| Records |
Author  |
Cornelia Caragea; Anna Squicciarini; Sam Stehle; Kishore Neppalli; Andrea H. Tapia |
| Title |
Mapping moods: Geo-mapped sentiment analysis during hurricane sandy |
Type |
Conference Article |
| Year |
2014 |
Publication |
ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2014 |
| Volume |
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Issue |
|
Pages |
642-651 |
| Keywords |
Data mining; Disasters; Hurricanes; Information systems; Disaster-related geo-tagged tweets; Online reviews; Online social networkings; Sentiment analysis; Sentiment classification; Social networking sites; Social networking (online) |
| Abstract |
Sentiment analysis has been widely researched in the domain of online review sites with the aim of generating summarized opinions of product users about different aspects of the products. However, there has been little work focusing on identifying the polarity of sentiments expressed by users during disaster events. Identifying sentiments expressed by users in an online social networking site can help understand the dynamics of the network, e.g., the main users' concerns, panics, and the emotional impacts of interactions among members. Data produced through social networking sites is seen as ubiquitous, rapid and accessible, and it is believed to empower average citizens to become more situationally aware during disasters and coordinate to help themselves. In this work, we perform sentiment classification of user posts in Twitter during the Hurricane Sandy and visualize these sentiments on a geographical map centered around the hurricane. We show how users' sentiments change according not only to users' locations, but also based on the distance from the disaster. |
| Address |
Computer Science and Engineering, University of North Texas, Denton, TX-76203, United States; Information Sciences and Technology, Pennsylvania State University, University Park, PA-16801, United States |
| Corporate Author |
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Thesis |
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| Publisher |
The Pennsylvania State University |
Place of Publication |
University Park, PA |
Editor |
S.R. Hiltz, M.S. Pfaff, L. Plotnick, and P.C. Shih. |
| Language |
English |
Summary Language |
English |
Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
2411-3387 |
ISBN |
9780692211946 |
Medium |
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| Track |
Social Media in Crisis Response and Management |
Expedition |
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Conference |
11th International ISCRAM Conference on Information Systems for Crisis Response and Management |
| Notes |
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Approved |
no |
| Call Number |
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Serial |
372 |
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| Author |
Hongmin Li; Nicolais Guevara; Nic Herndon; Doina Caragea; Kishore Neppalli; Cornelia Caragea; Anna Squicciarini; Andrea H. Tapia |
| Title |
Twitter Mining for Disaster Response: A Domain Adaptation Approach |
Type |
Conference Article |
| Year |
2015 |
Publication |
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2015 |
| Volume |
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Issue |
|
Pages |
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| Keywords |
Disaster Response; domain adaptation; tweet classification |
| Abstract |
Microblogging data such as Twitter data contains valuable information that has the potential to help improve the speed, quality, and efficiency of disaster response. Machine learning can help with this by prioritizing the tweets with respect to various classification criteria. However, supervised learning algorithms require labeled data to learn accurate classifiers. Unfortunately, for a new disaster, labeled tweets are not easily available, while they are usually available for previous disasters. Furthermore, unlabeled tweets from the current disaster are accumulating fast. We study the usefulness of labeled data from a prior source disaster, together with unlabeled data from the current target disaster to learn domain adaptation classifiers for the target. Experimental results suggest that, for some tasks, source data itself can be useful for classifying target data. However, for tasks specific to a particular disaster, domain adaptation approaches that use target unlabeled data in addition to source labeled data are superior. |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
University of Agder (UiA) |
Place of Publication |
Kristiansand, Norway |
Editor |
L. Palen; M. Buscher; T. Comes; A. Hughes |
| Language |
English |
Summary Language |
English |
Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
2411-3387 |
ISBN |
9788271177881 |
Medium |
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| Track |
Social Media Studies |
Expedition |
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Conference |
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
| Notes |
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Approved |
yes |
| Call Number |
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Serial |
1234 |
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| Author |
Venkata Kishore Neppalli; Cornelia Caragea; Doina Caragea |
| Title |
Deep Neural Networks versus Naive Bayes Classifiers for Identifying Informative Tweets during Disasters |
Type |
Conference Article |
| Year |
2018 |
Publication |
ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2018 |
| Volume |
|
Issue |
|
Pages |
677-686 |
| Keywords |
deep neural networks, naive bayes classifiers, handcrafted features |
| Abstract |
In this paper, we focus on understanding the effectiveness of deep neural networks by comparison with the effectiveness of standard classifiers that use carefully engineered features. Specifically, we design various feature sets (based on tweet content, user details and polarity clues) and use these feature sets individually or in various combinations, with Naïve Bayes classifiers. Furthermore, we develop neural models based on Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) with handcrafted architectures. We compare the two types of approaches in the context of identifying informative tweets posted during disasters, and show that the deep neural networks, in particular the CNN networks, are more effective for the task considered. |
| Address |
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Thesis |
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| Publisher |
Rochester Institute of Technology |
Place of Publication |
Rochester, NY (USA) |
Editor |
Kees Boersma; Brian Tomaszeski |
| Language |
English |
Summary Language |
English |
Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
2411-3387 |
ISBN |
978-0-692-12760-5 |
Medium |
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| Track |
Social Media Studies CO - |
Expedition |
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Conference |
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| Notes |
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Approved |
no |
| Call Number |
|
Serial |
2141 |
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| Author |
Venkata Kishore Neppalli; Murilo Cerqueira Medeiros; Cornelia Caragea; Doina Caragea; Andrea Tapia; Shane Halse |
| Title |
Retweetability Analysis and Prediction during Hurricane Sandy |
Type |
Conference Article |
| Year |
2016 |
Publication |
ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2016 |
| Volume |
|
Issue |
|
Pages |
|
| Keywords |
Twitter; Retweetability Analysis; Retweetability Prediction; Hurricane Sandy; Disaster Events |
| Abstract |
Twitter is a very important source for obtaining information, especially during events such as natural disasters. Users can spread information in Twitter either by crafting new posts, which are called ?tweets,? or by using retweet mechanism to re-post the previously created tweets. During natural disasters, identifying how likely a tweet is to be highly retweeted is very important since it can help promote the spread of good information in a network such as Twitter, as well as it can help stop the spread of misinformation, when corroborated with approaches that identify trustworthy information or misinformation, respectively. In this paper, we present an analysis on retweeted tweets to determine several aspects affecting retweetability. We then extract features from tweets? content and user account information and perform experiments to develop models that automatically predict the retweetability of a tweet in the context of the Hurricane Sandy. |
| Address |
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Thesis |
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| Publisher |
Federal University of Rio de Janeiro |
Place of Publication |
Rio de Janeiro, Brasil |
Editor |
A. Tapia; P. Antunes; V.A. Bañuls; K. Moore; J. Porto |
| Language |
English |
Summary Language |
English |
Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
2411-3388 |
ISBN |
978-84-608-7984-9 |
Medium |
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| Track |
Social Media Studies |
Expedition |
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Conference |
13th International Conference on Information Systems for Crisis Response and Management |
| Notes |
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Approved |
no |
| Call Number |
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Serial |
1389 |
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