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

ATEM: A Topic Evolution Model for the Detection of Emerging Topics in Scientific Archives

This paper presents ATEM, a novel framework for studying topic evolution in scientific archives. ATEM is based on dynamic topic modeling and dynamic graph embedding techniques that explore the dynamics of content and citations of documents within a scientific corpus. ATEM explores a new notion of contextual emergence for the discovery of emerging interdisciplinary research topics based on the dynamics of citation links in topic clusters. Our experiments show that ATEM can efficiently detect emerging cross-disciplinary topics within the DBLP archive of over five million computer science articles

Suicide risk: a psychophysiological perspective

openCon il termine suicidio ci si riferisce alla morte causata da un atto autolesionistico intenzionale e progettato per essere letale. Il numero di morti annue per atti suicidari è tornato a crescere lungo il periodo della pandemia del COVID-19, rendendo il suicidio un problema sociale sempre più pressante, soprattutto per quanto riguarda le fasce più giovani della popolazione. Il modello biopsicosociale permette di indagare non solo quali elementi contribuiscono alla formazione di pensieri suicidari ma anche perché solo alcuni individui passano dall’ideazione alla messa in atto di tentativi di suicidio concreti. Alla fragilità dell’individuo, determinata dall’ereditarietà genetica dei disturbi psicologici, dalle inclinazioni del carattere o dalle esperienze di vita traumatiche, si sommano i sentimenti di disperazione e di sconfitta che creano le basi per la formazione di pensieri suicidi. Il suicidio può quindi essere descritto come un fenomeno complesso determinato da una combinazione di fattori di rischio ambientali, psicologici, genetici e psicofisiologici che interagiscono tra loro in modo sempre differente. Tra i diversi fattori di rischio presi in considerazione si è deciso di porre l’accento su quelli esaminati tramite tecniche psicofisiologiche. Un esempio sono gli studi condotti tramite elettroencefalografia che hanno identificato una correlazione tra tentato suicidio e deficit nella distinzione tra stimoli di ricompensa e stimoli neutrali e dimostrato il fatto che soggetti a rischio di condotte suicidarie elaborino in modo differente gli stimoli negativi. Altri studi esaminati hanno preso in considerazione tecniche quali la polisonnografia, l’elettrocardiogramma, la conduttanza cutanea e l'analisi delle componenti neuroendocrine. Dagli studi emerge che il prosieguo degli studi in ambito psicofisiologico potrebbe risultare particolarmente importante per la ricerca futura in quanto, non solo è in grado di fornire nuovi spunti e dettagli sull’eziologia del suicidio ma, al contempo, permette di stabilire punteggi di rischio per identificare i soggetti con tendenze suicide. L’incremento delle nostre conoscenze in questo ambito fornirà poi le basi per la creazione di programmi di prevenzione e di trattamento potenzialmente sempre più efficaci.Suicide is described as death caused by intentional self-harm, designed to be lethal. The number of deaths per year from suicide has risen again throughout the COVID-19 pandemic, making suicide an increasingly pressing social problem, especially among the younger population. The biopsychosocial model allows us to investigate not only which elements contribute to the formation of suicidal thoughts but also why only some people turn suicidal ideation into a suicidal attempt. To the fragility of the individual, determined by the genetic heredity of psychological disorders, the inclinations of character or traumatic life experiences, are added to the feelings of despair and defeat that create the basis for the formation of suicidal thoughts. Suicide can therefore be described as a complex phenomenon determined by a combination of environmental, psychological, genetic and psychophysiological risk factors that interact with each other in an ever-changing way. Among the different risk factors considered, it was decided to focus on those examined using psychophysiological techniques. Examples are electroencephalography studies that have identified a correlation between attempted suicide and deficits in the ability to distinguish between reward-predicting and non–reward-predicting stimuli and demonstrated the fact that individuals at risk of suicidal behavior have a different way of processing negative stimuli. Other studies examined used techniques such as polysomnography, electrocardiogram, skin conductance and neuroendocrine tests. From this it emerges that the continuation of psychophysiological studies could be particularly important for future research as, it is not only able to provide new insights and details on the etiology of suicide but, at the same time, it allows to establish risk scores to identify individuals with suicidal tendencies. The increase in our knowledge in this area will then provide the basis for the creation of potentially more effective prevention and treatment programs