Wang, Q., Sailor, H. B., Lee, K. A., Ma, K., Goh, K. H., & Boh, W. F. (2024). Using Twitter Dataset for Social Listening in Singapore. IEEE Access, 1–1. https://doi.org/10.1109/access.2024.3427760
Abstract:
As a highly urbanized nation, Singapore faces unique urban planning challenges due to its
geographical attributes and demographics. These include optimizing land and transportation, enhancing
quality of life, and preparing for pandemics. Quick responses and understanding of region-specific social
voices are essential for effective policy-making and real-time insights into local dynamics. This work delves
into analyzing social media data sourced from Twitter within the context of Singapore, forming a crucial
component of a broader social listening initiative. Specifically, 96.7 million tweets from 2008 to 2023 were
collected using Twitter’s free API, providing a decade’s worth of social data from Singapore. Alongside the
Twitter data, we release a list of 10,357 places and property names with geographic coordinates, mapped
to 332 subzones and 55 planning areas in Singapore. In this paper, we further present examples of locating
methods that enable region-specific analysis of different urban zones, gathering information reflecting the
attitudes of citizens associated with each estate. We showcase the practical application of the dataset through
two distinct use cases: sentiment analysis on the prevalent issue of COVID-19 and bursty topic detection
during the years 2020 and 2021. Deep learning-based methods are employed for the analysis: sentiment
analysis using a zero-shot pretrained model and bursty topic analysis based on the biterm topic model. The
experimental analysis demonstrates the efficacy of social listening, providing valuable insights for future
city planning in other countries and cities. This work offers invaluable resources and methodologies for
the research community, highlighting the potential of social media data in enhancing urban planning and
policy-making. The data is released at https://doi.org/10.21979/N9/PALUID
License type:
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Funding Info:
This work was supported by project Assessing Cities: A SCoRE (Societal Comprehensive Reflective Estimate) Methodology
This research / project is supported by the National Research Foundation & Ministry of National Development - Cities of Tomorrow Research and Development Program
Grant Reference no. : COT-CityScan-2020-1