ABSTRACT:
Suicidal ideation detection in online
social networks is an emerging research area with major challenges. Recent
research has shown that the publicly available information, spread across
social media platforms, holds valuable indicators for effectively detecting
individuals with suicidal intentions. The key challenge of suicide prevention
is understanding and detecting the complex risk
factors and warning signs that may precipitate the event. In this paper, we
present a new approach that uses the social media platform Twitter to quantify
suicide warning signs for individuals and to detect posts containing
suicide-related content. The main originality of this approach is the automatic
identification of sudden changes in a user’s online behavior.
To detect such changes, we combine natural language processing techniques to
aggregate behavioral and textual features and pass
these features through a martingale framework, which is widely used for change
detection in data streams. Experiments show that our text-scoring approach
effectively captures warning signs in text compared to traditional machine
learning classifiers. Additionally, the application of the martingale framework
highlights changes in online behavior and shows
promise for detecting behavioral changes in at-risk
individuals.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
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System : Pentium Dual Core.
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Hard Disk : 120 GB.
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Monitor : 15’’ LED
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Input Devices : Keyboard, Mouse
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Ram : 1 GB
SOFTWARE REQUIREMENTS:
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Operating system : Windows 7.
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Coding Language : JAVA/J2EE
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Tool : Netbeans 7.2.1
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Database : MYSQL
REFERENCE:
Johnson Vioul_es,B. Moulahi,J. Az_e,S. Bringay, “Detection of
suicide-related posts in Twitter data streams”, IEEE, Volume: 62, Issue: 1,
Jan.-Feb. 1 2018