SNAF: Observation filtering and location inference for event monitoring on twitter

Zhang, Yihong; Szabo, Claudia; Sheng, Quan Z.; Fang, Xiu Susie

doi:10.1007/s11280-017-0453-1

Cited by 15 publications

(4 citation statements)

References 23 publications

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“…Similarly, the event detection system proposed by Zhang et al raises an alarm at the moment when the number of incident reports within a geographical region reaches a threshold Zhang et al. ( 2018 ). Zhao et al proposed a probabilistic model for detecting spatiotemporal events at the time they happen Zhao et al.…”

Section: Related Workmentioning

confidence: 99%

Generalized durative event detection on social media

2022

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Given the recent availability of large volumes of social media discussions, finding temporal unusual phenomena, which can be called events, from such data is of great interest. Previous works on social media event detection either assume a specific type of event, or assume certain behavior of observed variables. In this paper, we propose a general method for event detection on social media that makes few assumptions. The main assumption we make is that when an event occurs, affected semantic aspects will behave differently from their usual behavior, for a sustained period. We generalize the representation of time units based on word embeddings of social media text, and propose an algorithm to detect durative events in time series in a general sense. In addition, we also provide an incremental version of the algorithm for the purpose of real-time detection. We test our approaches on synthetic data and two real-world tasks. With the synthetic dataset, we compare the performance of retrospective and incremental versions of the algorithm. In the first real-world task, we use a novel setting to test if our method and baseline methods can exhaustively catch all real-world news in the test period. The evaluation results show that when the event is quite unusual with regard to the base social media discussion, it can be captured more effectively with our method. In the second real-world task, we use the event captured to help improve the accuracy of stock market movement prediction. We show that our event-based approach has a clear advantage compared to other ways of adding social media information.

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Section: Related Workmentioning

confidence: 99%

Generalized durative event detection on social media

2022

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“…For example, the Twitter-based earthquake detection system proposed by Sasaki et al [18] raises an alarm at the moment when number of tweets classified as earthquake reports reaches a certain threshold. Similarly, the event detection system proposed by Zhang et al raises an alarm at the moment when the number of incident reports within a geographical region reaches a threshold [25]. Weng and Lee proposed an event detection method based on wavelet transformation and word clustering [24].…”

Section: Related Workmentioning

confidence: 99%

A General Method for Event Detection on Social Media

Zhang

Shirakawa

Hara

2021

Preprint

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Event detection on social media has attracted a number of researches, given the recent availability of large volumes of social media discussions. Previous works on social media event detection either assume a specific type of event, or assume certain behavior of observed variables. In this paper, we propose a general method for event detection on social media that makes few assumptions. The main assumption we make is that when an event occurs, affected semantic aspects will behave differently from its usual behavior. We generalize the representation of time units based on word embeddings of social media text, and propose an algorithm to detect events in time series in a general sense. In the experimental evaluation, we use a novel setting to test if our method and baseline methods can exhaustively catch all real-world news in the test period. The evaluation results show that when the event is quite unusual with regard to the base social media discussion, it can be captured more effectively with our method. Our method can be easily implemented and can be treated as a starting point for more specific applications.

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“…People can create tweets , short messages of a length up to 140 characters, 1 which can be anything in their mind [ 101 ]. When posting from smart phones, tweets can be tagged with geo-coordinates to include location information [ 36 , 109 ]. People can follow other people, some of whom are their friends, some are just famous people [ 12 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Twitter-aided decision making: a review of recent developments

et al. 2022

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Twitter is one of the largest online platforms where people exchange information. In the first few years since its emergence, researchers have been exploring ways to use Twitter data in various decision making scenarios, and have shown promising results. In this review, we examine 28 newer papers published in last five years (since 2016) that continued to advance Twitter-aided decision making. The application scenarios we cover include product sales prediction, stock selection, crime prevention, epidemic tracking, and traffic monitoring. We first discuss the findings presented in these papers, that is how much decision making performance has been improved with the help of Twitter data. Then we offer a methodological analysis that considers four aspects of methods used in these papers, including problem formulation, solution, Twitter feature, and information transformation. This methodological analysis aims to enable researchers and decision makers to see the applicability of Twitter-aided methods in different application domains or platforms.

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SNAF: Observation filtering and location inference for event monitoring on twitter

Cited by 15 publications

References 23 publications

Generalized durative event detection on social media

Generalized durative event detection on social media

A General Method for Event Detection on Social Media

Twitter-aided decision making: a review of recent developments

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