Name
Abstract | ||
|---|---|---|
Corruption is a serious impediment to global goals of ensuring sustainable development and is now a threat specifically recognized in the UN Sustainable Development Goals under Target 16.5. Though corruption remains challenging to identify, measure, and combat, technology advances provide new opportunities to advance humanitarian goals, including the detection of corruption reported by the public. In this study, we address this challenge by developing a method using an unsupervised machine learning model to detect reports of corruption-related activity on the micro-blogging platform Twitter. In total, we collected over 6 million tweets containing keywords related to corruption between January and February 2019. We use the Biterm Topic Model to then isolate tweets from users who report corruption and found that most topics focus on police bribery and corruption in health-care. Though preliminary, these results shave the potential of identifying the scope and prevalence of corruption in society and also advance shared goals of combating corruption and advancing sustainable development in the 21st century. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/GHTC46095.2019.9033129 | IEEE Global Humanitarian Technology Conference Proceedings |
Keywords | DocType | ISSN |
Corruption,Machine Learning,Natural Language Processing,Topic modeling | Conference | 2377-6919 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiawei Li | 1 | 115 | 10.82 |
| Wen-Hao Chen | 2 | 64 | 7.74 |
| Qing Xu | 3 | 0 | 0.34 |
| Neal Shah | 4 | 0 | 0.34 |
| Timothy Mackey | 5 | 4 | 2.09 |