Twitter Suicidal Analysis

Mining social media has been the existing area of interest of many researchers mainly to obtain insights about human psychological traits. While social media can be mined for a variety of applications that include advertisement interests, promotion statistics; we aim at mining to predict suicidal instincts.

Overview

Online social networks, have increased connectivity between people. Mining such information can provide many benefits to society, such as providing reassurance and emergency management in the immediate aftermath of natural disasters. Here we mine social media for predicting suicidal tweets, which might mitigate the effects of the same when timely action is taken.

  By the end of the project we aim at reporting the results of a number of machine classifiers built with the aim of classifying text relating to suicide on Twitter. The classifier distinguishes between the more worrying content, such as suicidal ideation, and other suicide-related topics such as reporting of a suicide, memorial, campaigning and support. Also, we aim at improving the results obtained from baseline classifiers by ensemble techniques. We also aim at validating our results using known metrics.

The entire work division can be viewed at code/README.md of this repository.

Software Requirements

• Twitter API
• Python (ML and AI)
• LIWC software or equivalent

Directories

• Base code (Python): IE-NITK/TwitterSuicidalAnalysis/code
• Datasets: IE-NITK/TwitterSuicidalAnalysis/datasets
• References: IE-NITK/TwitterSuicidalAnalysis/references
• New Ideas (Literature and Feasibility Study): IE-NITK/TwitterSuicidalAnalysis/ideas

Installation and How to Use

will be updated with the project progress

Members

• Tushaar (T)
  • Soham (So) (Group - I)
    • Chaitany (C)
  • Harsha (H) (Group - II)
    • Shrinidhi (Sh)
  • Arpith (A) (Group - III)

Base Paper and References

[1]  Burnap, P., Colombo, G., Amery, R., Hodorog, A., & Scourfield, J. (2017). Multi-class machine classification of suicide-related communication on Twitter. Online social networks and media, 2, 32-44.

[2]  O'Dea, B., Wan, S., Batterham, P. J., Calear, A. L., Paris, C., & Christensen, H. (2015). Detecting suicidality on Twitter. Internet Interventions, 2(2), 183-188.