In a practical experiment, we benchmark five common text classification algorithms – Naive Bayes, Logistic Regression, Support Vector Machines, Random Forests, and eXtreme Gradient Boosting – on multiple misinformation datasets, accounting for both data-rich and data-poor environments. We test these methods by repeatedly reducing the sizes of training data, thus creating 435 AI models in total. From these models we make observations on the data require- ments, the training times that such models might require in practice, and how the availability on these things impact accuracy and algorithm choices in practical scenarios. We then discuss the implications and avenues of further research.
Exploring AI’s transformative potential: LIRNEasia launches ‘AI for Social Good’ workshop series
LIRNEasia concluded its first workshop (30 March, 24’), its first and part of a larger series of initiatives on ‘Artificial Intelligence for Social Good’; intended to raise interest and awareness on the potential of AI to benefit society at large. The event featured a keynote speech by Dr.
Use of online job portal data in research and in practice: A review
Last year we conducted research to explore the possibility of leveraging online job portal data for economic analysis in 13 Asia Pacific countries, as a part of a project for the Asian Development Bank. We examined the types of information available on major portals across the region, to discern the nature and format of available data.
Data science for social good
On 2nd October 2023, Research Manager and Team Lead (Data, Algorithms, and Policy) Merl Chandana, alongside Junior Researcher Chanuka Algama, held a session titled ‘Applied data science research for social good’ at the University of Kelaniya’s Department of Statistics and Computer Science. The session delved into LIRNEasia’s journey of forming a data science team and using large datasets to yield critical insights for public policy.
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