classifier, and it attained an accuracy of 75.13%

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

يعد التفكير في الانتحار من أخطر مشكلات الصحة العقلية التي يواجهها الناس في جميع أنحاء العالم. هناك عوامل خطر مختلفة يمكن أن تؤدي إلى الانتحار. من أكثر عوامل الخطر شيوعًا وأكثرها خطورة الاكتئاب والقلق والعزلة الاجتماعية واليأس. يمكن أن يساعد الاكتشاف المبكر لعوامل الخطر هذه في منع أو تقليل عدد حالات الانتحار. أصبحت منصات الشبكات الاجتماعية عبر الإنترنت مثل تويتر وريدت وفيس بوك طريقة جديدة للناس للتعبير عن أنفسهم بحرية دون القلق بشأن الوصمة الاجتماعية. تقدم هذه الورقة منهجية وتجربة باستخدام وسائل التواصل الاجتماعي كأداة لتحليل الأفكار الانتحارية بطريقة أفضل ، وبالتالي المساعدة في منع فرص الوقوع ضحية لهذا الاضطراب العقلي المؤسف. نجمع البيانات ذات الصلة عبر توترأحد مواقع الشبكات الاجتماعية الشهيرة (SNS) . ومن ثم تتم معالجة التغريدات يدويًا وإضافة تعليقات توضيحية لها يدويًا. وأخيرًا ، يتم استخدام أساليب التعلم الآلي المختلفة والمجموعات لتمييز التغريدات الانتحارية وغير الانتحارية تلقائيًا. ستساعد هذه الدراسة التجريبية الباحثين على معرفة وفهم كيفية استخدام الأشخاص للتعبير عن النفس في التعبير عن مشاعرهم وعواطفهم. وأكدت الدراسة أيضًا أنه من الممكن تحليل وتمييز هذه التغريدات باستخدام التشفير البشري ثم تكرار الدقة حسب تصنيف الماكينة. ومع ذلك ، فإن قوة التنبؤ للكشف عن الانتحار الحقيقي لم يتم تأكيدها بعد ، وهذه الدراسة لا تتواصل بشكل مباشر وتتدخل مع الأشخاص الذين لديهم سلوك انتحاري..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

Detecting suicide ideation in the era of social media: the population neuroscience perspective

Social media platforms are increasingly used across many population groups not only to communicate and consume information, but also to express symptoms of psychological distress and suicidal thoughts. The detection of suicidal ideation (SI) can contribute to suicide prevention. Twitter data suggesting SI have been associated with negative emotions (e.g., shame, sadness) and a number of geographical and ecological variables (e.g., geographic location, environmental stress). Other important research contributions on SI come from studies in neuroscience. To date, very few research studies have been conducted that combine different disciplines (epidemiology, health geography, neurosciences, psychology, and social media big data science), to build innovative research directions on this topic. This article aims to offer a new interdisciplinary perspective, that is, a Population Neuroscience perspective on SI in order to highlight new ways in which multiple scientific fields interact to successfully investigate emotions and stress in social media to detect SI in the population. We argue that a Population Neuroscience perspective may help to better understand the mechanisms underpinning SI and to promote more effective strategies to prevent suicide timely and at scale

Inj Prev

ObjectiveThe purpose of this research is to identify how data science is applied in suicide prevention literature, describe the current landscape of this literature and highlight areas where data science may be useful for future injury prevention research.DesignWe conducted a literature review of injury prevention and data science in April 2020 and January 2021 in three databases.MethodsFor the included 99 articles, we extracted the following: (1) author(s) and year; (2) title; (3) study approach (4) reason for applying data science method; (5) data science method type; (6) study description; (7) data source and (8) focus on a disproportionately affected population.ResultsResults showed the literature on data science and suicide more than doubled from 2019 to 2020, with articles with individual-level approaches more prevalent than population-level approaches. Most population-level articles applied data science methods to describe (n=10) outcomes, while most individual-level articles identified risk factors (n=27). Machine learning was the most common data science method applied in the studies (n=48). A wide array of data sources was used for suicide research, with most articles (n=45) using social media and web-based behaviour data. Eleven studies demonstrated the value of applying data science to suicide prevention literature for disproportionately affected groups.ConclusionData science techniques proved to be effective tools in describing suicidal thoughts or behaviour, identifying individual risk factors and predicting outcomes. Future research should focus on identifying how data science can be applied in other injury-related topics.CC999999/ImCDC/Intramural CDC HHSUnited States

Método semi-supervisado para detectar, clasificar y anotar en un corpus de suicidio textos extraídos de entornos digitales

La presente tesis doctoral, con un enfoque cualicuantitativo (mixto), se enmarca en la línea del análisis de sentimientos en redes sociales, forma parte del proyecto Life, que busca crear una plataforma integral para detectar y brindar apoyo especializado a usuarios de redes sociales que publican textos con contenido suicida. Por ello se desarrolló el Corpus Life para realizar experimentos con algoritmos de aprendizaje automático, mismo que originalmente constaba de 102 mensajes suicidas (71 textos en inglés y 31 textos en español), 70 de estas muestras Sin Riesgo y 32 con Riesgo. Pero debido al escaso número de muestras y al desbalance entre ellas, los resultados generados no eran confiables. Por ello esta investigación tuvo como objetivo general desarrollar un método semi-supervisado para detectar, clasificar y anotar en el Corpus Life, textos extraídos de entornos digitales, con el fin de incrementar su número de anotaciones, mediante un proceso de evaluación automática de su calidad, previo a su inclusión o exclusión. Anotaciones que fueron evaluadas manualmente, utilizando para ello la medida de concordancia Cohen´s Kappa, con la participación de anotadores especializados quienes evaluaron los textos, alcanzando un nivel de acuerdo entre anotadores de 0,86, cercano al 0,78-0,81 de significancia estadística alcanzado automáticamente por medio del índice macro f1, con el método semi-supervisado. Lo que conllevo a alcanzar experimentos de un mayor grado de confiabilidad, por medio de un método estructurado con actividades, roles y procesos bien definidos y enlazados.This doctoral thesis with a qualitative-quantitative (mixed) approach is part of the analysis of feelings in social networks that publish texts with suicidal content. For this reason, Corpus life was developed to carry out experiments with machine learning algorithms, which originally consisted of 102 suicide messages (71 texts in English and 31 texts in Spanish), 70 of these samples without risk and 32 with risk. But due to the small number of samples and the imbalance between them, the generated outcome was not reliable. Therefore, this research had the general objective of developing a semi-supervised method to detect, classify and annotate in the Corpus Life, texts extracted from digital environments, in order to increase their number of annotations, through a process of automatic assessments of their quality, prior to their inclusion or exclusion. Records which were tested manually, using the Cohen's Kappa concordance measure, with the participation of specialized annotators who evaluated the texts, reaching a level of agreement between annotators of 0.86, close to 0.78-0.81 of statistically significant reaching automatically by means of the f1 macro index, with the semi-supervised method. This led to achieving experiments with a higher degree of reliability, through a structured method with well-defined and linked activities, roles and processes

Social impact of psychological research on well-being shared in social media

The purpose of this article is to demonstrate how the Social Impact in Social Media (SISM, hereinafter) methodology applied in psychological research provides evidence for the visibility of the social impact of the research. This article helps researchers become aware of whether and how their improvements are capturing the interest of citizens and how citizens are applying such evidence and obtaining better outcomes, in this case, in relation to well-being. In addition, citizens can access the latest evidence on social media and act as channels of communication between science and social or personal networks and, in doing so, they can improve the living conditions of others. This methodology is also useful for agencies that support researchers in psychology with financial assistance, which can use it to evaluate the social impact of the funds that they invest in research. In this article, the 10 studies on well-being were selected for analysis using the following criteria: their research results led to demonstrable improvement in well-being, and these improvements are presented on social media. We applied the social impact coverage ratio to identify the percentage of the social impact shared in social media in relation to the total amount of social media data collected. Finally, examples of quantitative and qualitative evidence of the social impact of the research on well-being are presented