Understanding crisis communication on social media with CERC: topic model analysis of tweets about Hurricane Maria

2020 ◽  
pp. 1-22
Author(s):  
Xianlin Jin ◽  
Patric R. Spence
Keyword(s):  
Social Media ◽  
Crisis Communication ◽  
Topic Model ◽  
Model Analysis

Topics and topical phases in German social media communication during a disaster

2018 ◽  
Vol 24 (2) ◽  
pp. 221-264 ◽  
Author(s):  
SABINE GRÜNDER-FAHRER ◽  
ANTJE SCHLAF ◽  
GREGOR WIEDEMANN ◽  
GERHARD HEYER
Keyword(s):  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Disaster Management ◽  
Language Processing ◽  
Topic Model ◽  
Model Analysis ◽  
Media Content ◽  
Media Communication ◽  
Social Media Communication

AbstractSocial media are an emerging new paradigm in interdisciplinary research in crisis informatics. They bring many opportunities as well as challenges to all fields of application and research involved in the project of using social media content for an improved disaster management. Using the Central European flooding 2013 as our case study, we optimize and apply methods from the field ofnatural language processingand unsupervised machine learning to investigate the thematic and temporal structure of German social media communication. By means of topic model analysis, we will investigate which kind of content was shared on social media during the event. On this basis, we will, furthermore, investigate the development of topics over time and apply temporal clustering techniques to automatically identify different characteristic phases of communication. From the results, we, first, want to reveal properties of social media content and show what potential social media have for improving disaster management in Germany. Second, we will be concerned with the methodological issue of finding and adapting natural language processing methods that are suitable for analysing social media data in order to obtain information relevant for disaster management. With respect to the first, application-oriented focal point, our study reveals high potential of social media content in the factual, organizational and psychological dimension of the disaster and during all stages of the disaster management life cycle. Interestingly, there appear to be systematic differences in thematic profile between the different platforms Facebook and Twitter and between different stages of the event. In context of our methodological investigation, we claim that if topic model analysis is combined with appropriate optimization techniques, it shows high applicability for thematic and temporal social media analysis in disaster management.

scholarly journals Crisis Communication Patterns in Social Media during Hurricane Sandy

2018 ◽  
Vol 2672 (1) ◽  
pp. 125-137 ◽  
Author(s):  
Arif Mohaimin Sadri ◽  
Samiul Hasan ◽  
Satish V. Ukkusuri ◽  
Manuel Cebrian
Keyword(s):  
Social Media ◽  
Crisis Communication ◽  
Media Coverage ◽  
Topic Model ◽  
Original Data ◽  
Hurricane Sandy ◽  
Communication Patterns ◽  
Machine Learning Techniques ◽  
Power Outage ◽  
Response And Recovery

Hurricane Sandy was one of the deadliest and costliest of hurricanes of the past few decades. Many states experienced significant power outage; however, many people used social media to communicate while having limited or no access to traditional information sources. Using machine learning techniques, this study explored the evolution of various communication patterns and determined user concerns that emerged over the course of Hurricane Sandy. The original data included ∼52M tweets coming from ∼13M users between October 14, 2012 and November 12, 2012. A topic model was run on ∼763K tweets from the top 4,029 most frequent users who tweeted about Sandy at least 100 times. Some 250 well-defined communication patterns based on perplexity were identified. Conversations of the most frequent and relevant users indicate the evolution of numerous storm-phase (warning, response, and recovery) specific topics. People were also concerned about storm location and time, media coverage, and activities of political leaders and celebrities. Also presented is each relevant keyword that contributed to one particular pattern of user concerns. Such keywords would be particularly meaningful in targeted information-spreading and effective crisis communication in similar major disasters. Each of these words can also be helpful for efficient hash-tagging to reach the target audience as needed via social media. The pattern recognition approach of this study can be used in identifying real-time user needs in future crises.

scholarly journals Social Media in and Around a Temporary Large-Scale Refugee Shelter in the Netherlands

2021 ◽  
Vol 7 (2) ◽  
pp. 205630512110249
Author(s):  
Peer Smets ◽  
Younes Younes ◽  
Marinka Dohmen ◽  
Kees Boersma ◽  
Lenie Brouwer
Keyword(s):  
Social Media ◽  
The Netherlands ◽  
Crisis Communication ◽  
Asylum Seekers ◽  
Large Scale ◽  
State Policies ◽  
Refugee Crisis ◽  
Top Down ◽  
And Migration

During the 2015 refugee crisis in Europe, temporary refugee shelters arose in the Netherlands to shelter the large influx of asylum seekers. The largest shelter was located in the eastern part of the country. This shelter, where tents housed nearly 3,000 asylum seekers, was managed with a firm top-down approach. However, many residents of the shelter—mainly Syrians and Eritreans—developed horizontal relations with the local receiving society, using social media to establish contact and exchange services and goods. This case study shows how various types of crisis communication played a role and how the different worlds came together. Connectivity is discussed in relation to inclusion, based on resilient (non-)humanitarian approaches that link society with social media. Moreover, we argue that the refugee crisis can be better understood by looking through the lens of connectivity, practices, and migration infrastructure instead of focusing only on state policies.

The Value of Brand Fans during a Crisis: Exploring the Roles of Response Strategy, Source, and Brand Identification

2021 ◽  
pp. 232948842199969
Author(s):  
Hayoung Sally Lim ◽  
Natalie Brown-Devlin
Keyword(s):  
Social Media ◽  
Experimental Design ◽  
Crisis Communication ◽  
Word Of Mouth ◽  
Crisis Response ◽  
Response Strategy ◽  
Brand Identification ◽  
Communication Source ◽  
Practical Implications ◽  
Selection Of

Using a two (crisis response strategy: diminish vs. rebuild) × three (source: brand organization vs. brand executive vs. brand fan) experimental design, this study examines how brand fans (i.e., consumers who identify with a brand) can be prompted to protect a brand’s reputation during crises and how the selection of a crisis spokesperson can influence consumers’ evaluations of the crisis communication. Being buffers for their preferred brands, brand fans are more likely to accept their brand’s crisis response and engage in positive electronic word-of-mouth on social media. Brand fans are more likely to evaluate other brand fan’s social media accounts as a credible crisis communication source, whereas those who are not brand fans are more likely to evaluate brand and/or brand executives as credible. Findings provide theoretical applications in paracrisis literature pertaining to social media but also practical implications for brand managers to strategically utilize brand fans in crisis communication.

scholarly journals Perceiving Residents’ Festival Activities Based on Social Media Data: A Case Study in Beijing, China

2021 ◽  
Vol 10 (7) ◽  
pp. 474
Author(s):  
Bingqing Wang ◽  
Bin Meng ◽  
Juan Wang ◽  
Siyu Chen ◽  
Jian Liu
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Topic Model ◽  
Central Area ◽  
Classification Model ◽  
Social Media Data ◽  
Ring Road ◽  
Different Types ◽  
Spatial Differences ◽  
Media Data

Social media data contains real-time expressed information, including text and geographical location. As a new data source for crowd behavior research in the era of big data, it can reflect some aspects of the behavior of residents. In this study, a text classification model based on the BERT and Transformers framework was constructed, which was used to classify and extract more than 210,000 residents’ festival activities based on the 1.13 million Sina Weibo (Chinese “Twitter”) data collected from Beijing in 2019 data. On this basis, word frequency statistics, part-of-speech analysis, topic model, sentiment analysis and other methods were used to perceive different types of festival activities and quantitatively analyze the spatial differences of different types of festivals. The results show that traditional culture significantly influences residents’ festivals, reflecting residents’ motivation to participate in festivals and how residents participate in festivals and express their emotions. There are apparent spatial differences among residents in participating in festival activities. The main festival activities are distributed in the central area within the Fifth Ring Road in Beijing. In contrast, expressing feelings during the festival is mainly distributed outside the Fifth Ring Road in Beijing. The research integrates natural language processing technology, topic model analysis, spatial statistical analysis, and other technologies. It can also broaden the application field of social media data, especially text data, which provides a new research paradigm for studying residents’ festival activities and adds residents’ perception of the festival. The research results provide a basis for the design and management of the Chinese festival system.

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