Crime Prediction With Missing Data Via Spatiotemporal Regularized Tensor Decomposition

Liang, Weichao; Cao, Jie; Chen, Lei; Wang, Youquan; Wu, Jia; Beheshti, Amin; Tang, Jiangnan

doi:10.1109/tbdata.2023.3283098

Cited by 6 publications

(1 citation statement)

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“…Such an approach can bolster the efforts of offline rescue organizations, such as the Tree Hole Rescue Group [67], in addressing suicidal crises. Moreover, inspired by the work of Liang et al [68], this study could be integrated with the computer and information science community to develop a TD-Crime-like framework for suicide prediction on social media platforms. This approach is particularly pertinent considering that the likelihood of suicide is affected by spatiotemporal factors and the occurrence of missing data.…”

Section: Practicall Contributions To Researchmentioning

confidence: 99%

Predicting Users’ Latent Suicidal Risk in Social Media: An Ensemble Model Based on Social Network Relationships

Meng,

Wang,

Yang

et al. 2024

CMC

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Suicide has become a critical concern, necessitating the development of effective preventative strategies. Social media platforms offer a valuable resource for identifying signs of suicidal ideation. Despite progress in detecting suicidal ideation on social media, accurately identifying individuals who express suicidal thoughts less openly or infrequently poses a significant challenge. To tackle this, we have developed a dataset focused on Chinese suicide narratives from Weibo's Tree Hole feature and introduced an ensemble model named Text Convolutional Neural Network based on Social Network relationships (TCNN-SN). This model enhances predictive performance by leveraging social network relationship features and applying correction factors within a weighted linear fusion framework. It is specifically designed to identify key individuals who can help uncover hidden suicidal users and clusters. Our model, assessed using the bespoke dataset and benchmarked against alternative classification approaches, demonstrates superior accuracy, F1-score and AUC metrics, achieving 88.57%, 88.75% and 94.25%, respectively, outperforming traditional TextCNN models by 12.18%, 10.84% and 10.85%. We assert that our methodology offers a significant advancement in the predictive identification of individuals at risk, thereby contributing to the prevention and reduction of suicide incidences.

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Section: Practicall Contributions To Researchmentioning

confidence: 99%