Socially Enhanced Situation Awareness from Microblogs Using Artificial Intelligence: A Survey

Abstract

The rise of social media platforms provides an unbounded, infinitely rich source of aggregate knowledge of the world around us, both historic and real-time, from a human perspective. The greatest challenge we face is how to process and understand this raw and unstructured data, go beyond individual observations and see the “big picture”—the domain of Situation Awareness. We provide an extensive survey of Artificial Intelligence research, focusing on microblog social media data with applications to Situation Awareness, that gives the seminal work and state-of-the-art approaches across six thematic areas: Crime , Disasters , Finance , Physical Environment , Politics , and Health and Population . We provide a novel, unified methodological perspective, identify key results and challenges, and present ongoing research directions.

Twitter conversations predict the daily confirmed COVID-19 cases

“…Social media platforms, such as Facebook and Twitter, have an active user base of millions and hold an enormous amount of socially generated data through the exchange of conversations. These platforms have become an active source of information during day-to-day life as well as during mass emergencies such as the ongoing pandemic [5] . During mass emergencies, the number of user activities across these platforms increases exponentially, as people: (a) generate trends on search engines such as Google, (b) share their safety status or query the safety status of their near ones, and (c) also share what they have seen, felt, or heard.…”

“…During mass emergencies, the number of user activities across these platforms increases exponentially, as people: (a) generate trends on search engines such as Google, (b) share their safety status or query the safety status of their near ones, and (c) also share what they have seen, felt, or heard. These socially generated activities can be collected and analyzed for understanding the relationship between public discourse and how an emergency event unfolds at the ground level [5] . For example, in [35] , Chew et al.…”

“…The studies dealing with the early forecasts of confirmed cases, may it be COVID-19 or previous epidemics outbreaks, rely majorly on the “volume of conversations” feature, i.e. overall count, sentiment-based count, or a specific category-based count [5] . The issue with “volume of conversations” feature is its reliability and robustness.…”

“…With this consideration, this study attempts to incorporate the public discourse in designing pandemic-related time series forecasting models specially targeted for the steep-hill region of a pandemic’s ongoing wave . The modeling and early prediction of the prevalence of virus are essential to provide situational information to decision making bodies and authorities to estimate the requirements of resources and equipment needed to cope with the consequences of the virus [5] . This study, therefore, focuses on the forecast of COVID-19 spread while addressing the following research questions (RQs):

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Towards Automated Situational Awareness Reporting for Disaster Management—A Case Study

“…Evaluating microblogging sites such as Twitter has become essential to obtaining situational awareness in disaster situations [9][10][11][12][13][14][15]. As Phengsuwan et al pointed out in their Survey about the use of social media data in disaster management, "Several publications have proposed the exploitation of social media data for disaster management, with Twitter being one of the most significant social media data sources used for disaster management.…”