Exploring the Scope of Using News Articles to Understand Development Patterns of Districts in India
Mehak Gupta, Shayan Saifi, Konark Verma, Kumari Rekha, Aaditeshwar, Seth

TL;DR
This paper proposes using unsupervised learning on news articles to uncover unmeasured factors influencing district development in India, highlighting the importance of qualitative data in socio-economic modeling.
Contribution
It introduces a novel approach to analyze unstructured news data to identify relevant development variables and understand district disparities.
Findings
News articles can reveal unmeasured development factors.
Unsupervised methods can rank and analyze district-related news.
Qualitative data enhances socio-economic development models.
Abstract
Understanding what factors bring about socio-economic development may often suffer from the streetlight effect, of analyzing the effect of only those variables that have been measured and are therefore available for analysis. How do we check whether all worthwhile variables have been instrumented and considered when building an econometric development model? We attempt to address this question by building unsupervised learning methods to identify and rank news articles about diverse events occurring in different districts of India, that can provide insights about what may have transpired in the districts. This can help determine whether variables related to these events are indeed available or not to model the development of these districts. We also describe several other applications that emerge from this approach, such as to use news articles to understand why pairs of districts that…
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Taxonomy
TopicsMedia Influence and Politics
