Algorithmic uncertainties in geolocating social media data for disaster management

Abstract

The emergence and rapid development of information and communications technology (ICT) have turned individuals into sensors, fostering the growth of human-generated geospatial big data.These geospatial big data, sometimes referred to as social sensing data, have been coupled with traditional spatial data and applied in various domains to understand society and the environment at multiple spatial and temporal scales. In disaster management, social sensing data, mainly social media data, have opened new avenues for observing human responses and behaviors under disasters in near real-time. Previous research relies on geographical information in geotags, content, and user profiles to locate social media messages. However, less than 1% of users share their locations through geotagging, leaving geolocating users through addresses in user profiles or the message content increasingly crucial in future location-based social media analysis and applications. This paper attempts to evaluate and visualize the margin of error incurred when using user profile locations or message-mentioned addresses to geolocate social media data for disaster research. Using Twitter data during the 2017 Hurricane Harvey as a case study, this research assessed the inconsistencies in predicting users' locations in various administrative units during each disaster phase using three geolocating strategies. The study provides insights into the relationship of spatial scales and conflated geolocating strategies, with the 'user from' method achieving the highest agreement at the country level (94.07%) compared to the 'tweet about' method. Interestingly, the findings indicate that geolocation accuracy remains relatively stable across the three disaster recovery phases. Moreover, during the preparedness phase, the agreement percentages between the 'tweet from' and 'user from' locations reach their peak, ranging from 95.1% at the country level to 40.3% at the county level. The paper rigorously quantifies the uncertainties associated with conflating geolocating methods for Twitter data for disaster management applications, underscoring the importance of accepting an appropriate level of uncertainty or using the three geolocating methods separately in future social media-based investigations. Furthermore, the study quantifies the trade-off between spatial scale and geolocation accuracy, revealing a decline in agreement between two geolocating methods as the geographical scale transitions from state to county, block group, 1-kilometer, and 30-meter levels.The potential impacts of uncertainties in geolocating Twitter data for disaster management applications were further unraveled. The findings offer valuable insights into selecting appropriate scales when applying different geolocating strategies in future social media-based investigations.