scholarly journals Linking picture with text: tagging flood relevant tweets for rapid flood inundation mapping

2019 ◽  
Vol 2 ◽  
pp. 1-6 ◽  
Author(s):  
Xiao Huang ◽  
Cuizhen Wang ◽  
Zhenlong Li
Keyword(s):  
Social Media ◽  
Real Time ◽  
Visual Information ◽  
Flood Event ◽  
Disaster Studies ◽  
Automatic Tagging ◽  
Inundation Mapping ◽  
Flood Inundation Mapping ◽  
Media Platform ◽  
Textual Approach

<p><strong>Abstract.</strong> Recent years have seen the growth of popularity in social media, especially in social media based disaster studies. During a flood event, volunteers may contribute useful information regarding the extent and the severity of a flood in a real-time manner, largely facilitating the process of rapid inundation mapping. However, considering that ontopic (flood related) social media only comprises a small amount in the entire social media space, a robust extraction method is in great need. Taking Twitter as targeted social media platform, this study presents a visual-textual approach to automatic tagging flood related tweets in order to achieve real-time flood mapping. Two convolutional neural networks are adopted to process pictures and text separately. Their outputs are further combined and fed to a visual-textual fused classifier. The result suggests that additional visual information from pictures leads to better classification accuracy and the extracted tweets, representing timely documentation of flood event, can greatly benefit a variety of flood mitigation approaches.</p>

scholarly journals Social media as an information source for rapid flood inundation mapping

2015 ◽  
Vol 15 (12) ◽  
pp. 2725-2738 ◽  
Author(s):  
J. Fohringer ◽  
D. Dransch ◽  
H. Kreibich ◽  
K. Schröter
Keyword(s):  
Social Media ◽  
Information Source ◽  
Remote Sensing Data ◽  
Related Information ◽  
Inundation Mapping ◽  
Flood Inundation Mapping ◽  
Disaster Data ◽  
Inundation Extent ◽  
Available Information

Abstract. During and shortly after a disaster, data about the hazard and its consequences are scarce and not readily available. Information provided by eyewitnesses via social media is a valuable information source, which should be explored in a~more effective way. This research proposes a methodology that leverages social media content to support rapid inundation mapping, including inundation extent and water depth in the case of floods. The novelty of this approach is the utilization of quantitative data that are derived from photos from eyewitnesses extracted from social media posts and their integration with established data. Due to the rapid availability of these posts compared to traditional data sources such as remote sensing data, areas affected by a flood, for example, can be determined quickly. The challenge is to filter the large number of posts to a manageable amount of potentially useful inundation-related information, as well as to interpret and integrate the posts into mapping procedures in a timely manner. To support rapid inundation mapping we propose a methodology and develop "PostDistiller", a tool to filter geolocated posts from social media services which include links to photos. This spatial distributed contextualized in situ information is further explored manually. In an application case study during the June 2013 flood in central Europe we evaluate the utilization of this approach to infer spatial flood patterns and inundation depths in the city of Dresden.

scholarly journals Social media as an information source for rapid flood inundation mapping

2015 ◽  
Vol 3 (7) ◽  
pp. 4231-4264 ◽  
Author(s):  
J. Fohringer ◽  
D. Dransch ◽  
H. Kreibich ◽  
K. Schröter
Keyword(s):  
Social Media ◽  
Information Source ◽  
Remote Sensing Data ◽  
Related Information ◽  
Inundation Mapping ◽  
Flood Inundation Mapping ◽  
Disaster Data ◽  
Inundation Extent ◽  
Available Information

Abstract. During and shortly after a disaster data about the hazard and its consequences are scarce and not readily available. Information provided by eye-witnesses via social media are a valuable information source, which should be explored in a more effective way. This research proposes a methodology that leverages social media content to support rapid inundation mapping, including inundation extent and water depth in case of floods. The novelty of this approach is the utilization of quantitative data that are derived from photos from eye-witnesses extracted from social media posts and its integration with established data. Due to the rapid availability of these posts compared to traditional data sources such as remote sensing data, for example areas affected by a flood can be determined quickly. The challenge is to filter the large number of posts to a manageable amount of potentially useful inundation-related information as well as their timely interpretation and integration in mapping procedures. To support rapid inundation mapping we propose a methodology and develop a tool to filter geo-located posts from social media services which include links to photos. This spatial distributed contextualized in-situ information is further explored manually. In an application case study during the June 2013 flood in central Europe we evaluate the utilization of this approach to infer spatial flood patterns and inundation depths in the city of Dresden.

Young People's Reasons for Feeling Suicidal

Crisis ◽  
2019 ◽  
Vol 40 (6) ◽  
pp. 400-406 ◽  
Author(s):  
Aamina Ali ◽  
Kerry Gibson
Keyword(s):  
Social Media ◽  
Young People ◽  
Real Time ◽  
Suicide Prevention ◽  
Youth Suicide ◽  
Social Media Platform ◽  
Data Limitations ◽  
The Social ◽  
Media Platform ◽  
The Moment

Abstract. Background: While considerable attention has been given to explanations for youth suicide, less is known about the reasons that young people themselves give for suicidality. Research on online communications gives an opportunity to investigate the real-time reasons young people give for feeling suicidal. Aims: This study aimed to identify the reasons that young people provide for feeling suicidal in posts published on a suicide prevention forum, hosted on the social media platform Tumblr. Method: We filtered 2 months' worth of posts to identify those that related specifically to suicide. In total, 210 posts were thematically analyzed to identify the reasons given for suicidality and the meanings associated with these. Results: Six main reasons for suicidality were identified in the analysis: feeling lonely and socially disconnected, experiencing identity stigma, failing to meet expectations, being helpless, feeling worthless, and experiences of mental ill-health. Limitations: There are advantages as well as limitations associated with relying on Internet-based data. Limitations include the inability to establish participant demographics and the lack of context for posts. Conclusion: Suicide prevention efforts should target the reasons that young people give for feeling suicidal in the moment of crisis in order to engage this population more effectively.

A probabilistic model for real-time flood warning based on deterministic flood inundation mapping

2011 ◽  
Vol 26 (7) ◽  
pp. 1079-1089 ◽  
Author(s):  
Jiun-Huei Jang ◽  
Pao-Shan Yu ◽  
Sen-Hai Yeh ◽  
Jin-Cheng Fu ◽  
Chen-Jia Huang
Keyword(s):  
Real Time ◽  
Probabilistic Model ◽  
Flood Inundation ◽  
Flood Warning ◽  
Inundation Mapping ◽  
Flood Inundation Mapping

scholarly journals VARTTA: A Visual Analytics System for Making Sense of Real-Time Twitter Data

Data ◽  
2020 ◽  
Vol 5 (1) ◽  
pp. 20
Author(s):  
Amir Haghighati ◽  
Kamran Sedig
Keyword(s):  
Social Media ◽  
Real Time ◽  
Visual Analytics ◽  
Time Data ◽  
Making Sense ◽  
Twitter Data ◽  
Social Media Platforms ◽  
Media Platform ◽  
Analytical Tools ◽  
Data Visualizations

Through social media platforms, massive amounts of data are being produced. As a microblogging social media platform, Twitter enables its users to post short updates as “tweets” on an unprecedented scale. Once analyzed using machine learning (ML) techniques and in aggregate, Twitter data can be an invaluable resource for gaining insight into different domains of discussion and public opinion. However, when applied to real-time data streams, due to covariate shifts in the data (i.e., changes in the distributions of the inputs of ML algorithms), existing ML approaches result in different types of biases and provide uncertain outputs. In this paper, we describe VARTTA (Visual Analytics for Real-Time Twitter datA), a visual analytics system that combines data visualizations, human-data interaction, and ML algorithms to help users monitor, analyze, and make sense of the streams of tweets in a real-time manner. As a case study, we demonstrate the use of VARTTA in political discussions. VARTTA not only provides users with powerful analytical tools, but also enables them to diagnose and to heuristically suggest fixes for the errors in the outcome, resulting in a more detailed understanding of the tweets. Finally, we outline several issues to be considered while designing other similar visual analytics systems.

scholarly journals Sequential Data Assimilation for Real-Time Probabilistic Flood Inundation Mapping

2021 ◽  
Author(s):  
Keighobad Jafarzadegan ◽  
Peyman Abbaszadeh ◽  
Hamid Moradkhani
Keyword(s):  
Decision Making ◽  
Data Assimilation ◽  
Point Source ◽  
Real Time ◽  
Sequential Data ◽  
Flood Inundation ◽  
Modeling Framework ◽  
Inundation Mapping ◽  
Flood Inundation Mapping ◽  
Sequential Data Assimilation

Abstract. Real-time probabilistic flood inundation mapping is crucial for flood risk warning and decision making during the emergency of an upcoming flood event. Considering high uncertainties involved in the modeling of a nonlinear and complex flood event, providing a deterministic flood inundation map can be erroneous and misleading for reliable and timely decision making. The conventional flood hazard maps provided for different return periods cannot also represent the actual dynamics of flooding rivers. Therefore, a real-time modeling framework that forecasts the inundation areas before the onset of an upcoming flood is of paramount importance. Sequential Data Assimilation (DA) techniques are well-known for real-time operation of physical models while accounting for existing uncertainties. In this study, we present a Data Assimilation (DA)-hydrodynamic modeling framework where multiple gauge observations are integrated into the LISFLOOD-FP model to improve its performance. This study utilizes the Ensemble Kalman Filter (EnKF) in a multivariate fashion for dual estimation of model state variables and parameters where the correlations among point source observations are taken into account. First, a synthetic experiment is designed to assess the performance of the proposed approach, then the method is used to simulate the Hurricane Harvey flood in 2017. Our results indicate that the multivariate assimilation of point-source observations into hydrodynamic models can improve the accuracy and reliability of probabilistic flood inundation mapping by 5–7% while it also provides the basis for sequential updating and real-time flood inundation mapping.

scholarly journals Sequential data assimilation for real-time probabilistic flood inundation mapping

2021 ◽  
Vol 25 (9) ◽  
pp. 4995-5011
Author(s):  
Keighobad Jafarzadegan ◽  
Peyman Abbaszadeh ◽  
Hamid Moradkhani
Keyword(s):  
Decision Making ◽  
Data Assimilation ◽  
Point Source ◽  
Real Time ◽  
Sequential Data ◽  
Flood Inundation ◽  
Modeling Framework ◽  
Inundation Mapping ◽  
Flood Inundation Mapping ◽  
Sequential Data Assimilation

Abstract. Real-time probabilistic flood inundation mapping is crucial for flood risk warning and decision-making during the emergency period before an upcoming flood event. Considering the high uncertainties involved in the modeling of a nonlinear and complex flood event, providing a deterministic flood inundation map can be erroneous and misleading for reliable and timely decision-making. The conventional flood hazard maps provided for different return periods cannot also represent the actual dynamics of flooding rivers. Therefore, a real-time modeling framework that forecasts the inundation areas before the onset of an upcoming flood is of paramount importance. Sequential data assimilation (DA) techniques are well known for real-time operation of physical models while accounting for existing uncertainties. In this study, we present a DA hydrodynamic modeling framework where multiple gauge observations are integrated into the LISFLOOD-FP model to improve its performance. This study utilizes the ensemble Kalman filter (EnKF) in a multivariate fashion for dual estimation of model state variables and parameters where the correlations among point source observations are taken into account. First, a synthetic experiment is designed to assess the performance of the proposed approach; then the method is used to simulate the Hurricane Harvey flood in 2017. Our results indicate that the multivariate assimilation of point source observations into hydrodynamic models can improve the accuracy and reliability of probabilistic flood inundation mapping by 5 %–7 %, while it also provides the basis for sequential updating and real-time flood inundation mapping.

Real-Time Sentiment Analysis of Microblog Messages with the Maltego “Tweet Analyzer” Machine

2017 ◽  
pp. 316-337
Author(s):  
Shalin Hai-Jew
Keyword(s):  
Social Media ◽  
Open Access ◽  
Sentiment Analysis ◽  
Real Time ◽  
Sensor Network ◽  
Fast Moving ◽  
Social Media Platform ◽  
Other Information ◽  
Media Platform ◽  
The Web

For social media to work as a “human sensor network” or a relevant source for “eventgraphing” for some fast-moving events, it is important to be capture real-time and locational information. It may help to not only capture information from a particular social media platform but from across the Web. In such a context, Maltego Carbon 3.5.3/Chlorine 3.6.0's Tweet Analyser “machine” (with AlchemyAPI built-in) and used in combination with other “transforms,” may serve the purpose—at least for initial and iterated sampling of the related messaging. This tool may be used to capture information from social media accounts.. social media accounts, linked URLs, geolocational information, and other information of research value. Maltego is an open-access tool with a community version and a proprietary commercial version available by subscription. Maltego Chlorine's Tweet Analyzer has a built-in sentiment analysis feature.

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