Predicting Tweet Retweetability during Hurricane Disasters

Tapia, Andrea H.; Neppalli, Venkata Kishore; Caragea, Cornelia; Caragea, Doina; Medeiros, Murilo Cerqueira; Halse, Shane E.

doi:10.4018/ijiscram.2016070103

Cited by 5 publications

(3 citation statements)

References 25 publications

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“…A variety of traditional machine learning algorithms have been applied to the problem of crisis tweet classification. Commonly used methods include Logistic Regression [16], Support Vector Machines (SVM) [15], Naïve Bayes [17], and Decision Tree [18]. Although these methods have achieved reasonable performance, they often require extensive feature engineering and may not be well-suited for capturing the intricacies of natural language.…”

Section: Related Workmentioning

confidence: 99%

Leveraging Self-Attention-Based Deep Learning Networks in Language Processing for Real Crisis Detection on the Web

Li¹

2023

Proceedings of the 2nd International Conference on Bigdata Blockchain and Economy Management, ICBBEM 2023, May 19–21, 2023, Han

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With the proliferation of social media platforms, there has been a marked surge in the spread of information during crisis events. The identification of authentic crisis-related content on these platforms is essential for effective emergency management and response. In this paper, we introduce a novel approach for predicting the authenticity of crisis-related content using a self-attention-based deep learning network for Natural Language Processing (NLP). In this paper, various self-attention-based layers, Long Short Term Memory(LSTM), Multi Layer Perception(MLP) are explored. The proposed model is trained and evaluated on a dataset of labeled crisis-related posts from various social media platforms. The model demonstrates superior performance in distinguishing between authentic and non-authentic crisis-related content compared to baseline methods.The results suggest that self-attention-based deep learning networks can be effectively utilized for real-time detection of authentic crisis events on social media platforms.

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Section: Related Workmentioning

confidence: 99%

Leveraging Self-Attention-Based Deep Learning Networks in Language Processing for Real Crisis Detection on the Web

Li¹

2023

Proceedings of the 2nd International Conference on Bigdata Blockchain and Economy Management, ICBBEM 2023, May 19–21, 2023, Han

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“…We implemented an algorithm based on K nearest neighbor (KNN) for extracting information from VGI which resulted in about 70% of microblogs classified correctly [19], which was not enough. A further approach used is to verify the coincidence of the typhoon trajectory and the number of tweets in conjunction with their time and location [13,[20][21][22]. To date, not much research has been done to further analyze each twitter's contents, and most studies have stayed at statistical analysis of the amount of data from a specific time or place, as described above.…”

Section: Analysis Of Existing Studiesmentioning

confidence: 99%

Extracting Typhoon Disaster Information from VGI Based on Machine Learning

Zhao

Chin

2019

JMSE

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The southeastern coast of China suffers many typhoon disasters every year, causing huge casualties and economic losses. In addition, collecting statistics on typhoon disaster situations is hard work for the government. At the same time, near-real-time disaster-related information can be obtained on developed social media platforms like Twitter and Weibo. Many cases have proved that citizens are able to organize themselves promptly on the spot, and begin to share disaster information when a disaster strikes, producing massive VGI (volunteered geographic information) about the disaster situation, which could be valuable for disaster response if this VGI could be exploited efficiently and properly. However, this social media information has features such as large quantity, high noise, and unofficial modes of expression that make it difficult to obtain useful information. In order to solve this problem, we first designed a new classification system based on the characteristics of social medial data like Sina Weibo data, and made a microblogging dataset of typhoon damage with according category labels. Secondly, we used this social medial dataset to train the deep learning model, and constructed a typhoon disaster mining model based on a deep learning network, which could automatically extract information about the disaster situation. The model is different from the general classification system in that it automatically selected microblogs related to disasters from a large number of microblog data, and further subdivided them into different types of disasters to facilitate subsequent emergency response and loss estimation. The advantages of the model included a wide application range, high reliability, strong pertinence and fast speed. The research results of this thesis provide a new approach to typhoon disaster assessment in the southeastern coastal areas of China, and provide the necessary information for the authoritative information acquisition channel.

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“…One of the essential research topics in information diffusion is to predict the popularity of a piece of information (i.e. its diffusion scale or cascade size) based on early-stage spreading dynamics [7,8]. Existing prediction methods can be roughly divided into two main paradigms.…”

Section: Introductionmentioning

confidence: 99%

Why cannot long-term cascade be predicted? Exploring temporal dynamics in information diffusion processes

Cao

Liu

2021

R. Soc. open sci.

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Predicting information cascade plays a crucial role in various applications such as advertising campaigns, emergency management and infodemic controlling. However, predicting the scale of an information cascade in the long-term could be difficult. In this study, we take Weibo, a Twitter-like online social platform, as an example, exhaustively extract predictive features from the data, and use a conventional machine learning algorithm to predict the information cascade scales. Specifically, we compare the predictive power (and the loss of it) of different categories of features in short-term and long-term prediction tasks. Among the features that describe the user following network, retweeting network, tweet content and early diffusion dynamics, we find that early diffusion dynamics are the most predictive ones in short-term prediction tasks but lose most of their predictive power in long-term tasks. In-depth analyses reveal two possible causes of such failure: the bursty nature of information diffusion and feature temporal drift over time. Our findings further enhance the comprehension of the information diffusion process and may assist in the control of such a process.

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Predicting Tweet Retweetability during Hurricane Disasters

Cited by 5 publications

References 25 publications

Leveraging Self-Attention-Based Deep Learning Networks in Language Processing for Real Crisis Detection on the Web

Leveraging Self-Attention-Based Deep Learning Networks in Language Processing for Real Crisis Detection on the Web

Extracting Typhoon Disaster Information from VGI Based on Machine Learning

Why cannot long-term cascade be predicted? Exploring temporal dynamics in information diffusion processes

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