In the early stages of a disaster caused by a natural hazard (e.g., flood), the amount of available and useful information is low. To fill this informational gap, emergency responders are increasingly using data from geo-social media to gain insights from eyewitnesses to build a better understanding of the situation and design effective responses. However, filtering relevant content for this purpose poses a challenge. This work thus presents a comparison of different machine learning models (Naïve Bayes, Random Forest, Support Vector Machine, Convolutional Neural Networks, BERT) for semantic relevance classification of flood-related, German-language Tweets. For this, we relied on a four-category training data set created with the help of experts from human aid organisations. We identified fine-tuned BERT as the most suitable model, averaging a precision of 71% with most of the misclassifications occurring across similar classes. We thus demonstrate that our methodology helps in identifying relevant information for more efficient disaster management.

Twitter, one of the most important social media platforms, has been in the headlines regularly since its acquisition by Elon Musk in October 2022. This acquisition has had a strong impact on the employees, functionality, and discourse on Twitter. So far, however, there has been no analysis that examines the perception of the acquisition by the users on the platform itself. For this purpose, in this paper, we use georeferenced Tweets from the US and classify them using a polarity-based sentiment analysis. We find that the number of Tweets about Twitter and Elon Musk has increased significantly, as have negative sentiments on the subject. Using a spatial hot spot analysis, we find distinct centres of discourse, but no clear evidence of their significant change over time. On the West Coast, however, we suspect the first signs of polarisation, which could be an important indication for the future development of discourse on Twitter.

This study deals with the issue of greenwashing, i.e. the false portrayal of companies as environmentally friendly. The analysis focuses on the US metal industry, which is a major emission source of sulfur dioxide (SO₂), one of the most harmful air pollutants. One way to monitor the distribution of atmospheric SO₂ concentrations is through satellite data from the Sentinel-5P programme, which represents a major advance due to its unprecedented spatial resolution. In this paper, Sentinel-5P remote sensing data was combined with a plant-level firm database to investigate the relationship between the US metal industry and SO₂ concentrations using a spatial regression analysis. Additionally, this study considered web text data, classifying companies based on their websites in order to depict their self-portrayal on the topic of sustainability. In doing so, we investigated the topic of greenwashing, i.e. whether or not a positive self-portrayal regarding sustainability is related to lower local SO₂ concentrations. Our results indicated a general, positive correlation between the number of employees in the metal industry and local SO₂ concentrations. The web-based analysis showed that only 8% of companies in the metal industry could be classified as engaged in sustainability based on their websites. The regression analyses indicated that these self-reported "sustainable" companies had a weaker effect on local SO₂ concentrations compared to their "non-sustainable" counterparts, which we interpreted as an indication of the absence of general greenwashing in the US metal industry. However, the large share of firms without a website and lack of specificity of the text classification model were limitations to our methodology.

The widespread use of mobile phones and social media platforms provides valuable information about users’ behavior and activities. Mobile phone data are rich on positional information, but lack semantic context. Conversely, geo-social media data reveal users’ opinions and activities, but are rather sparse in space and time. In the context of emergency management, both data types have been considered separately. To exploit their complementary nature and potential for emergency management, this paper introduces a novel methodology for improving situational awareness with the focus on urban events. For crowd detection, a spatial hot spot analysis of mobile phone data is used. The analysis of geo-social media data involves building spatio-temporal topic-sentiment clusters of posts. The results of the spatio-temporal contextual enrichment include unusual crowds associated with topics and sentiments derived from the analyzed geo-social media data. This methodology is demonstrated using the case study of the Vienna Pride. The results show how crowds change over time in terms of their location, size, topics discussed, and sentiments.