Analyzing the EU Migration Crisis as Reflected on Twitter

Mukherjee, Sagnik; Hauthal, Eva; Burghardt, Dirk

doi:10.1007/s42489-022-00114-6

Cited by 7 publications

(2 citation statements)

References 29 publications

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“…The second spatial analysis of typicality used a 100 by 100 kilometer grid for analysis and visualization. This system follows the methods used in Mukherjee et al (2022) which demonstrate the usefulness of spatial typicality visualizations for interpretations. For each of the 100 selected emojis, a choropleth map was generated by assigning each grid cell a color according to the typicality value of the emoji in that location.…”

Section: Figurementioning

confidence: 99%

Visualizing emoji usage in geo-social media across time, space, and topic

Levi,

Hauthal,

Mukherjee

et al. 2024

Front. Commun.

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Social media is ubiquitous in the modern world and its use is ever-increasing. Similarly, the use of emojis within social media posts continues to surge. Geo-social media produces massive amounts of spatial data that can provide insights into users' thoughts and reactions across time and space. This research used emojis as an alternative to text-based social media analysis in order to avoid the common obstacles of natural language processing such as spelling mistakes, grammatical errors, slang, and sarcasm. Because emojis offer a non-verbal means to express thoughts and emotions, they provide additional context in comparison to purely text-based analysis. This facilitates cross-language studies. In this study, the spatial and temporal usage of emojis were visualized in order to detect relevant topics of discussion within a Twitter dataset that is not thematically pre-filtered. The dataset consists of Twitter posts that were geotagged within Europe during the year 2020. This research leveraged cartographic visualization techniques to detect spatial-temporal changes in emoji usage and to investigate the correlation of emoji usage with significant topics. The spatial and temporal developments of these topics and their respective emojis were visualized as a series of choropleth maps and map matrices. This geovisualization technique allowed for individual emojis to be independently analyzed and for specific spatial or temporal trends to be further investigated. Emoji usage was found to be spatially and temporally heterogeneous, and trends in emoji usage were found to correlate with topics including the COVID-19 pandemic, several political movements, and leisure activities.

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Section: Figurementioning

confidence: 99%

Visualizing emoji usage in geo-social media across time, space, and topic

Levi,

Hauthal,

Mukherjee

et al. 2024

Front. Commun.

Self Cite

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“…Social media provides rich information sources about disaster situations. The extraction and analysis of social media information based on NLP are new means of assessing disaster phenomena and public opinion (Atefeh & Khreich, 2015; Fan et al, 2021; Mukherjee et al, 2022). Compared to traditional means (e.g., surveys, field interviews, etc.…”

Section: Background and Related Workmentioning

confidence: 99%

Social media insights on public perception and sentiment during and after disasters: The European floods in 2021 as a case study

Li,

Haunert,

Knechtel

et al. 2023

Transactions in GIS

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Detecting and collecting public opinion via social media can provide near real‐time information to decision‐makers, which plays a vital role in urban disaster management and sustainable development. However, there has been little work focusing on identifying the perception and the sentiment polarity expressed by users during and after disasters, particularly regional flood events. In this article, we comprehensively analyze tweets data related to the “European floods in 2021” over time, topic, and sentiment, forming a complete workflow from data processing, topic modeling, sentiment analysis, and topic and sentiment prediction. The aim is to address the following research questions: (1) What are the public perception and main concerns during and after floods? (2) How does the public sentiment change during and after floods? Results indicate that there is a significant correlation between a flood's trend and the heat of corresponding tweets. The three topics that receive the most public concern are: (1) climate change and global warming; (2) praying for the victims: and (3) disaster situations and information. Negative sentiments are predominant during the floods and will continue for some time. We tested five different classifiers, of which TextCNN‐attention turned out to deliver the best predictions in topic and sentiment prediction, and performed well for sparse flood tweets, it can be used to predict the topic and sentiment polarity of a single tweet in real‐time during the flood events. Our findings can help disaster agencies to better understand the dynamics of social networks and develop stronger situational awareness towards a disaster, which can contribute to scientifically justified decision‐making in urban risk management and also meet the challenges associated with the global sustainable development goal 11 (SDGs) on Sustainable Cities and Communities.

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Geospatial Data Literacy: Considering the Multisensory Perspective

Edler

Dickmann

2023

RaumFragen: Stadt – Region – Landschaft

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Analyzing the EU Migration Crisis as Reflected on Twitter

Cited by 7 publications

References 29 publications

Visualizing emoji usage in geo-social media across time, space, and topic

Visualizing emoji usage in geo-social media across time, space, and topic

Social media insights on public perception and sentiment during and after disasters: The European floods in 2021 as a case study

Geospatial Data Literacy: Considering the Multisensory Perspective

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