This paper examines the energy consumption in correlation with social media activity density and socio economic statistics. By ana�lyzing the aggregated Twitter data, the users can be classi�ed into two categories, local and tourist user. Spatial mapping of district�based data has shown a signi�cant correlation between Twitter activities, energy consumption and socio economics. Additionally,
there was a considerable statistical relationship between Twitter activities and energy consumption as the results of Panel Data regression. A further analysis of social media activities and socio�economic statistics by using step wise regression method leads to a far-reaching statistical model to infer the energy consumption in Bali with an accuracy rate higher than 95%. The number of tourists visiting as well as the population density of each district are highly
contribute to the changing pattern of electricity consumption in the province of Bali.