More Like this

1. A Twitter Data Credibility Framework—Hurricane Harvey as a Use Case

   https://doi.org/10.3390/ijgi8030111  

   Yang, Jingchao; Yu, Manzhu; Qin, Han; Lu, Mingyue; Yang, Chaowei (March 2019, ISPRS International Journal of Geo-Information)

   Social media data have been used to improve geographic situation awareness in the past decade. Although they have free and openly availability advantages, only a small proportion is related to situation awareness, and reliability or trustworthiness is a challenge. A credibility framework is proposed for Twitter data in the context of disaster situation awareness. The framework is derived from crowdsourcing, which states that errors propagated in volunteered information decrease as the number of contributors increases. In the proposed framework, credibility is hierarchically assessed on two tweet levels. The framework was tested using Hurricane Harvey Twitter data, in which situation awareness related tweets were extracted using a set of predefined keywords including power, shelter, damage, casualty, and flood. For each tweet, text messages and associated URLs were integrated to enhance the information completeness. Events were identified by aggregating tweets based on their topics and spatiotemporal characteristics. Credibility for events was calculated and analyzed against the spatial, temporal, and social impacting scales. This framework has the potential to calculate the evolving credibility in real time, providing users insight on the most important and trustworthy events. 

   more » « less
2. Estimating Hourly Population Distribution Patterns at High Spatiotemporal Resolution in Urban Areas Using Geo-Tagged Tweets and Dasymetric Mapping

   https://doi.org/10.4230/LIPIcs.GIScience.2021.I.10  

   Park, Jaehee (September 2020, 11th International Conference on Geographic Information Science (GIScience 2021))

   null (Ed.)

   This paper introduces a spatiotemporal analysis framework for estimating hourly changing population distribution patterns in urban areas using geo-tagged tweets (the messages containing users’ geospatial locations), land use data, and dasymetric maps. We collected geo-tagged social media (tweets) within the County of San Diego during one year (2015) by using Twitter’s Streaming Application Programming Interfaces (APIs). A semi-manual Twitter content verification procedure for data cleaning was applied first to separate tweets created by humans from non-human users (bots). The next step was to calculate the number of unique Twitter users every hour within census blocks. The final step was to estimate the actual population by transforming the numbers of unique Twitter users in each census block into estimated population densities with spatial and temporal factors using dasymetric maps. The temporal factor was estimated based on hourly changes of Twitter messages within San Diego County, CA. The spatial factor was estimated by using the dasymetric method with land use maps and 2010 census data. Comparing to census data, our methods can provide better estimated population in airports, shopping malls, sports stadiums, zoo and parks, and business areas during the day time. 

   more » « less
3. Social Media Analytics for Disaster Response: Classification and Geospatial Visualization Framework

   https://doi.org/10.3390/app15084330  

   He, Chao; Hu, Da (April 2025, Applied Sciences)

   Social media has become an indispensable resource in disaster response, providing real-time crowdsourced data on public experiences, needs, and conditions during crises. This user-generated content enables government agencies and emergency responders to identify emerging threats, prioritize resource allocation, and optimize relief operations through data-driven insights. We present an AI-powered framework that combines natural language processing with geospatial visualization to analyze disaster-related social media content. Our solution features a text analysis model that achieved an 81.4% F1 score in classifying Twitter/X posts, integrated with an interactive web platform that maps emotional trends and crisis situations across geographic regions. The system’s dynamic visualization capabilities allow authorities to monitor situational developments through an interactive map, facilitating targeted response coordination. The experimental results show the model’s effectiveness in extracting actionable intelligence from Twitter/X posts during natural disasters. 

   more » « less
4. Analyzing the Sentiment of Crowd for Improving the Emergency Response Services

   https://doi.org/10.1109/SMARTCOMP.2018.00067  

   Singh, Neha; Roy, Nirmalya; Gangopadhyay, Aryya (June 2018, 2018 IEEE International Conference on Smart Computing (SMARTCOMP))

   Twitter is an extremely popular micro-blogging social platform with millions of users, generating thousands of tweets per second. The huge amount of Twitter data inspire the researchers to explore the trending topics, event detection and event tracking which help to postulate the fine-grained details and situation awareness. Obtaining situational awareness of any event is crucial in various application domains such as natural calamities, man made disaster and emergency responses. In this paper, we advocate that data analytics on Twitter feeds can help improve the planning and rescue operations and services as provided by the emergency personnel in the event of unusual circumstances. We take a different approach and focus on the users' emotions, concerns and feelings expressed in tweets during the emergency situations, and analyze those feelings and perceptions in the community involved during the events to provide appropriate feedback to emergency responders and local authorities. We employ sentiment analysis and change point detection techniques to process, discover and infer the spatiotemporal sentiments of the users. We analyze the tweets from recent Las Vegas shooting (Oct. 2017) and note that the changes in the polarity of the sentiments and articulation of the emotional expressions, if captured successfully can be employed as an informative tool for providing feedback to EMS. 

   more » « less
5. Analyzing the Emotions of Crowd for Improving the Emergency Response Services

   https://doi.org/https://doi.org/10.1016/j.pmcj.2019.04.009  

   Neha Singh, Nirmalya Roy (August 2019, Pervasive and mobile computing)

   Twitter is an extremely popular micro-blogging social platform with millions of users, generating thousands of tweets per second. The huge amount of Twitter data inspire the researchers to explore the trending topics, event detection and event tracking which help to postulate the fine-grained details and situation awareness. Obtaining situational awareness of any event is crucial in various application domains such as natural calamities, man-made disaster and emergency responses. In this paper, we advocate that data analytics on Twitter feeds can help improve the planning and rescue operations and services as provided by the emergency personnel in the event of unusual circumstances. We take an emotional change detection approach and focus on the users’ emotions, concerns and feelings expressed in tweets during the emergency situations, and analyze those feelings and perceptions in the community involved during the events to provide appropriate feedback to emergency responders and local authorities. We employ improved emotion analysis and change point detection techniques to process, discover and infer the spatiotemporal sentiments of the users. We analyze the tweets from recent Las Vegas shooting (Oct. 2017) and note that the changes in the polarity of the sentiments and articulation of the emotional expressions, if captured successfully can be employed as an informative tool for providing feedback to EMS. 

   more » « less