Storm Warnings: Time Sensitive Proximity

Although social media industry is now a very congested Marketplace, Twitter continues to maintain its status as a popular social media platform. There are 330 million monthly active users and 145 million daily active users on Twitter sending more than 6,000 tweets every second in the world. In Spain case 85% population are social media users, with around 5 million tweeter profiles for a population around 47 million. In the autonomous community of Basque country (2.17 million inhabitants) around 20% of citizens use Twitter.Twitter is a social tool that enables users to post messages (tweets) of up to 280 characters supporting a wide variety of social communication practices including photo and video attach. The Basque Meteorology Agency @Euskalmet with more than 115,3 K followers is one of the most popular accounts in Basque Country. Twitter is not only an opportunity to instantaneous spread messages to people without intermediaries, but also as a potential platform for valuable data acquisition using tweeter API capabilities. In this contribution, we present a study of different aspects related to the operational use of Twitter data in the context of high impact weather scenarios at local level.The most important activity in Euskalmet are actions in severe weather events. Before the event, mainly centered in forecast and communication, during the event in nowcast, surveillance and impact monitoring and after the event in post-event analysis. During all these complex processes real time tweets posted by local users offer a huge amount of data that conveniently processed could be useful for different purposes. For operational staff, working at office during severe weather episodes, is critical to understand the local effects that an adverse phenomenon is causing and the correct perception of the extent of impact and social alarm. For this purposes, among others, different information associated with posted tweets can be extracted and exploited conveniently. In this work, we present some results that demonstrate how different data mining and advances analytics techniques can be used in order to include social media data information for different tasks and particularly during high impact weather events.In this paper we summarize our experience during a proof of concept project for automatic real time tweeter analysis and the development of an operational tool for tweeter API data exploitation in the Basque Country. We present the main challenges and problems that we have had to face, including how to deal with the lack of geolocation information, since in the case of the Basque country, as in other parts of the world, tweets containing geotags are the exception, not the rule.

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No Place to Hide: Inadvertent Location Privacy Leaks on Twitter

Proceedings on Privacy Enhancing Technologies ◽

10.2478/popets-2019-0064 ◽

2019 ◽

Vol 2019 (4) ◽

pp. 172-189

Author(s):

Jonathan Rusert ◽

Osama Khalid ◽

Dat Hong ◽

Zubair Shafiq ◽

Padmini Srinivasan

Keyword(s):

United States ◽

Social Media ◽

Location Privacy ◽

Naive Bayes ◽

The United States ◽

Sensitive Information ◽

Online Social Media ◽

Points Of Interest ◽

Bayes Algorithm ◽

Share Information

Abstract There is a natural tension between the desire to share information and keep sensitive information private on online social media. Privacy seeking social media users may seek to keep their location private by avoiding the mentions of location revealing words such as points of interest (POIs), believing this to be enough. In this paper, we show that it is possible to uncover the location of a social media user’s post even when it is not geotagged and does not contain any POI information. Our proposed approach Jasoos achieves this by exploiting the shared vocabulary between users who reveal their location and those who do not. To this end, Jasoos uses a variant of the Naive Bayes algorithm to identify location revealing words or hashtags based on both temporal and atemporal perspectives. Our evaluation using tweets collected from four different states in the United States shows that Jasoos can accurately infer the locations of close to half a million tweets corresponding to more than 20,000 distinct users (i.e., more than 50% of the test users) from the four states. Our work demonstrates that location privacy leaks do occur despite due precautions by a privacy conscious user. We design and evaluate countermeasures based Jasoos to mitigate location privacy leaks.

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Initial results of the project SINOPTICA (Satellite-borne and IN-situ Observations to Predict The Initiation of Convection for ATM)

10.5194/ems2021-189 ◽

2021 ◽

Author(s):

Laura Esbri ◽

Maria Carmen Llasat ◽

Tomeu Rigo ◽

Massimo Milelli ◽

Vincenzo Mazzarella ◽

...

Keyword(s):

Weather Prediction ◽

Wrf Model ◽

Remote Sensing Data ◽

Weather Forecast ◽

Relevant Information ◽

Air Traffic ◽

Severe Weather ◽

Radar Images ◽

Weather Events ◽

Adverse Weather

In the framework of the SINOPTICA project (EU H2020 SESAR, 2020 &#8211; 2022), different meteorological forecasting techniques are being tested to better nowcast severe weather events affecting Air Traffic Management (ATM) operations. Short-range severe weather forecasts with very high spatial resolution will be obtained starting from radar images, through an application of nowcasting techniques combined with Numerical Weather Prediction (NWP) model with data assimilation. The final goal is to integrate compact nowcast information into an Arrival Manager to support Air Traffic Controllers (ATCO) when sequencing and guiding approaching aircraft even in adverse weather situations. The guidance-support system will enable the visualization of dynamic weather information on the radar display of the controller, and the 4D-trajectory calculation for diversion coordination around severe weather areas. This meteorological information must be compact and concise to not interfere with other relevant information on the radar display of the controller.Three severe weather events impacting different Italian airports have been selected for a preliminary radar analysis. Some products are considered for obtaining the best radar approach to characterize the severity of the events for ATM interests. Combining the Vertical Integrated Liquid and the Echo Top Maximum products, hazard thresholds are defined for different domains around the airports. The Weather Research and Forecasting (WRF) model has been used to simulate the formation and development of the aforementioned convective events. In order to produce a more accurate very short-term weather forecast (nowcasting), remote sensing data (e.g. radar, GNSS) and conventional observations are assimilated by using a cycling three-dimensional variational technique. This contribution presents some preliminary results on the progress of the project.

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Weather events identification in social media streams: tools to detect their evidence in Twitter

10.7287/peerj.preprints.2241 ◽

2017 ◽

Author(s):

Valentina Grasso ◽

Imad Zaza ◽

Federica Zabini ◽

Gianni Pantaleo ◽

Paolo Nesi ◽

...

Keyword(s):

Social Media ◽

Data Science ◽

Social Impact ◽

Graphical Representation ◽

Severe Weather ◽

Social Media Data ◽

Twitter Data ◽

Weather Events ◽

Crisis Events ◽

Media Data

Severe weather impact identification and monitoring through social media data is a good challenge for data science. In last years we assisted to an increase of natural disasters, also due to climate change. Many works showed that during such events people tend to share specific messages by of mean of social media platforms, especially Twitter. Not only they contribute to"situational" awareness also improving the dissemination of information during emergency but can be used to assess social impact of crisis events. We present in this work preliminary findings concerning how temporal distribution of weather related messages may help the identification of severe events that impacted a community. Severe weather events are recognizable by observing the synchronization of twitter streams volumes concerning extractions by using different but semantically graduate terms and hash-tags including the specific containing geo-content names. Impacting events seems immediately recognizable by graphical representation of weather streams and when the time-line show a specific parallel-wise pattern that we named "Half Onion Shape". Different but weather semantically linked twitter streams could exhibits different magnitude, in order to their term popularity, but they show, when a weather event occurs, the same temporal relative maximum. In reason of to these interesting indications, that needs to be confirmed through more deeper analysis, and of the great use of social media, as Twitter, during crisis events it's becoming fundamental to have a suite of suitable tools to monitor social media data. For Twitter data a comprehensive suite of tools is presented: the DISIT-Twitter Vigilance Platform for twitter data retrieve,management and visualization.

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Navigating Monsters: Credibility in the Twittersphere

Proceedings from the Document Academy ◽

10.35492/docam/7/1/10 ◽

2020 ◽

Vol 7 (1) ◽

Author(s):

Carrie Boettcher

Keyword(s):

Situation Awareness ◽

Information Behavior ◽

Relevant Information ◽

Severe Weather ◽

Dynamic Information ◽

Weather Events ◽

Cognitive Authority ◽

High Level ◽

Information Landscape

The increasing use of OSM during emergency, or potentially threatening, situations creates conditions in which emergency planners and responders need a high level of investigative skill to weed through a dynamic information landscape to determine the quality of information to contribute to improved situation awareness. This weeding process transforms the big data environment of OSM to focused information retrieval. Inquiry into indicators of quality in OSM (authority, objectivity, currency, coverage, and glyphicality) during severe weather situations informs how OSM impacts the information behavior of the severe weather enterprise of the U. S. Specifically, this paper focuses on investigation into how a particular element of the severe weather enterprise in the Midwest, the integrated warning team (IWT), identifies relevant information in OSM during severe weather events. This paper describes the theoretical framework of an inquiry into information behavior of the IWT during severe weather events through the lens of cognitive authority theory (Wilson, 1983) and Bonnici’s (2016) CAF-QIS for understanding the phenomena of both credibility and trustworthiness in the Twittersphere where author is potentially unknown.

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Weather events identification in social media streams: tools to detect their evidence in Twitter

10.7287/peerj.preprints.2241v1 ◽

2016 ◽

Cited By ~ 2

Author(s):

Valentina Grasso ◽

Imad Zaza ◽

Federica Zabini ◽

Gianni Pantaleo ◽

Paolo Nesi ◽

...

Keyword(s):

Social Media ◽

Data Science ◽

Social Impact ◽

Graphical Representation ◽

Severe Weather ◽

Social Media Data ◽

Twitter Data ◽

Weather Events ◽

Crisis Events ◽

Media Data

Severe weather impact identification and monitoring through social media data is a good challenge for data science. In last years we assisted to an increase of natural disasters, also due to climate change. Many works showed that during such events people tend to share specific messages by of mean of social media platforms, especially Twitter. Not only they contribute to"situational" awareness also improving the dissemination of information during emergency but can be used to assess social impact of crisis events. We present in this work preliminary findings concerning how temporal distribution of weather related messages may help the identification of severe events that impacted a community. Severe weather events are recognizable by observing the synchronization of twitter streams volumes concerning extractions by using different but semantically graduate terms and hash-tags including the specific containing geo-content names. Impacting events seems immediately recognizable by graphical representation of weather streams and when the time-line show a specific parallel-wise pattern that we named "Half Onion Shape". Different but weather semantically linked twitter streams could exhibits different magnitude, in order to their term popularity, but they show, when a weather event occurs, the same temporal relative maximum. In reason of to these interesting indications, that needs to be confirmed through more deeper analysis, and of the great use of social media, as Twitter, during crisis events it's becoming fundamental to have a suite of suitable tools to monitor social media data. For Twitter data a comprehensive suite of tools is presented: the DISIT-Twitter Vigilance Platform for twitter data retrieve,management and visualization.

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Weather Radar Spatiotemporal Saliency: A First Look at an Information Theory–Based Human Attention Model Adapted to Reflectivity Images

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-16-0092.1 ◽

2017 ◽

Vol 34 (1) ◽

pp. 137-152 ◽

Cited By ~ 1

Author(s):

David Schvartzman ◽

Sebastián Torres ◽

Tian-You Yu

Keyword(s):

Information Theory ◽

Weather Radar ◽

Relevant Information ◽

Severe Weather ◽

Radar Reflectivity ◽

Cognitive Resources ◽

Analysis Tool ◽

Attention Model ◽

Human Attention ◽

Weather Events

AbstractForecasters often monitor and analyze large amounts of data, especially during severe weather events, which can be overwhelming. Thus, it is important to effectively allocate their finite perceptual and cognitive resources for the most relevant information. This paper introduces a novel analysis tool that quantifies the amount of spatial and temporal information in time series of constant-elevation weather radar reflectivity images. The proposed Weather Radar Spatiotemporal Saliency (WR–STS) is based on the mathematical model of the human attention system (referred to as saliency) adapted to radar reflectivity images and makes use of information theory concepts. It is shown that WR-STS highlights spatially and temporally salient (attention attracting) regions in weather radar reflectivity images, which can be associated with meteorologically important regions. Its skill in highlighting current regions of interest is assessed by analyzing the WR-STS values within regions in which severe weather is likely to strike in the near future as defined by National Weather Service forecasters. The performance of WR-STS is demonstrated for a severe weather case and analyzed for a set of 10 diverse cases. Results support the hypothesis that WR-STS can identify regions with meteorologically important echoes and could assist in discerning fast-changing, highly structured weather echoes during complex severe weather scenarios, ultimately allowing forecasters to focus their attention and spend more time analyzing those regions.

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Weather events identification in social media streams: tools to detect their evidence in Twitter

10.7287/peerj.preprints.2241v2 ◽

2017 ◽

Author(s):

Valentina Grasso ◽

Imad Zaza ◽

Federica Zabini ◽

Gianni Pantaleo ◽

Paolo Nesi ◽

...

Keyword(s):

Social Media ◽

Data Science ◽

Social Impact ◽

Graphical Representation ◽

Severe Weather ◽

Social Media Data ◽

Twitter Data ◽

Weather Events ◽

Crisis Events ◽

Media Data

Severe weather impact identification and monitoring through social media data is a good challenge for data science. In last years we assisted to an increase of natural disasters, also due to climate change. Many works showed that during such events people tend to share specific messages by of mean of social media platforms, especially Twitter. Not only they contribute to"situational" awareness also improving the dissemination of information during emergency but can be used to assess social impact of crisis events. We present in this work preliminary findings concerning how temporal distribution of weather related messages may help the identification of severe events that impacted a community. Severe weather events are recognizable by observing the synchronization of twitter streams volumes concerning extractions by using different but semantically graduate terms and hash-tags including the specific containing geo-content names. Impacting events seems immediately recognizable by graphical representation of weather streams and when the time-line show a specific parallel-wise pattern that we named "Half Onion Shape". Different but weather semantically linked twitter streams could exhibits different magnitude, in order to their term popularity, but they show, when a weather event occurs, the same temporal relative maximum. In reason of to these interesting indications, that needs to be confirmed through more deeper analysis, and of the great use of social media, as Twitter, during crisis events it's becoming fundamental to have a suite of suitable tools to monitor social media data. For Twitter data a comprehensive suite of tools is presented: the DISIT-Twitter Vigilance Platform for twitter data retrieve,management and visualization.

Download Full-text