A Methodology for Automatic Acquisition of Flood‐event Management Information From Social Media: the Flood in Messinia, South Greece, 2016

Arapostathis, Stathis G.

doi:10.1007/s10796-021-10105-z

Cited by 18 publications

(10 citation statements)

References 33 publications

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“…Social media platforms play an integral role in the contemporary digitized world (Celik, 2019;Kapil & Ekbal, 2020;MacAvaney et al, 2019;Meske & Amojo, 2020). The data generated on these platforms enable data analytics and are valuable to companies, institutions, and individuals (Arapostathis, 2021;Shin et al, 2020;Vallejos et al, 2021). However, social media platforms also pose risks.…”

Section: Hate Speech On Social Media Platformsmentioning

confidence: 99%

Design Principles for User Interfaces in AI-Based Decision Support Systems: The Case of Explainable Hate Speech Detection

Meske

Bunde

2022

Inf Syst Front

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Hate speech in social media is an increasing problem that can negatively affect individuals and society as a whole. Moderators on social media platforms need to be technologically supported to detect problematic content and react accordingly. In this article, we develop and discuss the design principles that are best suited for creating efficient user interfaces for decision support systems that use artificial intelligence (AI) to assist human moderators. We qualitatively and quantitatively evaluated various design options over three design cycles with a total of 641 participants. Besides measuring perceived ease of use, perceived usefulness, and intention to use, we also conducted an experiment to prove the significant influence of AI explainability on end users’ perceived cognitive efforts, perceived informativeness, mental model, and trustworthiness in AI. Finally, we tested the acquired design knowledge with software developers, who rated the reusability of the proposed design principles as high.

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Section: Hate Speech On Social Media Platformsmentioning

confidence: 99%

Design Principles for User Interfaces in AI-Based Decision Support Systems: The Case of Explainable Hate Speech Detection

Meske

Bunde

2022

Inf Syst Front

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“…Even, research demonstrates that neural networks could have some comparative advantages, while used for classifying short text of specific topics [28,36] there are many algorithms that perform very well. Support vector machines (SVMs) are widely used for text classification [2,3,12,32,35] while there is demonstrated credibility in topics regarding natural disasters [30,31].…”

Section: Discussion: Conclusionmentioning

confidence: 99%

“…Additionally, the author, during the latest years, researched on manipulating exclusively social media information for generating seismic intensity maps, emerging thus the usefulness of the enormous, in terms of quantity, dynamic content that is constantly generated [3,4,5]. The output of author's previous research led, among others, to the identification of word patterns that can be used to classify the texts of social media posts according to the values of the macroseismic intensity scale.…”

Section: Introductionmentioning

confidence: 99%

Social Network Users Create Seismic Intensity Maps: An Automatic Approach of the Methodology

Arapostathis

2021

SN COMPUT. SCI.

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Main purpose of current article is to present current state-of-the-art, regarding a methodology for producing seismic intensity maps directly and exclusively from twitter data. The methodology had been presented previously, however, the vast majority of the processing procedures were based on manual work. In current state, an automatic approach is presented, aspiring thus to minimize the processing time needed which is quite enormous considering manual analytic processes. In specific, word patterns that had been identified, were used, to initially select tweet texts that contain macroseismic observations and for classifying them according to the corresponding values of the European macroseismic intensity scale (EMS98). As a next step machine learning was employed through the use of a long short term memory-recurrent neural network (LSTM-RNN) model, trained for removing ambiguity of potentially wrongly classified data. Sequentially, the classified information was geo-referenced by detecting geolocations contained within each tweet's text, while the precision of each georeference was indicated with a related value. Two georules improve the accuracy of the processed output, completing thus the extraction of macroseismic observations. Sequentially, the processed dataset was interpolated to create seismic intensity prediction surfaces at a raster format. The applied technique was the simple kriging, widely used for the creation of seismic intensity maps. As case study, the earthquake event of SW of Lesvos (M w = 6.3, 2017, Greece) was used. The output, was consisted of four seismic intensity maps, produced from macroseismic observations extracted from twitter, posted within 2, 6, 24 and total data period, respectively, since the earthquake event occurrence. The results were assessed through a comparison with official seismic intensity maps of the same earthquake published by seismological institutes and reality. Through the assessment, the main conclusions include significant steps towards the potential operational use of the methodology, as the produced output was close to reality while in some cases was able to be more precise than other approaches. Moreover, the time needed for data processing and for creating the final maps, has been limited drastically. Those facts emerge the potentials of integrating social network data approaches even in the most specialized scientific fields, like seismic intensity mapping.

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“…Wang et al (2021) found that Twitter use has a positive correlation with hurricane resiliency, suggesting that it can be a tool to enhance resilience. Machine learning techniques have revealed the temporal evolution of the various "stages" of Tweet applications in the context of a single disaster (Arapostathis, 2021), with other research calling for further integration of machine-learning-derived information in this regard (Dwarakanath et al, 2021).…”

Section: Social Media Effectivenessmentioning

confidence: 99%

Actionable Information in Flood Risk Communications and the Potential for New Web-Based Tools for Long-Term Planning for Individuals and Community

et al. 2022

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Because of its ubiquitous nature and catastrophic impacts, flood information should be readily available and continually evaluated, to maximize utility for the public and professionals. Web-based tools can fill existing needs for actionable information to inform decisions regarding flood damage mitigation for new and existing structures. The goal of this research is to identify the current capabilities, gaps, and future demands of Web-based flood communication systems. To understand the current and potential niche of Web-based flood tools, a review of the literature concerning the effectiveness of mass media, grassroots-based “citizen science” efforts, and social media in communicating flood risk is conducted. Then, the strengths of 50 major, freely-available, Web-based flood information portals are reviewed. Results suggest that mass media often provide actionable information, especially for short-term benefit immediately before, during, and immediately after the flood for individuals and community leaders. Citizen science and grassroots efforts encourage planning strategies to prevent or mitigate flood. Social media is most beneficial in raising awareness of the flood hazard and communicating short- and long-term mitigation and adaptation strategies. However, while mass media, citizen science, and social media have revolutionized the way that people plan for, survive, and recover from floods, their utility is largely restricted to addressing short-term information needs at the meso-scale or broader and/or conveying information about singular events to scientists and/or other professional interests. Actionable information to inform long-term planning and mitigating flood, for both the public and community leaders, remains lacking. A particular need is for communication mechanisms that satisfy several criteria simultaneously: wide broadcast, appealing delivery method, and focus on enhancing decision-making for long-term needs rather than solely for short-term benefit. Particularly useful would be a new, webtool that provides sufficient functionality to enhance flood risk reduction decision making, considering both the costs and benefits of mitigation.

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A Methodology for Automatic Acquisition of Flood‐event Management Information From Social Media: the Flood in Messinia, South Greece, 2016

Cited by 18 publications

References 33 publications

Design Principles for User Interfaces in AI-Based Decision Support Systems: The Case of Explainable Hate Speech Detection

Design Principles for User Interfaces in AI-Based Decision Support Systems: The Case of Explainable Hate Speech Detection

Social Network Users Create Seismic Intensity Maps: An Automatic Approach of the Methodology

Actionable Information in Flood Risk Communications and the Potential for New Web-Based Tools for Long-Term Planning for Individuals and Community

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