Gender-based violence in 140 characters or fewer: a #BigData case study of Twitter [PeerJ Preprints]
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Gender-based violence in 140 characters or fewer: a #BigData case study of Twitter

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1 Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis), Department of Computer Science and Engineering, Wright State University, Dayton, OH, United States
2 Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis), Department of Psychology, Wright State University, Dayton, OH, United States
3 MIS Branch, United Nations Population Fund Headquarters (UNFPA), New York City, NY, United States
DOI
10.7287/peerj.preprints.883v1
Published
Accepted
Subject Areas
Data Science, Social Computing, World Wide Web and Web Science
Keywords
computational social science, gender-based violence, public attitude, public awareness, intervention campaign, social media, citizen sensing, policy, social computing, web science
Copyright
© 2015 Purohit et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
Cite this article
Purohit H, Banerjee T, Hampton A, Shalin V, Bhandutia N, Sheth A. 2015. Gender-based violence in 140 characters or fewer: a #BigData case study of Twitter. PeerJ PrePrints 3:e883v1

Abstract

Humanitarian and public institutions are increasingly relying on data from social media sites to measure public attitude, and provide timely public engagement. Such engagement supports the exploration of public views on important social issues such as gender-based violence (GBV). In this study, we examine Big (Social) Data consisting of nearly fourteen million tweets collected from the Twitter platform over a period of ten months to analyze public opinion regarding GBV, highlighting the nature of tweeting practices by geographical location and gender. The exploitation of Big Data requires the techniques of Computational Social Science to mine insight from the corpus while accounting for the influence of both transient events and sociocultural factors. We reveal public awareness regarding GBV tolerance and suggest opportunities for intervention and the measurement of intervention effectiveness assisting both governmental and non-governmental organizations in policy development

Author Comment

This is a submission to PeerJ for review.

Additional Information

Competing Interests

Amit Sheth is an Academic Editor for PeerJ Computer Science.

Author Contributions

Hemant Purohit conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, wrote the paper, prepared figures and/or tables, performed the computation work.

Tanvi Banerjee performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, wrote the paper, prepared figures and/or tables, performed the computation work.

Andrew Hampton performed the experiments, analyzed the data, wrote the paper, prepared figures and/or tables.

Valerie Shalin conceived and designed the experiments, analyzed the data, contributed reagents/materials/analysis tools, wrote the paper, reviewed drafts of the paper.

Nayanesh Bhandutia conceived and designed the experiments, wrote the paper, reviewed drafts of the paper, domain use of the analyses for Gender-based Violence.

Amit Sheth conceived and designed the experiments, contributed reagents/materials/analysis tools, wrote the paper, reviewed drafts of the paper.

Data Deposition

The following information was supplied regarding the deposition of related data:

We plan to provide dataset of nearly 14 million tweets studied to the research community via conference data sharing forums, such as AAAI ICWSM.

Funding

Partial support for our study came from the U.S. National Science Foundation Social-Computational Systems (SoCS) program, for grant IIS–1111182 ‘Social Media Enhanced Organizational Sensemaking in Emergency Response’. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Gender-based violence in 140 characters or fewer: a #BigData case study of Twitter
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