This was a test of the utility of Twitter during a couple of weather events. The test was performed by the Arkansas Geographic Information Office. The kml can be downloaded from http://bit.ly/bKzf5T
This document summarizes how the speaker hacked into a government training website and downloaded training videos without authentication. They describe their multiple unsuccessful attempts to report the vulnerability to the government via email, Twitter, and other means. The document suggests ways for the government to improve security, such as implementing a bug bounty program, conducting code audits, and having a vulnerability management program. It concludes that the government is currently unresponsive to security issues and that improvements are needed to address vulnerabilities.
How I Hacked The Government And Got Away With ItSteven Hatfield
In this presentation I spoke about how I found an information disclosure vulnerability within a Federal CyberSecurity training website, was able to report it responsibly, and the issues that came up as I attempted to do so.
Chad Stewart is an aerospace engineer seeking a career in the space industry. He has a background in communications systems from his work as an electronics technician in the Navy maintaining RF equipment. He has a bachelor's degree in aerospace engineering with a concentration in astronautics and relevant skills in MATLAB, STK, ANSYS, and additive manufacturing.
Weather events identification in social media streams: tools to detect their ...Alfonso Crisci
- The document discusses tools and methods for detecting weather events using social media data, specifically Twitter.
- It describes analyzing Twitter streams related to weather over 4 years to extract metrics and detect impactful weather events in order to increase situational awareness for weather services.
- Key findings include that semantic tuning of Twitter search queries is important to obtain suitable data, and that different search strategies are needed to detect different types of weather events based on their duration and impacts.
The document discusses various cloud computing options for hosting geospatial data and applications, including infrastructure as a service (IaaS), platform as a service (PaaS), and data as a service (DaaS). It provides examples of companies using clouds services like Esri, Google, and The RITRE Corporation. The key points are to understand your goals, evaluate provider capabilities, and do your own research to select the best cloud solution.
The document is a presentation by Learon Dalby about his work with the Arkansas Geographic Information Office (AGIO). Over the past 14 years, AGIO has developed and improved geographic data and services for Arkansas, including statewide road centerlines, parcel data for 46 counties, and address points for 49 counties. Dalby discusses the growth of AGIO from an initial staff of 1 to its current status. He also addresses challenges around keeping data and applications up to date and making them accessible to both government agencies and the public.
This presentation was provided in 2009 and is certainly temporal, given the nature of the discussion.
Presentation roughly 20 minutes and discussion ensued.
This document summarizes how the speaker hacked into a government training website and downloaded training videos without authentication. They describe their multiple unsuccessful attempts to report the vulnerability to the government via email, Twitter, and other means. The document suggests ways for the government to improve security, such as implementing a bug bounty program, conducting code audits, and having a vulnerability management program. It concludes that the government is currently unresponsive to security issues and that improvements are needed to address vulnerabilities.
How I Hacked The Government And Got Away With ItSteven Hatfield
In this presentation I spoke about how I found an information disclosure vulnerability within a Federal CyberSecurity training website, was able to report it responsibly, and the issues that came up as I attempted to do so.
Chad Stewart is an aerospace engineer seeking a career in the space industry. He has a background in communications systems from his work as an electronics technician in the Navy maintaining RF equipment. He has a bachelor's degree in aerospace engineering with a concentration in astronautics and relevant skills in MATLAB, STK, ANSYS, and additive manufacturing.
Weather events identification in social media streams: tools to detect their ...Alfonso Crisci
- The document discusses tools and methods for detecting weather events using social media data, specifically Twitter.
- It describes analyzing Twitter streams related to weather over 4 years to extract metrics and detect impactful weather events in order to increase situational awareness for weather services.
- Key findings include that semantic tuning of Twitter search queries is important to obtain suitable data, and that different search strategies are needed to detect different types of weather events based on their duration and impacts.
The document discusses various cloud computing options for hosting geospatial data and applications, including infrastructure as a service (IaaS), platform as a service (PaaS), and data as a service (DaaS). It provides examples of companies using clouds services like Esri, Google, and The RITRE Corporation. The key points are to understand your goals, evaluate provider capabilities, and do your own research to select the best cloud solution.
The document is a presentation by Learon Dalby about his work with the Arkansas Geographic Information Office (AGIO). Over the past 14 years, AGIO has developed and improved geographic data and services for Arkansas, including statewide road centerlines, parcel data for 46 counties, and address points for 49 counties. Dalby discusses the growth of AGIO from an initial staff of 1 to its current status. He also addresses challenges around keeping data and applications up to date and making them accessible to both government agencies and the public.
This presentation was provided in 2009 and is certainly temporal, given the nature of the discussion.
Presentation roughly 20 minutes and discussion ensued.
The document analyzes Twitter data during Hurricane Sandy to understand communication flows. It finds that tweet volume increased as the storm hit but geotagging decreased, possibly due to cell network issues. Keyword analysis showed tweets mentioning "Sandy" rose dramatically. Maps showed tweet locations and densities increased in affected areas. Network analysis revealed interactions between general accounts and those mentioning "weather." Proper data handling, geocoding analysis, deduplication and device platform understanding are needed for effective emergency communication analysis.
Information extraction from social media: A linguistically motivated approachAli Hürriyetoğlu
We propose a flexible method for extracting traffic information from social media. The abundance of
microposts on Twitter make it possible to tap into what is going on as users are reporting on what they
are actually observing. This information is highly relevant as it can help traffic security organizations
and drivers to be better prepared and take appropriate action. Distinguishing 22 information categories
deemed relevant to the traffic domain, we achieve a success rate of 74% when individual tweets are
considered. This performance we judge to be satisfactory, seeing that there are usually multiple tweets
about a given event so that we will pick up what relevant information is out there.
This document describes a project to develop a system that searches for the top-k social media users from large amounts of geo-tagged social media data. The system would take as input a location, distance, and keywords, and output the top users who have posted content relevant to the keywords near the given location. It discusses collecting and analyzing Twitter data using APIs, parsing and storing the data, generating a user interaction graph, analyzing and scoring data based on location, keyword relevance, and popularity to identify top users, and processing top-k queries from users. The project is estimated to take two months to complete and will be developed using Python, PostgreSQL, and web front-end tools.
This document describes a study that aimed to develop a real-time traffic classification system using Twitter data in Yogyakarta, Indonesia. The study collected over 110,000 tweets, preprocessed them, extracted features, and used machine learning classifiers like Naive Bayes, Support Vector Machine, and Decision Tree to classify tweets as related to traffic or not. Experimental results showed that for balanced datasets, SVM achieved the best performance of 99.77% accuracy, while for imbalanced datasets, SVM also performed best with 99.87% accuracy. The study demonstrates the potential of using social media data for real-time traffic anomaly detection.
This paper analyzes social media conversations around the TomorrowWorld music festival through two Twitter data sets collected a month apart. It finds that the first data set focused mainly on performances from this year's festival, while the second shifted to next year's event. The paper also examines the Twitter account @belugaPOD and recommends increasing interactions with important users and involvement in smaller conversations to improve their presence. Google Analytics showed most important website visitors came from SoundCloud. Overall, the paper aims to understand social media discussions of TomorrowWorld and how to enhance @belugaPOD's online and social media presence.
Iaetsd real time event detection and alert system using sensorsIaetsd Iaetsd
This document proposes a real-time earthquake detection system using Twitter data and sensors. Tweets containing keywords related to earthquakes are analyzed using machine learning classifiers to detect earthquake events. A particle filtering algorithm is used to estimate the location and trajectory of earthquakes based on geotagged tweets. The system was able to detect earthquakes faster than traditional methods and send alerts to registered users.
Liveblogging, mobile journalism and verificationPaul Bradshaw
The document discusses the rise of continuous news reporting across multiple platforms using new technologies like live blogging and mobile journalism. It provides examples of how various organizations have adopted these practices to report on events like elections. It also discusses tools, techniques, challenges and ethics of verifying information and reporting in real-time from mobile devices.
IRJET- An Experimental Evaluation of Mechanical Properties of Bamboo Fiber Re...IRJET Journal
This document discusses tweet segmentation and its application to named entity recognition. It proposes a novel framework called Hybrid Segmentation that segments tweets into meaningful phrases by maximizing the stickiness score of candidate segments based on global context from external sources like Wikipedia and local context from linguistic features within a batch of tweets. Two methods are developed - one using local linguistic features and the other using local term dependency. Experimental results on two tweet datasets show improved segmentation quality when using both global and local contexts compared to global context alone. The segmented tweets achieve higher accuracy for named entity recognition compared to word-based methods, demonstrating the benefit of tweet segmentation for downstream natural language processing applications.
IRJET- Tweet Segmentation and its Application to Named Entity RecognitionIRJET Journal
This document summarizes a research paper on tweet segmentation and its application to named entity recognition. It proposes a novel framework called Hybrid Segmentation that splits tweets into meaningful segments to preserve semantic context for downstream natural language processing applications like named entity recognition. HybridSeg finds optimal tweet segmentations by maximizing the stickiness score of segments, which considers global context based on English phrases and local context based on linguistic features or term dependencies within a batch of tweets. Experiments show significantly improved segmentation quality when learning both global and local contexts compared to global context alone. The segmented tweets can then be used for high accuracy named entity recognition through part-of-speech tagging.
An Update on Drone Regulations - EU U-Space & US UTM ...Kevin O'Donovan
An update on Drone Regulations - EU U-Space & US UTM update delivered at CIGRE Israel UAV Seminar on 30th July in Tel Aviv.
Posted as is without audio.
For any comments/questions, contact me on LinkedIN or Twitter.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
WWW2010_Earthquake Shakes Twitter User: Analyzing Tweets for Real-Time Event...tksakaki
Twitter, a popular microblogging service, has received much
attention recently. An important characteristic of Twitter
is its real-time nature. For example, when an earthquake
occurs, people make many Twitter posts (tweets) related
to the earthquake, which enables detection of earthquake
occurrence promptly, simply by observing the tweets. As
described in this paper, we investigate the real-time interaction
of events such as earthquakes in Twitter and propose
an algorithm to monitor tweets and to detect a target
event. To detect a target event, we devise a classifier of
tweets based on features such as the keywords in a tweet,
the number of words, and their context. Subsequently, we
produce a probabilistic spatiotemporal model for the target
event that can find the center and the trajectory of the
event location. We consider each Twitter user as a sensor
and apply Kalman filtering and particle filtering, which are
widely used for location estimation in ubiquitous/pervasive
computing. The particle filter works better than other comparable
methods for estimating the centers of earthquakes
and the trajectories of typhoons. As an application, we construct
an earthquake reporting system in Japan. Because of
the numerous earthquakes and the large number of Twitter
users throughout the country, we can detect an earthquake
with high probability (96% of earthquakes of Japan Meteorological
Agency (JMA) seismic intensity scale 3 or more
are detected) merely by monitoring tweets. Our system detects
earthquakes promptly and sends e-mails to registered
users. Notification is delivered much faster than the announcements
that are broadcast by the JMA.
Twitter is now an established and a widely popular news medium. Be it normal banter or a discussion on high impact events like Boston marathon blasts, February 2014 US Icestorm, etc., people use Twitter to get updates and also broadcast their thoughts and views. Twitter bots have today become very common and acceptable. People are using them to get updates about emergencies like natural disasters, terrorist strikes, etc., users also use them for getting updates about different places and events, both local and global. Twitter bots provide these users a means to perform certain tasks on Twitter that are both simple and structurally repetitive, at a much higher rate than what would be possible for a human alone. During high impact events these Twitter bots tend to provide a time critical and a comprehensive information source with information aggregated form various different sources. In this study, we present how these bots participate in discussions and augment them during high impact events. We identify bots in 5 high impact events for 2013: Boston blasts, February 2014 US Icestorm, Washington Navy Yard Shooting, Oklahoma tornado, and Cyclone Phailin. We identify bots among top tweeters by getting all such accounts manually annotated. We then study their activity and present many important insights. We determine the impact bots have on information diffusion during these events and how they tend to aggregate and broker information from various sources to different users. We also analyzed their tweets, list down important differentiating features between bots and non bots (normal or human accounts) during high impact events. We also show how bots are slowly moving away from traditional API based posts towards web automation platforms like IFTTT, dlvr.it, etc. Using standard machine learning, we proposed a methodology to identify bots/non bots in real time during high impact events. This study also looks into how the bot scenario has changed by comparing data from high impact events from 2013 against data from similar type of events from 2011. Bots active in high impact events generally don't spread malicious content. Lastly, we also go through an in-depth analysis of Twitter bots who were active during 2013 Boston Marathon Blast. We show how bots because of their programming structure don't pick up rumors easily during these events and even if they do; they do it after a long time.
Event detection in twitter using text and image fusioncsandit
In this paper, we describe an accurate and effective event detection method to detect events from
Twitter stream. It detects events using visual information as well as textual information to improve
the performance of the mining. It monitors Twitter stream to pick up tweets having texts and photos
and stores them into database. Then it applies mining algorithm to detect the event. Firstly, it detects
event based on text only by using the feature of the bag-of-words which is calculated using the term
frequency-inverse document frequency (TF-IDF) method. Secondly, it detects the event based on
image only by using visual features including histogram of oriented gradients (HOG) descriptors,
grey-level co-occurrence matrix (GLCM), and color histogram. K nearest neighbours (Knn)
classification is used in the detection. Finally, the final decision of the event detection is made based
on the reliabilities of text only detection and image only detection. The experiment result showed that
the proposed method achieved high accuracy of 0.93, comparing with 0.89 with texts only, and 0.86
with images only.
The document summarizes an MSR presentation on rumor detection on real-time Twitter data using supervised learning. It discusses introducing rumor detection and reviewing literature on current methods. The proposed work involves collecting Twitter data, preprocessing it, extracting features, and classifying tweets using techniques like decision trees and SVM. It aims to accurately detect rumors on Twitter in real-time by analyzing sentiment and verifying information with news sources. The implementation strategy and environment are also outlined along with conclusions and future work.
This document describes a research project analyzing real-time geotagged tweets to detect trending topics based on location. The project involved collecting geotagged tweets from Twitter using an API, preprocessing the tweets, and applying techniques to detect trending topics for specific geographic locations. The detected trending topics were then compared to topics displayed on other social media platforms. The goal was to automatically identify emerging topics being discussed on Twitter in real-time based on location to better understand information flow and user behavior patterns.
Semantic Twitter Analyzing Tweets For Real Time Event Notificationokazaki117
This document proposes a system to analyze tweets semantically in real-time to detect and notify users of events. It describes using Twitter to detect earthquakes in Japan by classifying tweets about earthquakes vs other topics using machine learning. The system would send email alerts to registered users about detected earthquakes near their location. It also discusses expanding the system to detect rainbows or celebrity sightings from tweets. The goal is to leverage Twitter's real-time nature and semantic analysis to provide timely notifications about events from social media data.
For my final year project I used data analysis techniques to investigate user behavior pattern recognition in respect of similar interests and culture versus offline geographical location. This was an out-of-the-box topic, which I selected due to my love on Data Analysis, in respect of the Social Network Analysis in the Internet era.
The document discusses the importance of companies using their own web mapping applications to demonstrate quality and capabilities. It notes that by using their own product, companies can identify issues and actively work to fix them. Additionally, when others also use the application, especially the right others to solve business problems, it will lead the application to get better and help the company survive. The presentation emphasizes how dogfooding a product helps ensure it works as intended and meets user needs.
This document appears to be a presentation by Learon Dalby on the topic of "Where are we going & who is 'we'". Some of the key points discussed include:
- The contributing factors that have led procurement, management structures, and project schedules to become barriers to progress.
- How approaches to procurement, software/applications, and data management have evolved since the 1950s and the challenges faced by the rapid adoption of technology.
- Questions around whether existing approaches to creating and maintaining large amounts of data have actually led to effective use and problem solving.
- The potential benefits of refocusing on solving problems directly rather than being constrained by existing procurement and software/infrastructure approaches.
The document analyzes Twitter data during Hurricane Sandy to understand communication flows. It finds that tweet volume increased as the storm hit but geotagging decreased, possibly due to cell network issues. Keyword analysis showed tweets mentioning "Sandy" rose dramatically. Maps showed tweet locations and densities increased in affected areas. Network analysis revealed interactions between general accounts and those mentioning "weather." Proper data handling, geocoding analysis, deduplication and device platform understanding are needed for effective emergency communication analysis.
Information extraction from social media: A linguistically motivated approachAli Hürriyetoğlu
We propose a flexible method for extracting traffic information from social media. The abundance of
microposts on Twitter make it possible to tap into what is going on as users are reporting on what they
are actually observing. This information is highly relevant as it can help traffic security organizations
and drivers to be better prepared and take appropriate action. Distinguishing 22 information categories
deemed relevant to the traffic domain, we achieve a success rate of 74% when individual tweets are
considered. This performance we judge to be satisfactory, seeing that there are usually multiple tweets
about a given event so that we will pick up what relevant information is out there.
This document describes a project to develop a system that searches for the top-k social media users from large amounts of geo-tagged social media data. The system would take as input a location, distance, and keywords, and output the top users who have posted content relevant to the keywords near the given location. It discusses collecting and analyzing Twitter data using APIs, parsing and storing the data, generating a user interaction graph, analyzing and scoring data based on location, keyword relevance, and popularity to identify top users, and processing top-k queries from users. The project is estimated to take two months to complete and will be developed using Python, PostgreSQL, and web front-end tools.
This document describes a study that aimed to develop a real-time traffic classification system using Twitter data in Yogyakarta, Indonesia. The study collected over 110,000 tweets, preprocessed them, extracted features, and used machine learning classifiers like Naive Bayes, Support Vector Machine, and Decision Tree to classify tweets as related to traffic or not. Experimental results showed that for balanced datasets, SVM achieved the best performance of 99.77% accuracy, while for imbalanced datasets, SVM also performed best with 99.87% accuracy. The study demonstrates the potential of using social media data for real-time traffic anomaly detection.
This paper analyzes social media conversations around the TomorrowWorld music festival through two Twitter data sets collected a month apart. It finds that the first data set focused mainly on performances from this year's festival, while the second shifted to next year's event. The paper also examines the Twitter account @belugaPOD and recommends increasing interactions with important users and involvement in smaller conversations to improve their presence. Google Analytics showed most important website visitors came from SoundCloud. Overall, the paper aims to understand social media discussions of TomorrowWorld and how to enhance @belugaPOD's online and social media presence.
Iaetsd real time event detection and alert system using sensorsIaetsd Iaetsd
This document proposes a real-time earthquake detection system using Twitter data and sensors. Tweets containing keywords related to earthquakes are analyzed using machine learning classifiers to detect earthquake events. A particle filtering algorithm is used to estimate the location and trajectory of earthquakes based on geotagged tweets. The system was able to detect earthquakes faster than traditional methods and send alerts to registered users.
Liveblogging, mobile journalism and verificationPaul Bradshaw
The document discusses the rise of continuous news reporting across multiple platforms using new technologies like live blogging and mobile journalism. It provides examples of how various organizations have adopted these practices to report on events like elections. It also discusses tools, techniques, challenges and ethics of verifying information and reporting in real-time from mobile devices.
IRJET- An Experimental Evaluation of Mechanical Properties of Bamboo Fiber Re...IRJET Journal
This document discusses tweet segmentation and its application to named entity recognition. It proposes a novel framework called Hybrid Segmentation that segments tweets into meaningful phrases by maximizing the stickiness score of candidate segments based on global context from external sources like Wikipedia and local context from linguistic features within a batch of tweets. Two methods are developed - one using local linguistic features and the other using local term dependency. Experimental results on two tweet datasets show improved segmentation quality when using both global and local contexts compared to global context alone. The segmented tweets achieve higher accuracy for named entity recognition compared to word-based methods, demonstrating the benefit of tweet segmentation for downstream natural language processing applications.
IRJET- Tweet Segmentation and its Application to Named Entity RecognitionIRJET Journal
This document summarizes a research paper on tweet segmentation and its application to named entity recognition. It proposes a novel framework called Hybrid Segmentation that splits tweets into meaningful segments to preserve semantic context for downstream natural language processing applications like named entity recognition. HybridSeg finds optimal tweet segmentations by maximizing the stickiness score of segments, which considers global context based on English phrases and local context based on linguistic features or term dependencies within a batch of tweets. Experiments show significantly improved segmentation quality when learning both global and local contexts compared to global context alone. The segmented tweets can then be used for high accuracy named entity recognition through part-of-speech tagging.
An Update on Drone Regulations - EU U-Space & US UTM ...Kevin O'Donovan
An update on Drone Regulations - EU U-Space & US UTM update delivered at CIGRE Israel UAV Seminar on 30th July in Tel Aviv.
Posted as is without audio.
For any comments/questions, contact me on LinkedIN or Twitter.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
WWW2010_Earthquake Shakes Twitter User: Analyzing Tweets for Real-Time Event...tksakaki
Twitter, a popular microblogging service, has received much
attention recently. An important characteristic of Twitter
is its real-time nature. For example, when an earthquake
occurs, people make many Twitter posts (tweets) related
to the earthquake, which enables detection of earthquake
occurrence promptly, simply by observing the tweets. As
described in this paper, we investigate the real-time interaction
of events such as earthquakes in Twitter and propose
an algorithm to monitor tweets and to detect a target
event. To detect a target event, we devise a classifier of
tweets based on features such as the keywords in a tweet,
the number of words, and their context. Subsequently, we
produce a probabilistic spatiotemporal model for the target
event that can find the center and the trajectory of the
event location. We consider each Twitter user as a sensor
and apply Kalman filtering and particle filtering, which are
widely used for location estimation in ubiquitous/pervasive
computing. The particle filter works better than other comparable
methods for estimating the centers of earthquakes
and the trajectories of typhoons. As an application, we construct
an earthquake reporting system in Japan. Because of
the numerous earthquakes and the large number of Twitter
users throughout the country, we can detect an earthquake
with high probability (96% of earthquakes of Japan Meteorological
Agency (JMA) seismic intensity scale 3 or more
are detected) merely by monitoring tweets. Our system detects
earthquakes promptly and sends e-mails to registered
users. Notification is delivered much faster than the announcements
that are broadcast by the JMA.
Twitter is now an established and a widely popular news medium. Be it normal banter or a discussion on high impact events like Boston marathon blasts, February 2014 US Icestorm, etc., people use Twitter to get updates and also broadcast their thoughts and views. Twitter bots have today become very common and acceptable. People are using them to get updates about emergencies like natural disasters, terrorist strikes, etc., users also use them for getting updates about different places and events, both local and global. Twitter bots provide these users a means to perform certain tasks on Twitter that are both simple and structurally repetitive, at a much higher rate than what would be possible for a human alone. During high impact events these Twitter bots tend to provide a time critical and a comprehensive information source with information aggregated form various different sources. In this study, we present how these bots participate in discussions and augment them during high impact events. We identify bots in 5 high impact events for 2013: Boston blasts, February 2014 US Icestorm, Washington Navy Yard Shooting, Oklahoma tornado, and Cyclone Phailin. We identify bots among top tweeters by getting all such accounts manually annotated. We then study their activity and present many important insights. We determine the impact bots have on information diffusion during these events and how they tend to aggregate and broker information from various sources to different users. We also analyzed their tweets, list down important differentiating features between bots and non bots (normal or human accounts) during high impact events. We also show how bots are slowly moving away from traditional API based posts towards web automation platforms like IFTTT, dlvr.it, etc. Using standard machine learning, we proposed a methodology to identify bots/non bots in real time during high impact events. This study also looks into how the bot scenario has changed by comparing data from high impact events from 2013 against data from similar type of events from 2011. Bots active in high impact events generally don't spread malicious content. Lastly, we also go through an in-depth analysis of Twitter bots who were active during 2013 Boston Marathon Blast. We show how bots because of their programming structure don't pick up rumors easily during these events and even if they do; they do it after a long time.
Event detection in twitter using text and image fusioncsandit
In this paper, we describe an accurate and effective event detection method to detect events from
Twitter stream. It detects events using visual information as well as textual information to improve
the performance of the mining. It monitors Twitter stream to pick up tweets having texts and photos
and stores them into database. Then it applies mining algorithm to detect the event. Firstly, it detects
event based on text only by using the feature of the bag-of-words which is calculated using the term
frequency-inverse document frequency (TF-IDF) method. Secondly, it detects the event based on
image only by using visual features including histogram of oriented gradients (HOG) descriptors,
grey-level co-occurrence matrix (GLCM), and color histogram. K nearest neighbours (Knn)
classification is used in the detection. Finally, the final decision of the event detection is made based
on the reliabilities of text only detection and image only detection. The experiment result showed that
the proposed method achieved high accuracy of 0.93, comparing with 0.89 with texts only, and 0.86
with images only.
The document summarizes an MSR presentation on rumor detection on real-time Twitter data using supervised learning. It discusses introducing rumor detection and reviewing literature on current methods. The proposed work involves collecting Twitter data, preprocessing it, extracting features, and classifying tweets using techniques like decision trees and SVM. It aims to accurately detect rumors on Twitter in real-time by analyzing sentiment and verifying information with news sources. The implementation strategy and environment are also outlined along with conclusions and future work.
This document describes a research project analyzing real-time geotagged tweets to detect trending topics based on location. The project involved collecting geotagged tweets from Twitter using an API, preprocessing the tweets, and applying techniques to detect trending topics for specific geographic locations. The detected trending topics were then compared to topics displayed on other social media platforms. The goal was to automatically identify emerging topics being discussed on Twitter in real-time based on location to better understand information flow and user behavior patterns.
Semantic Twitter Analyzing Tweets For Real Time Event Notificationokazaki117
This document proposes a system to analyze tweets semantically in real-time to detect and notify users of events. It describes using Twitter to detect earthquakes in Japan by classifying tweets about earthquakes vs other topics using machine learning. The system would send email alerts to registered users about detected earthquakes near their location. It also discusses expanding the system to detect rainbows or celebrity sightings from tweets. The goal is to leverage Twitter's real-time nature and semantic analysis to provide timely notifications about events from social media data.
For my final year project I used data analysis techniques to investigate user behavior pattern recognition in respect of similar interests and culture versus offline geographical location. This was an out-of-the-box topic, which I selected due to my love on Data Analysis, in respect of the Social Network Analysis in the Internet era.
The document discusses the importance of companies using their own web mapping applications to demonstrate quality and capabilities. It notes that by using their own product, companies can identify issues and actively work to fix them. Additionally, when others also use the application, especially the right others to solve business problems, it will lead the application to get better and help the company survive. The presentation emphasizes how dogfooding a product helps ensure it works as intended and meets user needs.
This document appears to be a presentation by Learon Dalby on the topic of "Where are we going & who is 'we'". Some of the key points discussed include:
- The contributing factors that have led procurement, management structures, and project schedules to become barriers to progress.
- How approaches to procurement, software/applications, and data management have evolved since the 1950s and the challenges faced by the rapid adoption of technology.
- Questions around whether existing approaches to creating and maintaining large amounts of data have actually led to effective use and problem solving.
- The potential benefits of refocusing on solving problems directly rather than being constrained by existing procurement and software/infrastructure approaches.
This document discusses Chris Anderson's concept of "free" as it relates to digital goods and services. It outlines four business models for "free": direct cross-subsidies, three-party markets, freemium, and non-monetary markets. The models are shifting from scarcity to abundance as digital goods can be reproduced nearly infinitely at little to no cost. While companies may struggle to profit from free goods directly, the models find ways to do so through other revenue streams or non-monetary exchanges. Ultimately, businesses will be forced to compete with free in many industries and figure out new management and decision-making approaches as a result.
The document discusses the history and development of geographic information systems (GIS) in Arkansas from 1999 to 2013. It notes that in the early years there was only 1 staff member with no dedicated budget, but that over time staffing increased to 6 members and a general revenue budget was obtained. It outlines various geospatial datasets and programs that were established, such as statewide orthoimagery, road centerlines, parcels, addresses, and data distribution through GeoStor. Collaboration with other state agencies is also emphasized.
The document discusses the importance of being relevant when communicating information. It provides examples of county courthouse maps created for Arkansas counties and maps of a tornado that occurred in Dumas, Arkansas. It also discusses a project to create parcel maps for counties and receiving funds to accelerate that project during a difficult economic time. The key message is that relevance is important to effectively answer questions and ask good follow up questions.
The document discusses an event hosted by the National States Geographic Information Council on June 15-16, 2011 regarding open source mapping possibilities. It provides the address and contact information for the council. Several links are included to mapping ecosystems and resources. Open source is cited as a viable option by the Department of Defense, and that need, not predetermined solutions, should drive architectural and software choices. Contact information is given for a person named Learon Dalby.
This document discusses using geospatial technologies like satellite imagery and floodplain mapping to better understand flood risks. It also lists various geospatial companies and tools, and provides Learon Dalby's contact information for questions. The document seems to be notes from a presentation on emerging geospatial topics, opportunities, and challenges.
The document discusses mapping Arkansas' state budget by geographic location using GIS, including obtaining funding data from various state agencies, displaying the data thematically by county, city, and school district on maps, and presenting the results in binders divided by legislative district for the House and Senate to easily view budget allocations for their areas. Issues addressed include acquiring and processing the data, mapping and display methods, delivering the results on time, and ensuring clear and consistent presentation.
The Arkansas Road Centerline File was created in 2002 to provide a single, accurate source for road information in Arkansas that was maintained by local contributors and shared publicly. It took 8 years and over 75 contributors to build the 420,216 record, 1 gigabyte file, which is updated as information is provided and aims to be a globally accessed resource available through Arkansas' GeoStor website.
The document discusses Arkansas' statewide GIS data program called GeoStor. It notes that GeoStor was established in 2003 to coordinate GIS data development and distribution across state, local, and federal government agencies to reduce duplication. GeoStor currently hosts over 200 datasets that are freely accessible online through various download and web service methods. The document highlights that Arkansas GIS data is accessed globally and notes the economic and disaster response benefits of openly sharing standardized GIS data. It also discusses opportunities to improve data quality through increased sharing with other systems like OpenStreetMap.
I had the opportunity to author the first half of this presentation and provide feedback on the second half. This presentation was presented by Shelby Johnson, Arkansas Geographic Information Officer and Michael Turner, Applied Geographics to the Joint Arkansas Advanced Communications and Information Technology Legislative Committee.
This 2010 presentation discussed Arkansas' response to an 2009 ice storm and issues with visualizing state network outages. The presentation lasted 20 minutes and included discussion. Key issues focused on communication. The problem was visualizing where state network outages occurred on the state network. Additional potential uses of visualizing outages included power outages, lines cut, assessing the significance of an event like a tornado, and quick calculations of outage percentages. Contact information was provided for further information.
This keynote was put together in less than 24 hour hours with the help of
Andrew Turner, FortiusOne
Jeff Harrison, Carbon Project
Jill M Terlaak Mulica, City of Greeley, CO
Gretchen N. Peterson, Peterson GIS
Nuke Goldstein, Carbon Project
Sean Gorman, FortiousOne
Shelby Johnson, AGIO
My apologies if I left anyone out
Is government ready to embrace what social mediaLearon Dalby
This document discusses how government can better embrace social media to communicate with the public. It notes that people, especially younger generations, increasingly use mobile apps and social networks to communicate. However, government still relies primarily on one-way communication methods like websites and email lists. The document suggests that government should communicate through multiple social channels simultaneously and be responsive to public feedback in order to better serve citizens in the modern era of technology. It provides examples of how government agencies can use social games and crowd-sourced crisis maps to engage the public and receive authoritative data.
NSGIC Intro to a Different Kind of ConferenceLearon Dalby
This document discusses Ignite sessions and opportunities for an Ignite/Un-Conference event. Ignite sessions involve 20 slides that automatically advance every 15 seconds, allowing presenters 5 minutes to share their idea. An Un-Conference allows attendees to provide topics and have discussions. The document proposes combining Ignite sessions in the morning with Un-Conference breakouts in the afternoon, which could enable more topics to be covered, deeper dives into topics, and greater interaction among attendees. Some challenges of this hybrid event include uncertainty and needing speakers, but it has potential to build a more customized conference experience. Contact information is provided for the author discussing this proposal.
Social Media For Business IntelligenceLearon Dalby
This document discusses using social media tools like Twitter for business intelligence. It provides two demonstrations of analyzing Twitter data using FME Server and Google Maps/Earth. Tweets are collected from Twitter and stored in a database. FME Server transforms the tweets and streams them to Google clients via KML. The tweets are displayed spatially based on location data and refreshed regularly. Social media can provide bidirectional information exchange between government and citizens.
FME Server is used to transform Twitter data and stream it to Google Maps and Earth via KML. The tweets are stored in a database and time stamped to allow for future analysis and replaying the event. Every 30 seconds new tweets are archived and every 10 seconds Google Earth requests updated tweets from the database, while the Google Maps view requires page refreshes to see new tweets.
How to Sign Up for Various Communication ToolsLearon Dalby
1) The presentation introduces various Google communication tools and how to set them up, including setting up a Google account, homepage, documents, and access to Google Wave.
2) It also discusses how to set up a Facebook page and connect it to NSGIC's page and how to set up a Twitter account to search for people to follow and tweet.
3) The presentation encourages exploring different communication tools and considering how to establish an online presence across multiple platforms using consistent profile details.
This presentation was provided in 2009 and is certainly temporal, given the nature of the discussion.
Presentation roughly 10 minutes as part of a panel.
1. Apologies for the use of text in this slide show. This was slide deck is intended to provide the findings of a Tweet test performed by the Arkansas Geographic Information Office. 1
2. This was Just a Test Prepared by: Arkansas Geographic Information Office www.gis.arkansas.gov 2 All pictures shown; tweeted with #Arwx A KMZ can be downloaded from http://bit.ly/bKzf5T Still working on date/time format
3. #ARwx Tweet TestApril 23-26, 2010April 29 – May 3, 2010 Prepared by: Arkansas Geographic Information Office www.gis.arkansas.gov 3 All pictures shown; tweeted with #Arwx A KMZ can be downloaded from http://bit.ly/bKzf5T Still working on date/time format
4. There is a geographic component to Tweets 4 All tweets were not located properly during this test
5. There is a geographic component to Tweets 5 Location is a significant component
6. Purpose of Test Determine twitter use for an event Determine acceptance of a hashtag Determine acceptance of geoenabled tweets Figure out if this is useful http://www.srh.noaa.gov/lzk/?n=svr0410a.htm 6
8. Results April 23-26, 2010 Safe Software Twitter Workbench worked properly Data was captured and stored in a database Data can be viewed in GoogleEarth Tweets 32 unique users 10 geotagged (lat/long) 77 profile was used to locate 9 could not be located 96 total number of tweets during this event 8
9. Results April 23-26, 2010 Safe Software Twitter Workbench worked properly Data was captured and stored in a database Data can be viewed in GoogleEarth Tweets 32 unique users 10 geotagged (lat/long) 77 profile was used to locate 9 could not be located 96 total number of tweets during this event Yeah!!! Nota ‘significant’ Event 9
10. Information Captured Date / Time Created Tweet ID Tweet Text Tweet User Tweet User Profile Image Location (lat-long / profile) Search Text = #arwx Geometry 10
11. Results April 30-May 3, 2010 Safe Software Twitter Workbench- Failed* A number of tweets were not captured Issues identified: Number of Twitter API calls per hour Configuration of scripts Each of these are being addressed for next test An export from Twapper allowed analysis Captured Tweets Georefrenced using profiles captured from failed test Manually inserted several based on profile description Exported to KML 11 *Failure of the workbench was due to implementation; not the Safe Software product
12. Results April 30-May 3, 2010 Tweets 219 unique users 19 geotagged (lat/long) 1,425 profile was used to locate 135 could not be located 1,579 total number of tweets during this event Users Most tweets by a single user 100 User retweeted the most 108 (@wxmandan – LR NWS) 12
13. Process Used to Generate KML Exported #ARwx from Twapper Performed table join based on userid to the data captured from the failed test Inserted location to a number of tweets from Twapper Removed duplicates Attempted to adjust date and time to CST Ran Safe Software workbench to generate a kml 13
20. General Test Observations Determine twitter use for an event Twitter proved useful for communicating the events as they unfolded. Tweets provided on the ground information and pictures. Determine acceptance of a hashtag The hashtag was generally accepted, but took roughly 18 hours for uptake Determine acceptance of geoenabled tweets The majority of tweets were not geotagged. Several profiles did not allow for any location information to be identified. Figure out if this is useful No doubt twitter is useful for communicating events. The next step will be evaluating protocols needed for real-time analysis and bi-directional communication 20
27. Considerations Network Assumes network access Assumes Twitter (in this case) is available Education Enabling geotagging Acceptance Geotagging Ability to provide reports Mapability API works properly API provides useful information Usability Filtering the noise Can the appropriate protocols be put in place to make this a useful tool for hearing those that ‘call for help’ or submit ‘field reports’? The handling of date/time stamps 27
28. Summary The recent events have provided us with good information to use in our test. We will continue to analyze the information and determine how the power of the technology might be further used in future events. 28