A Multi-Dimensional Analysis of El Niño on Twitter: Spatial, Social, Temporal, and Semantic Perspectives

Ye, Xinyue; Wei, Xuebin

doi:10.3390/ijgi8100436

Cited by 13 publications

(7 citation statements)

References 40 publications

(68 reference statements)

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“…Their work was based on the extraction of data during and after the aforementioned natural disaster. Ye et al [52] conducted a multidimensional analysis of El Niño on Twitter: "Whenever Twitter users perceived what they thought were abnormal weather conditions, they immediately expressed their feelings and opinions on Twitter".…”

Section: Natural Disasters Big Data and Social Mediamentioning

confidence: 99%

Social-Media Analysis for Disaster Prevention: Forest Fire in Artenara and Valleseco, Canary Islands

Zamarreño-Aramendia

Cristòfol

Vela

et al. 2020

Journal of Open Innovation: Technology, Market, and Complexity

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This manuscript investigates the use of social media, specifically Twitter, during the forest fires in Artenara and Valleseco, Canary Islands, Spain, during summer 2019. The used methodology was big-data analysis through the Union Metrics and Twlets tools, as well as content analysis of posts related to the fires written by seven relevant accounts on the days when the fires were active, which was between 17 August and 26 September, when 9636.40 hectares were burned. The accounts selected for analysis were the following: Ángel Víctor Torres, autonomous president; Canary Islands Government; Civil Protection of Las Palmas; Military Emergency Unit of the Spanish Army; Delegation of the Spanish Government in the Canary Islands; Citizen’s Service of the Canary Islands Government; and the information account of the Security and Emergency area of the Canary Islands Government. The study concludes that the Canary Islands authorities did not use social media as a preventive element, but almost exclusively as a live-information channel. Future recommendations are presented for the management of social media during natural disasters.

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Section: Natural Disasters Big Data and Social Mediamentioning

confidence: 99%

Social-Media Analysis for Disaster Prevention: Forest Fire in Artenara and Valleseco, Canary Islands

Zamarreño-Aramendia

Cristòfol

Vela

et al. 2020

Journal of Open Innovation: Technology, Market, and Complexity

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“…The semantic analysis and text sentiment analysis model is based on machine learning [48,49]. The public opinion data are converted into a corresponding score index through semantic analysis [50]. The data are all objective public opinion attitudes toward certain aspects of the city to ensure an objective and timely evaluation.…”

Section: Analytical Frameworkmentioning

confidence: 99%

City Intelligence Quotient Evaluation System Using Crowdsourced Social Media Data: A Case Study of the Yangtze River Delta Region, China

Zhou

et al. 2021

IJGI

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Despite the trending studies on smart city development, how to evaluate the smartness of a city remains unclear. This research aimed to design a smart city evaluation system, named the City Intelligence Quotient (CityIQ) evaluation system, which considers both the hard (e.g., physical infrastructure) and soft sides (e.g., citizens’ perspectives) of smart city development. Based on the two-level structure of the CityIQ evaluation system (i.e., five dimensions and twenty indicators), a list of keywords was defined for automated information scraping in leading social media platforms to obtain volunteered geographic information. Semantic analysis was then used to update the CityIQ evaluations in a timely manner. Fifteen major cities in the Yangtze River Delta region, China, were selected for the empirical study, in which their smartness indices were calculated, traced and compared. Finally, suggestions for collaborative smart agglomerations were put forward. With the CityIQ evaluation system, policy makers can be informed of up-to-date changes in urban smartness levels and, thus, design context-specific collaborative policies to promote smart agglomerations.

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“…This is where the benefits of a thorough data pre-processing method, including translations and user location extraction, become relevant, which supports the more precise assessment of a post-event situation through extracting an additional information layer from the digital footprints of users by revealing contextual insights. This approach already exists in other fields, for example to uncover disaster footprints, but not in the case of social movements or unrest [36][37][38].…”

Section: Related Workmentioning

confidence: 99%

#AllforJan: How Twitter Users in Europe Reacted to the Murder of Ján Kuciak—Revealing Spatiotemporal Patterns through Sentiment Analysis and Topic Modeling

Kovács

Kovács-Györi

Resch

2021

IJGI

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Social media platforms such as Twitter are considered a new mediator of collective action, in which various forms of civil movements unite around public posts, often using a common hashtag, thereby strengthening the movements. After 26 February 2018, the #AllforJan hashtag spread across the web when Ján Kuciak, a young journalist investigating corruption in Slovakia, and his fiancée were killed. The murder caused moral shock and mass protests in Slovakia and in several other European countries, as well. This paper investigates how this murder, and its follow-up events, were discussed on Twitter, in Europe, from 26 February to 15 March 2018. Our investigations, including spatiotemporal and sentiment analyses, combined with topic modeling, were conducted to comprehensively understand the trends and identify potential underlying factors in the escalation of the events. After a thorough data pre-processing including the extraction of spatial information from the users’ profile and the translation of non-English tweets, we clustered European countries based on the temporal patterns of tweeting activity in the analysis period and investigated how the sentiments of the tweets and the discussed topics varied over time in these clusters. Using this approach, we found that tweeting activity resonates not only with specific follow-up events, such as the funeral or the resignation of the Prime Minister, but in some cases, also with the political narrative of a given country affecting the course of discussions. Therefore, we argue that Twitter data serves as a unique and useful source of information for the analysis of such civil movements, as the analysis can reveal important patterns in terms of spatiotemporal and sentimental aspects, which may also help to understand protest escalation over space and time.

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A Multi-Dimensional Analysis of El Niño on Twitter: Spatial, Social, Temporal, and Semantic Perspectives

Cited by 13 publications

References 40 publications

Social-Media Analysis for Disaster Prevention: Forest Fire in Artenara and Valleseco, Canary Islands

Social-Media Analysis for Disaster Prevention: Forest Fire in Artenara and Valleseco, Canary Islands

City Intelligence Quotient Evaluation System Using Crowdsourced Social Media Data: A Case Study of the Yangtze River Delta Region, China

#AllforJan: How Twitter Users in Europe Reacted to the Murder of Ján Kuciak—Revealing Spatiotemporal Patterns through Sentiment Analysis and Topic Modeling

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