Robust Detection of Extreme Events Using Twitter: Worldwide Earthquake Monitoring

Poblete, Bárbara; Guzman, Jheser; Maldonado, Jazmine; Tobar, Felipe

doi:10.1109/tmm.2018.2855107

Cited by 57 publications

(31 citation statements)

References 14 publications

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“…However, by including more keywords we also risk adding more noise to our dataset. In this sense, we the keywords that we are currently using are the same as in those used in [39], which have showed excellent recall for earthquakes in Chile. This is supported by Table 2, which shows that the selected terms allow us to achieve good coverage in our dataset for all relevant events in the country.…”

Section: Discussionmentioning

confidence: 99%

“…We believe these types of features can also be helpful in the elaboration of spatial intensity reports. In addition, since the reports produced by our method are almost identical to those produced by experts, we plan to embed Twitter-based intensity estimations into the state-of-the-art earthquake detection and visualization system Twicalli i [39].…”

Section: Discussionmentioning

confidence: 99%

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Nowcasting earthquake damages with Twitter

2019

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The Modified Mercalli intensity scale (Mercalli scale for short) is a qualitative measure used to express the perceived intensity of an earthquake in terms of damages. Accurate intensity reports are vital to estimate the type of emergency response required for a particular earthquake. In addition, Mercalli scale reports are needed to estimate the possible consequences of strong earthquakes in the future, based on the effects of previous events. Emergency offices and seismological agencies worldwide are in charge of producing Mercalli scale reports for each affected location after an earthquake. However, this task relies heavily on human observers in the affected locations, who are not always available or accurate. Consequently, Mercalli scale reports may take up to hours or even days to be published after an earthquake. We address this problem by proposing a method for early prediction of spatial Mercalli scale reports based on people's reactions to earthquakes in social networks. By tracking users' comments about real-time earthquakes, we create a collection of Mercalli scale point estimates at municipality (i.e., state subdivisions) level granularity. We introduce the concept of reinforced Mercalli support, which combines Mercalli scale point estimates with locally supported data (named 'local support'). We use this concept to provide Mercalli scale estimates for real-world events by providing smooth point estimates using a spatial smoother that incorporates the distribution of municipalities in each affected region. Our method is the first method based on social media that can provide spatial reports of damages in the Mercalli intensity scale. Experimental results show that our method is accurate and provides early spatial Mercalli reports 30 minutes after an earthquake. Furthermore, we show that our method performs well for earthquake spatial detection and maximum intensity prediction tasks. Our findings indicate that social media is a valuable source of spatial information for quickly estimating earthquake damages.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Nowcasting earthquake damages with Twitter

2019

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“…However, this method is different from the application scenarios of our method, in which the burstiness of words is mainly used to infer and obtain public opinion topics based on generating a probability. Poblete et al [37] propose a real-time public opinion topic monitoring method based on bursty word clustering, which can effectively monitor earthquake-related topics in social networks. However, in the face of massive data, the public opinion obtained from bursty words or bursty feature clustering will mix with many noisy data, which reduces the accuracy of topic identification and cannot address the context sparsity of social networks.…”

Section: B Public Opinion Topic Identification Methods Based On Clustmentioning

confidence: 99%

ST-ETM: A Spatial-Temporal Emergency Topic Model for Public Opinion Identifying in Social Networks

2020

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With the development and popularization of social networks, many users and authoritative media broach and share topics through social networks every day. This type of sharing is widespread and is sometimes the first communication of topics of public discussion. Therefore, research on identifying public opinion topics on social networks is of great significance. However, social network posts are short, sparse and noisy, which creates challenges for this research. To overcome these challenges, we propose a spatialtemporal emergency topic model (ST-ETM) to identify public opinion topics in social networks. By introducing spatial-temporal features into the topic model to construct spatial-temporal regions for focusing on topics, the ST-ETM can alleviate the sparsity problem of context in social networks and succeed in focusing on public opinion topics. Moreover, to automatically identify public opinion topics, we introduce the burstiness of the words as a priori in the model, and binary switch variables are combined to automatically identify public opinion topics in social networks. Based on a real Sina Weibo dataset, several comparative experiments are designed to evaluate the performance of our ST-ETM. The experimental results verify the effectiveness of the proposed ST-ETM method. INDEX TERMS public opinion topic, social network, topic model, topic identification.

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“…These prior thoughts or opinions can be accumulated using the past sentiments of a user. Therefore, we propose a sliding window mechanism (in sentiment classification) -a mechanism well known in event detection, where the aim is to analyze words or hashtags within a time window [41], [42]. With the same approach, we based this feature on the assumption that the current tweet's sentiment is dependent on past sentiment(s).…”

Section: B Sliding Window Featuresmentioning

confidence: 99%

Context-Aware Sliding Window for Sentiment Classification

Masood

Abbasi

2020

IEEE Access

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Sentiment classification is an active area of research with applications in many domains. Many researchers in the past have proposed techniques to identify sentiments with reasonable accuracy. However, the focus is more on the syntactic and semantic features of the documents. These features are effective but they ignore the user's past sentiments. In this research, we hypothesize that the past sentiments help the classifier to effectively link the user's history along with the contents of the current tweet. Thus, allowing learning algorithms to correlate past activities in determining the current sentiments. For this sake, we propose three sliding window features to accumulate past sentiments from the time series data. In this paper, we propose seven variations of Context-aware Sliding Window (CSW) features on different machine learning and deep learning algorithms. Furthermore, we propose a temporal dataset of user tweets, which is manually labeled by nine human annotators. The proposed dataset consists of 36 users having 4,557 tweets. Results indicate significant improvements over six state-of-the-art baseline methods. INDEX TERMS Sentiment classification, user history, temporal sliding window, deep learning, machine-learning, temporal dataset.

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Robust Detection of Extreme Events Using Twitter: Worldwide Earthquake Monitoring

Cited by 57 publications

References 14 publications

Nowcasting earthquake damages with Twitter

Nowcasting earthquake damages with Twitter

ST-ETM: A Spatial-Temporal Emergency Topic Model for Public Opinion Identifying in Social Networks

Context-Aware Sliding Window for Sentiment Classification

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