Automatic classification of social media reports on violent incidents in South Africa using machine learning




WhatsApp, text classification, Word2Vec, protests, open-source intelligence, OSINT


With the growing amount of data available in the digital age, it has become increasingly important to use automated methods to extract useful information from data. One such application is the extraction of events from news sources for the purpose of a quantitative analysis that does not rely on someone needing to read through thousands of news articles. Overseas, projects such as the Integrated Crisis Early Warning System (ICEWS) monitor news stories and extract events using automated coding. However, not all violent events are reported in the news, and while monitoring only news agencies is sufficient for projects such as ICEWS which have a global focus, more news sources are required when assessing a local situation. We used WhatsApp as a news source to identify the occurrence of violent incidents in South Africa. Using machine learning, we have shown how violent incidents can be coded and recorded, allowing for a local level recording of these events over time. Our experimental results show good performance on both training and testing data sets using a logistic regression classifier with unigrams and Word2vec feature models. Future work will evaluate the inclusion of pre-trained word embedding for both Afrikaans and English words to improve the performance of the machine learning classifier.


  • The logistic regression classifier using TFIDF unigram, CBOW and skip-gram Word2Vec models were successfully implemented to automatically analyse and classify WhatsApp messages from groups that share information on protests and mass violence in South Africa. At the time of publishing, messages were collected from 26 WhatsApp groups across South Africa and automatically classified on an hourly basis.



How to Cite

Kotzé, E., Senekal, B. A., & Daelemans, W. (2020). Automatic classification of social media reports on violent incidents in South Africa using machine learning. South African Journal of Science, 116(3/4).



Research Article