Original Article

Textual Analysis of Tweets Associated with Domestic Violence

Abstract

Background: Domestic violence is a global public health concern as stated by World Health Organization. We aimed to conduct a textual analysis of tweets associated with domestic violence through keyword identification, word trends and word collocations. The data was obtained from Twitter, focusing on publicly available tweets written in English.  The objectives are to find out if the identified keywords, word trends and word collocations can help differentiate between domestic violence-related tweets and non-domestic violence-related tweets, as well as, to analyze the textual characteristics of domestic violence-related tweets and non-domestic violence-related tweets.

Methods: Overall, 11,041 tweets were collected using a few keywords over a period of 15 days from 22 March 2021 to 5 April 2021. A text analysis approach was used to discover the most frequent keywords used, the word trends of those keywords and the word collocations of the keywords in differentiating between domestic violence-related or non-domestic violence-related tweets.

Results: Domestic violence-related tweets and non-domestic violence-related tweets had differentiating characteristics, despite sharing several main keywords. In particular, keywords like “domestic”, “violence” and “suicide” featured prominently in domestic-violence related tweets but not in non-domestic violence-related tweets. Significant differences could also be seen in the frequency of keywords and the word trends in the collection of the tweets.

Conclusion: These findings are significant in helping to automate the flagging of domestic-violence related tweets and alert the authorities so that they can take proactive steps such as assisting the victims in getting medical, police and legal help as needed.

 
1. World Health Organization (WHO) (2021). Violence against women. Available from: https://www.who.int/news-room/fact-sheets/detail/violence-against-women
2. Kourti A, Stavridou A, Panagouli E, et al (2023). Domestic Violence during the COVID-19 Pandemic: A Systematic Review. Trauma Violence Abuse, 24(2):719-745.
3. Reisenhofer S, Taft A (2013). Women's journey to safety - the Transtheoretical model in clinical practice when working with women experiencing Intimate Partner Violence: a scientific review and clinical guidance. Patient Educ Couns, 93(3):536-548.
4. Huecker MR, King KC, Jordan GA, Smock W (2021). Domestic Violence. Available from: https://www.ncbi.nlm.nih.gov/books/NBK499891/
5. Rakovec-Felser Z (2014). Domestic Violence and Abuse in Intimate Relationship from Public Health Perspective. Health Psychol Res, 2(3):1821-1821.
6. Chunara R, Andrews JR, Brownstein JS (2012). Social and news media enable es-timation of epidemiological patterns early in the 2010 Haitian cholera out-break. Am J Trop Med Hyg, 86(1):39-45.
7. Neiger BL, Thackeray R, Burton SH, Gi-raud-Carrier CG, Fagen MC (2013). Evaluating social media's capacity to develop engaged audiences in health promotion settings: use of Twitter met-rics as a case study. Health Promot Pract, 14(2):157-162.
8. Prieto VM, Matos S, Álvarez M, Cacheda F, Oliveira JL (2014). Twitter: A Good Place to Detect Health Conditions. PLoS One, 9(1):e86191.
9. Yeung AWK, Kletecka-Pulker M, Eiben-steiner F, et al (2021). Implications of Twitter in Health-Related Research: A Landscape Analysis of the Scientific Lit-erature. Frontiers in Public Health, 9.
10. Sinnenberg L, Buttenheim AM, Padrez K, Mancheno C, Ungar L, Merchant RM (2017). Twitter as a Tool for Health Re-search: A Systematic Review. Am J Public Health, 107(1):e1-e8.
11. Jordan SE, Hovet SE, Fung ICH, Liang H, Fu KW, Tse ZTH (2019). Using Twitter for Public Health Surveillance from Monitoring and Prediction to Public Re-sponse. Data, 4(6).
12. Ji X, Chun SA, Geller J (2013). Monitoring Public Health Concerns Using Twitter Sentiment Classifications. Proceedings of the 2013 IEEE International Conference on Healthcare Informatics, 335-344.
13. Mahdikhani M (2022). Predicting the popu-larity of tweets by analyzing public opinion and emotions in different stag-es of Covid-19 pandemic. International Journal of Information Management Data In-sights, 2(1):100053.
14. Tsai MH, Wang Y (2021). Analyzing Twitter Data to Evaluate People's Attitudes to-wards Public Health Policies and Events in the Era of COVID-19. Int J Environ Res Public Health, 18(12):6272.
15. Evans ML, Lindauer M, Farrell ME (2020). A Pandemic within a Pandemic - Inti-mate Partner Violence during Covid-19. N Engl J Med, 383(24):2302-2304.
16. Hou K, Hou T, Cai L (2021). Public atten-tion about COVID-19 on social media: An investigation based on data mining and text analysis. Pers Individ Dif, 175:110701.
17. López G, Bogen KW, Meza-Lopez RJ, Nugent NR, Orchowski LM (2022). Do-mesticViolence during the COVID-19 Global Pandemic: An Analysis of Public Commentary via Twitter. Digit Health, 8:20552076221115024.
18. Xue J, Chen J, Gelles R (2019). Using Data Mining Techniques to Examine Domes-tic Violence Topics on Twitter. Violence and Gender, 6(2).
19. Safa R, Bayat P, Moghtader L (2022). Auto-matic detection of depression symp-toms in twitter using multimodal analy-sis. J Supercomput, 78:4709-4744.
20. Vidhya, KA, Aghila G (2010). Text Mining Process, Techniques and Tools: An Overview. Int J Inf Technol Knowl Manag, 2(2):613-622.
21. Get TAGS (2017). Twitter Archiving Google Sheet (TAGS). Available from: https://tags.hawksey.info/get-tags/
22. Sinclair S, Rockwell G (2016). Voyant Tools. Available from: http://voyant-tools.org/
23. Devries K, Watts C, Yoshihama M, et al (2011). WHO Multi-Country Study Team. Violence against women is strongly as-sociated with suicide attempts: evidence from the WHO multi-country study on women's health and domestic violence against women. Soc Sci Med, 73(1):79-86.
24. Brown S, Seals J (2019). Intimate partner problems and suicide: are we missing the violence? J Inj Violence Res, 11(1):53-64.
25. Kavak F, Aktürk Ü, Özdemir A, Gültekin A (2018). The relationship between domes-tic violence against women and suicide risk. Arch Psychiatr Nurs, 32(4):574-579.
Files
IssueVol 52 No 11 (2023) QRcode
SectionOriginal Article(s)
DOI https://doi.org/10.18502/ijph.v52i11.14039
Keywords
Domestic violence Twitter Text analysis

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
1.
Chua S, anak Sabang J, Chew KS, binti Nohuddin PNE. Textual Analysis of Tweets Associated with Domestic Violence. Iran J Public Health. 2023;52(11):2402-2411.