Suicidal ideation prediction in twitter data using machine learning techniques

Kumar, Ela; Rao, Koduganti Venkata; Nayak, Soumya Ranjan; Chandra, Ramesh

doi:10.1080/09720502.2020.1721674

Cited by 48 publications

(7 citation statements)

References 6 publications

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“…In 2020, Kumar et al [13] exhibited various models to detect early suicidal ideation using Twitter data through sentiment analysis and supervised learning methods. This work used 60,188 positive and negative tweets, and among these, 86.42% were suicidal tweets.…”

Section: Literature Reviewmentioning

confidence: 99%

Perceived Factors Analysis for Depression and Suicidal Ideation among Bangladeshi University Students Using Association Algorithm

Alam,

Ejaz,

Hasan

et al. 2023

AJRCoS

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Depression stands as a prominent and prevalent mental health issue, representing a significant global public health concern. Its emergence can be attributed to diverse factors. Suicide stands as a prominent global cause of death, eliciting concern on a widespread scale. This study was to analyze the perceived factors for depression and suicidal ideation among Bangladeshi university students in Bangladesh. There are so many factors such as Loneliness, Hopelessness, Helplessness, Relationship Issues, Grade problems, Academic Pressure, Parental problems, Money problems, Social Comparison, Social Media Influence, Family Expectations, Lack of Sleep, Uncertain Future, Health Issues, Bullying, Substance Abuse and Unemployment etc. These factors vary among male and female students. Apriori association algorithm were used to calculate support, confidence and lift of factors sets. Frequent factors sets and relationship were found from the work using Apriori association algorithm. The work is an online survey-based study about psychological and stress status of participants and statistical analysis is used for concluding the results. The research participants are Bangladeshi university students, Data collection carried out by online questionnaire. The findings from data analysis indicated that academic pressure (72.41%), uncertain future (56.32%), hopelessness (48.28%), family expectation (47.13%), financial crisis (42.53%), loneliness (41.38%) and unemployment (37.93%) are the key factors. The prevention of suicides is achievable. Hence, identifying depression and forecasting the potential for suicide risk serves as a means to prevent instances of self-harm within the university student population.

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Section: Literature Reviewmentioning

confidence: 99%

Perceived Factors Analysis for Depression and Suicidal Ideation among Bangladeshi University Students Using Association Algorithm

Alam,

Ejaz,

Hasan

et al. 2023

AJRCoS

View full text Add to dashboard Cite

show abstract

“…Tal como muestra la Figura 2, el estudio se desarrolló en 2 fases y siguió el enfoque del aprendizaje supervisado (Rajesh Kumar et al, 2020), el cual contempla dos momentos: entrenamiento y evaluación.…”

Section: Fases De La Investigaciónunclassified

Representación y aprendizaje de conceptos en Twitter: un análisis de tuits como huellas digitales

Buitrago-Ropero

Laverde

2023

RIED

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Los tuits o mensajes publicados en la red social Twitter son entendidos como huellas digitales que se producen por la interacción de las personas en entornos digitales. Estas huellas se generan tanto en los procesos de educación formal como los que se conducen a través de Ambientes Virtuales de Aprendizaje, como en los procesos de interacción social propios de las redes sociales. En este estudio se analizaron los procesos de representación y aprendizaje de conceptos a partir de la producción de tuits generados por tres grupos de estudiantes universitarios de tercer y quinto año. Dicho estudio de carácter mixto se adelantó bajo el enfoque del aprendizaje supervisado que contempló dos momentos: uno de instrucción y otro de evaluación. Los tuits se analizaron desde tres categorías: contenido, contenedor y contexto, así como desde las operaciones intelectuales del pensamiento conceptual: supra-ordinación, exclusión, infra-ordinación e iso-ordinación. Adicionalmente, se analizó el tono emocional de los tuits mediante técnicas de análisis de contenido, minería de textos y de análisis de sentimiento. Los resultados del estudio señalan la posibilidad de que las huellas digitales puedan ser utilizadas como indicadores de los procesos de representación y aprendizaje de conceptos, no solo desde la perspectiva de la construcción lingüística y cognitiva que supone aprender y representar conceptos, sino desde las condiciones emocionales que se dan en las interacciones de una red social como Twitter. A partir de allí se discuten y se abordan conclusiones relacionadas con el potencial transformador del uso de huellas digitales en educación.

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“…IDS have long stood as sentinels against cyber threats, leveraging various algorithms and techniques to monitor and identify suspicious activities. Among these techniques, Naive Bayes (NB) has garnered attention for its probabilistic approach, which utilizes Bayes' theorem to calculate the likelihood of an event based on prior knowledge of related conditions [6]. While its simplicity and computational efficiency make it suitable for real-time analysis, the burgeoning data volume in IoT systems poses a challenge.…”

Section: Introductionmentioning

confidence: 99%

Intrusion Detection in Internet of Things Systems: A Feature Extraction with Naive Bayes Classifier Approach

Juarez Vargas,

Ghanimi,

et al. 2024

JMC

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The Internet of Things (IoT) has proliferated, transitioning from modest home automation to encompass sectors like healthcare, agriculture, transportation, and manufacturing. This evolution is characterized by devices' ability to autonomously gather, disseminate, and analyze data, leading to improved real-time decision-making, predictive insights, and customized user experiences. The ubiquity of IoT, while promising, introduces significant data security concerns. The vast number of interlinked devices and diverse and often insufficient security features make them vulnerable to cyber threats, emphasizing the need for robust security mechanisms. Intrusion Detection Systems (IDS) have traditionally acted as vital guards against such threats; however, with the ever-increasing data in the IoT, traditional IDS models, such as Naive Bayes, face processing speed and accuracy challenges. This paper introduces a novel model, "FE+NB," which merges advanced Feature Extraction (FE) with the Naive Bayes (NB) classifier. Central to this model is the "Temporal-Structural Synthesis" technique tailored for IoT traffic data, focusing on data compression, temporal and structural analyses, and Feature Selection (FS) using mutual information. Consequently, the model enhances efficiency and accuracy in Intrusion Detection (ID) in complex IoT networks.

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Suicidal ideation prediction in twitter data using machine learning techniques

Cited by 48 publications

References 6 publications

Perceived Factors Analysis for Depression and Suicidal Ideation among Bangladeshi University Students Using Association Algorithm

Perceived Factors Analysis for Depression and Suicidal Ideation among Bangladeshi University Students Using Association Algorithm

Representación y aprendizaje de conceptos en Twitter: un análisis de tuits como huellas digitales

Intrusion Detection in Internet of Things Systems: A Feature Extraction with Naive Bayes Classifier Approach

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