Determining Relative Airport Threats from News and Social Media

Khandpur, Rupinder Paul; Ji, Taoran; Ning, Yue; Zhao, Liang; Lu, Chang-Tien; Smith, Erik R.; Adams, Christopher; Ramakrishnan, Naren

doi:10.1609/aaai.v31i2.19097

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…Studies leverage incremental clustering (Zhao, Mitra, and Chen 2007;Weng and Lee 2011;Ag-garwal and Subbian 2012;Zhang, Zi, and Wu 2007;Feng et al 2015;Xie et al 2016), community detection (Fedoryszak et al 2019Liu et al 2020a,b;Yu et al 2017), and topic modeling (Zhou and Chen 2014;Zhou, Chen, and He 2015;Xing et al 2016;Wang et al 2016;Zhao et al 2011) are common. There are also methods for specific domains (Yao et al 2020;Arachie et al 2020;Khandpur et al 2017) such as airport threats. They extract attributes, e.g., hashtag, from the social media components then pre-process the attributes in highly-customized manners.…”

Section: Related Workmentioning

confidence: 99%

Hierarchical and Incremental Structural Entropy Minimization for Unsupervised Social Event Detection

Cao,

Peng,

et al. 2024

AAAI

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As a trending approach for social event detection, graph neural network (GNN)-based methods enable a fusion of natural language semantics and the complex social network structural information, thus showing SOTA performance. However, GNN-based methods can miss useful message correlations. Moreover, they require manual labeling for training and predetermining the number of events for prediction. In this work, we address social event detection via graph structural entropy (SE) minimization. While keeping the merits of the GNN-based methods, the proposed framework, HISEvent, constructs more informative message graphs, is unsupervised, and does not require the number of events given a priori. Specifically, we incrementally explore the graph neighborhoods using 1-dimensional (1D) SE minimization to supplement the existing message graph with edges between semantically related messages. We then detect events from the message graph by hierarchically minimizing 2-dimensional (2D) SE. Our proposed 1D and 2D SE minimization algorithms are customized for social event detection and effectively tackle the efficiency problem of the existing SE minimization algorithms. Extensive experiments show that HISEvent consistently outperforms GNN-based methods and achieves the new SOTA for social event detection under both closed- and open-set settings while being efficient and robust.

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Section: Related Workmentioning

confidence: 99%

Hierarchical and Incremental Structural Entropy Minimization for Unsupervised Social Event Detection

Cao,

Peng,

et al. 2024

AAAI

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“…The method harnesses the diverse relationships within the Twitter information network to The Efficacy of PRISTINE: Revealing Concealed Opioid Crisis Trends via Reddit Exam expand a seed query and improve coverage and accuracy [37]. In a real-time application, DQE was utilized to detect emerging threats related to airport security by tracking and analyzing the related tweets [38]. DQE has also been applied to enhance a multi-task framework for spatial event forecasting in social media [39].…”

Section: Dynamic Query Expansion For Event Detectionmentioning

confidence: 99%

The Efficacy of PRISTINE: Revealing Concealed Opioid Crisis Trends via Reddit Examination

Alhamadani

Sarkar

Behal

et al. 2023

Preprint

Self Cite

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This work is an extension of our previous efforts to combat the drug abuse epidemic which has been on the rise in the past few years []. We expand our developed framework PRISTINE ((opioid crisis detection on reddit)) to investigate the effectiveness of the framework on detecting opioids crisis trends on an expanded dataset from the two subreddits r/dugs and r/opiates. In this endeavor, we demonstrate the effectiveness of utilizing the DQE algorithm in identifying drug-related and evolving drug terms. we conduct comprehensive case studies for the seven drug categories and showcase the most associated keywords for each drug class and their slang/street names. In addition, we provide a case study on one of the most significant opioid crisis contributors to drug overdose deaths in the United States. Our case studies revealed hard-to-find drugrelated terms which we hope to contribute to mitigating this crisis. We additionally include a new analysis to investigate the efficacy of applying PRISTINE in categorizing subreddits into fine-grained drug classes. The new analysis includes a case study that classifies anonymized and lengthy subreddit comments into their correct drug class. The analysis shows the strong performance of PRISTINE and demonstrates that the framework can be applied to a wide range of subreddit comments. We finally include the performed extensive experiments to show the effectiveness of the overall performance of the proposed framework.

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“…In a field of analyzing social events in the physical world and the web, the event detection or forecast from multiple user activity data sets have been a hot topic (Atefeh and Khreich 2015) and have been applied to various kind of events including planned server downs (Becker et al 2012), natural hazards (Abhik and Toshniwal 2013), influenza epidemics (Santillana et al 2015), airport threats (Khandpur et al 2017), and civil unrest (Kallus 2014;Zhao et al 2015;. They have demonstrated considerable improvements by simultaneously analyzing multiple user activity data sets because each data represents different aspects of the user behavior.…”

Section: Introductionmentioning

confidence: 99%

Mobile Network Failure Event Detection and Forecasting With Multiple User Activity Data Sets

Oki

Takeuchi²,

Uematsu

2018

AAAI

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As the demand for mobile network services increases, immediate detection and forecasting of network failure events have become important problems for service providers. Several event detection approaches have been proposed to tackle these problems by utilizing social data. However, these approaches have not tried to solve event detection and forecasting problems from multiple data sets, such as web access logs and search queries. In this paper, we propose a machine learning approach that incorporates multiple user activity data into detecting and forecasting failure events. Our approach is based on a two-level procedure. First, we introduce a novel feature construction method that treats both the imbalanced label problem and the data sparsity problem of user activity data. Second, we propose a model ensemble method that combines outputs of supervised and unsupervised learning models for each data set and gives accurate predictions of network service outage. We demonstrate the effectiveness of the proposed models by extensive experiments with real-world failure events occurred at a network service provider in Japan and three user activity data sets.

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Determining Relative Airport Threats from News and Social Media

Cited by 6 publications

References 8 publications

Hierarchical and Incremental Structural Entropy Minimization for Unsupervised Social Event Detection

Hierarchical and Incremental Structural Entropy Minimization for Unsupervised Social Event Detection

The Efficacy of PRISTINE: Revealing Concealed Opioid Crisis Trends via Reddit Examination

Mobile Network Failure Event Detection and Forecasting With Multiple User Activity Data Sets

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