Didn’t roger that: Social media message complexity and situational awareness of emergency responders

Pogrebnyakov, Nicolai; Maldonado, Edgar

doi:10.1016/j.ijinfomgt.2018.02.004

Cited by 40 publications

(22 citation statements)

References 40 publications

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“…Facebook studies are also prevalent in the emergency response domain. Pogrebnyakov and Maldonado ( 2018 ) studied nearly a million messages from 997 Facebook pages of US police departments to study text complexity relative to emergency stage. For example, Sakaki et al ( 2010 ) used Twitter data to estimate a spatiotemporal model for earthquake events.…”

Section: Background Informationmentioning

confidence: 99%

The anatomy of natural disasters on online media: hurricanes and wildfires

et al. 2021

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Increasing prevalence and scale of natural disasters fuel the need for new approaches to evaluating, and eventually mitigating, their impact. This analysis quantifies and compares online and social media attention to hurricanes and wildfires over time and geographic space. Hurricanes studied included: Michael, Maria, Irma, Harvey, and Florence. Fires studied included: Woolsey, Mendocino, Carr, and Camp. It was hypothesized that total volume of online media content, measured in posts and mentions, varied measurably over the phases of the disasters. Furthermore, it was hypothesized that the anatomy of the disaster, specifically the number and timing/dates, of posts and mentions varied inside versus outside impacted zones/geographies. Social media content, in sheer volume, related to hurricanes was larger than that devoted to fires. A mismatch between the time periods that people post about natural disasters on social media and the times when aid is needed to rebuild was found. Mentions fell rapidly after landfall for hurricanes, and long before fires were officially contained or extinguished. This rapid fall in media attention may leave directly impacted populations without help and support during the rebuilding process. Greater understanding of volume of posts over time, or the anatomy of disasters in online media space, may help government agencies, private industry, and relief organizations understand public attentiveness before, during, and after various types of natural disasters.

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Section: Background Informationmentioning

confidence: 99%

The anatomy of natural disasters on online media: hurricanes and wildfires

et al. 2021

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“…Channel specification theory (CST) was adopted to extend the use of gratifications theory (GT) for understanding the cognitive process in selecting communications messages [119]. [112] proposed a general structural path model for understanding stakeholder relationships in normal times and crises while [120] and situational awareness (SA) model explored the relationship between social media message complexity and type.…”

Section: Q3 What Are the Related Crisis Communication Theoreticalmentioning

confidence: 99%

Crisis Informatics in the Context of Social Media Crisis Communication: Theoretical Models, Taxonomy, and Open Issues

et al. 2020

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The involvement application and use of crisis and emergency management and communication are increasing rapidly. This study conducts a systematic literature review to identify the development of theoretical models in the area of social media crisis communication and management. The study aims to review and analyse the relationship of social media-based crisis communication in the context of crisis informatics and its taxonomy and the related crisis communication theoretical models to derive the challenges and limitations. A total of 207 articles were selected for the evaluation based on quality, relevancy, and contribution. The findings revealed that the situational crisis communication theory (SCCT) is the most dominant theory, followed by social-mediated crisis communication (SMCC) and integrated crisis mapping (ICM) models. The study identified theories such as the STREMII model, social media crisis management matrix/framework (SMSMF), and an interactive crisis communication model (ICCM) as emerging models. Moreover, the result of the finding shows that stakeholder interaction is an understudied field, while information reliability and processing for decision-making purposes, the wider application of social media sites, privacy issues, and how social media interaction can improve community resilience or build stakeholders relationships remain suitable topics for future research.

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“…Without a dictionary NER becomes a non-trivial problem because of ambiguities associated with text structure, semantics and spelling [4]. These ambiguities are especially pronounced in user-generated content such as social media posts and online forum discussions, which can be particularly noisy [7,8].…”

Section: Related Workmentioning

confidence: 99%

Unsupervised domain-agnostic identification of product names in social media posts

Pogrebnyakov

2018

2018 IEEE International Conference on Big Data (Big Data)

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Product name recognition is a significant practical problem, spurred by the greater availability of platforms for discussing products such as social media and product review functionalities of online marketplaces. Customers, product manufacturers and online marketplaces may want to identify product names in unstructured text to extract important insights, such as sentiment, surrounding a product. Much extant research on product name identification has been domain-specific (e.g., identifying mobile phone models) and used supervised or semisupervised methods. With massive numbers of new products released to the market every year such methods may require retraining on updated labeled data to stay relevant, and may transfer poorly across domains. This research addresses this challenge and develops a domain-agnostic, unsupervised algorithm for identifying product names based on Facebook posts. The algorithm consists of two general steps: (a) candidate product name identification using an off-the-shelf pretrained conditional random fields (CRF) model, part-of-speech tagging and a set of simple patterns; and (b) filtering of candidate names to remove spurious entries using clustering and word embeddings generated from the data.

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Didn’t roger that: Social media message complexity and situational awareness of emergency responders

Cited by 40 publications

References 40 publications

The anatomy of natural disasters on online media: hurricanes and wildfires

The anatomy of natural disasters on online media: hurricanes and wildfires

Crisis Informatics in the Context of Social Media Crisis Communication: Theoretical Models, Taxonomy, and Open Issues

Unsupervised domain-agnostic identification of product names in social media posts

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