Adaptation of Information Retrieval Methods for Identifying of Destructive Informational Influence in Social Networks

Okhapkina, Elena; Okhapkin, Valentin; Kazarin, Oleg V.

doi:10.1109/waina.2017.116

Cited by 12 publications

(9 citation statements)

References 2 publications

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“…Models for cybersuicide detection include regression analysis [20], ANN [21], and CRF [22]. Okhapkina et al built a dictionary of terms pertaining to suicidal content and introduced term frequency-inverse document frequency (TF-IDF) matrices for messages and a singular vector decomposition for matrices [23]. Mulholland and Quinn extracted vocabulary and syntactic features to build a classifier for suicidal and nonsuicidal lyricists [24].…”

Section: Related Workmentioning

confidence: 99%

Supervised Learning for Suicidal Ideation Detection in Online User Content

Yu²,

et al. 2018

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Early detection and treatment are regarded as the most effective ways to prevent suicidal ideation and potential suicide attempts—two critical risk factors resulting in successful suicides. Online communication channels are becoming a new way for people to express their suicidal tendencies. This paper presents an approach to understand suicidal ideation through online user-generated content with the goal of early detection via supervised learning. Analysing users’ language preferences and topic descriptions reveals rich knowledge that can be used as an early warning system for detecting suicidal tendencies. Suicidal individuals express strong negative feelings, anxiety, and hopelessness. Suicidal thoughts may involve family and friends. And topics they discuss cover both personal and social issues. To detect suicidal ideation, we extract several informative sets of features, including statistical, syntactic, linguistic, word embedding, and topic features, and we compare six classifiers, including four traditional supervised classifiers and two neural network models. An experimental study demonstrates the feasibility and practicability of the approach and provides benchmarks for the suicidal ideation detection on the active online platforms: Reddit SuicideWatch and Twitter.

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Section: Related Workmentioning

confidence: 99%

Supervised Learning for Suicidal Ideation Detection in Online User Content

Yu²,

et al. 2018

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“…Using simple and linear classifiers, they found 70% of the users with a suicide attempt and identified their gender with 91.9% accuracy. Okhapkina et al [26] studied the adaptation of information retrieval methods for identifying a destructive informational influence in social networks. He built a dictionary of terms pertaining to a suicidal content.…”

Section: Background and Related Workmentioning

confidence: 99%

Detection of Suicide Ideation in Social Media Forums Using Deep Learning

et al. 2019

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Suicide ideation expressed in social media has an impact on language usage. Many at-risk individuals use social forum platforms to discuss their problems or get access to information on similar tasks. The key objective of our study is to present ongoing work on automatic recognition of suicidal posts. We address the early detection of suicide ideation through deep learning and machine learning-based classification approaches applied to Reddit social media. For such purpose, we employ an LSTM-CNN combined model to evaluate and compare to other classification models. Our experiment shows the combined neural network architecture with word embedding techniques can achieve the best relevance classification results. Additionally, our results support the strength and ability of deep learning architectures to build an effective model for a suicide risk assessment in various text classification tasks.

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“…Abboute et al [45] built a set of keywords for vocabulary feature extraction within nine suicidal topics. Okhapkina et al [46] built a dictionary of terms about suicidal content. They introduced term frequency-inverse document frequency (TF-IDF) matrices for messages and a singular value decomposition (SVD) for matrices.…”

Section: B Feature Engineeringmentioning

confidence: 99%

Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications

Pan

Xue

et al. 2021

IEEE Trans. Comput. Soc. Syst.

157

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Suicide is a critical issue in modern society. Early detection and prevention of suicide attempts should be addressed to save people's life. Current suicidal ideation detection methods include clinical methods based on the interaction between social workers or experts and the targeted individuals and machine learning techniques with feature engineering or deep learning for automatic detection based on online social contents. This paper is the first survey that comprehensively introduces and discusses the methods from these categories. Domain-specific applications of suicidal ideation detection are reviewed according to their data sources, i.e., questionnaires, electronic health records, suicide notes, and online user content. Several specific tasks and datasets are introduced and summarized to facilitate further research. Finally, we summarize the limitations of current work and provide an outlook of further research directions. Index TermsSuicidal ideation detection, social content, feature engineering, deep learning. a screening tool, but also pointed out that people's expression of suicidal ideation represents their psychological distress. Effective detection of early signals of suicidal ideation can identify people with suicidal thoughts and open a communication portal to let social workers mitigate their mental issues. The reasons for suicide are complicated and attributed to a complex interaction of many factors [5], [8]. To detect suicidal ideation, many researchers conducted psychological and clinical studies [9] and classified responses of questionnaires [10]. Based on their social media data, artificial intelligence (AI) and machine learning techniques can predict people's likelihood of suicide [11], which can better understand people's intentions and pave the way for early intervention. Detection on social content focuses on feature engineering [12], [13], sentiment analysis [14], [15], and deep learning [16], [17], [18]. Those methods generally require heuristics to select features or design artificial neural network architectures for learning rich representation. The research trend focuses on selecting more useful features from people's health records and developing neural architectures to understand the language with suicidal ideation better.Mobile technologies have been studied and applied to suicide prevention, for example, the mobile suicide intervention application iBobbly [19] developed by the Black Dog Institute 2 . Many other suicide prevention tools integrated with social networking services have also been developed, including Samaritans Radar 3 and Woebot 4 . The former was a Twitter plugin that was later discontinued because of privacy issues. For monitoring alarming posts. The latter is a Facebook chatbot based on cognitive behavioral therapy and natural language processing (NLP) techniques for relieving people's depression and anxiety.Applying cutting-edge AI technologies for suicidal ideation detection inevitably comes with privacy issues [20] and ethical concerns [21]. Linthicum et al....

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Adaptation of Information Retrieval Methods for Identifying of Destructive Informational Influence in Social Networks

Cited by 12 publications

References 2 publications

Supervised Learning for Suicidal Ideation Detection in Online User Content

Supervised Learning for Suicidal Ideation Detection in Online User Content

Detection of Suicide Ideation in Social Media Forums Using Deep Learning

Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications

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