Detecting Local Events by Analyzing Spatiotemporal Locality of Tweets

Sugitani, Takuya; Shirakawa, Masumi; Hara, Takahiro; Nishio, Shunya

doi:10.1109/waina.2013.246

Cited by 14 publications

(6 citation statements)

References 6 publications

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“…Location information of social media data is further integrated to identify and detect local events. Sugitani et al (2013) applied a hierarchical clustering method to cluster the tweets based on the geo-distance and identify the spatial and temporal burstiness of terms in each cluster to identify real local events. Krumm and Horvitz (2015) split the space into hierarchical and relatively small size regions and utilized the regression errors of geotagged tweet volumes in each volume to search local event regions.…”

Section: Event Detection From Twitter-like Textual Streamsmentioning

confidence: 99%

Research on event perception based on geo-tagged social media data

Zhu

Zuo

Lin

2019

Proc. Int. Cartogr. Assoc.

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<p><strong>Abstract.</strong> Technological advancement makes information dissemination more convenient. When a notable event occurs, social media serves a popular platform for citizens to share event-related information. Therefore, in the information age, how to effectively observe the event and improve event management ability is an open question worthy of attention. Traditional social survey methods and various automatic sensors have been widely used to monitor the specific event. However, widely used social media service provides a unique approach for the event study with individuals as smart sensors. How to perceive an event through social media data has triggered a series of researches. Currently, we can find when, where what happened and induced impact based on geo-tagged social media data. However, event study based on social media is still in its infancy. This paper provides an overview of event study based on geo-tagged social media data. Firstly, we introduce the event model and the characteristics of social media data. Then, how to detect and trace event, how to analyze event impact and visually express obtained knowledge are discussed respectively. Subsequently, based on the existing researches, we propose further questions and conclude.</p>

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Section: Event Detection From Twitter-like Textual Streamsmentioning

confidence: 99%

Research on event perception based on geo-tagged social media data

Zhu

Zuo

Lin

2019

Proc. Int. Cartogr. Assoc.

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“…Crooks et al assessed the potential of people to act as sensors for event monitoring and concluded that social media data could be used to enhance our situational awareness [25]. For event detection and tracking, some scholars integrated the temporal burstiness of terms or data volume and location information to find local events [26,27]. Adjusted classical probabilistic models combined with spatial and temporal features represent another frequently investigated approach of event detection [28,29].…”

Section: Event-related Research Based On Geo-tagged Social Media Datamentioning

confidence: 99%

Geo-Tagged Social Media Data-Based Analytical Approach for Perceiving Impacts of Social Events

Zhu

Lin

Jendryke

et al. 2018

IJGI

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Studying the impact of social events is important for the sustainable development of society. Given the growing popularity of social media applications, social sensing networks with users acting as smart social sensors provide a unique channel for understanding social events. Current research on social events through geo-tagged social media is mainly focused on the extraction of information about when, where, and what happened, i.e., event detection. There is a trend towards the machine learning of more complex events from even larger input data. This research work will undoubtedly lead to a better understanding of big geo-data. In this study, however, we start from known or detected events, raising further questions on how they happened, how they affect people’s lives, and for how long. By combining machine learning, natural language processing, and visualization methods in a generic analytical framework, we attempt to interpret the impact of known social events from the dimensions of time, space, and semantics based on geo-tagged social media data. The whole analysis process consists of four parts: (1) preprocessing; (2) extraction of event-related information; (3) analysis of event impact; and (4) visualization. We conducted a case study on the “2014 Shanghai Stampede” event on the basis of Chinese Sina Weibo data. The results are visualized in various ways, thus ensuring the feasibility and effectiveness of our proposed framework. Both the methods and the case study can serve as decision references for situational awareness and city management.

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“…Spatiotemporal hotspots are a special kind of clustered pattern whose inside has significantly higher intensity than outside. Localized events within small geographic areas, such as public event, based on clustering techniques are handled in [20]. A study in [19] proposed a system to identify bursty local by using a spatiotemporal clustering technique.…”

Section: Spatiotemporal Clusteringmentioning

confidence: 99%

Finding Spatiotemporal Co-occurrence Patterns of Heterogeneous Events for Prediction

Tran-The

Zettsu

2017

Proceedings of the 3rd ACM SIGSPATIAL Workshop on Emergency Management Using

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Advances of IoT facilitates leverage of heterogeneous sensing data over the Internet, such as remote sensing data, traffic data and SNS data. Integrated analysis of IoT data is crucial part for urban emergency management in smart cities in order to predict various social events co-occurring with a natural disaster event. Discovering of spatiotemporal co-occurrence patterns is a task of integrated analysis of IoT data and has received a lot of attention. However, such spatiotemporal co-occurrence patterns can fail to capture local events that occur in limited regions and limited time intervals.In this paper, we consider the problem of mining spatiotemporal co-occurrence patterns from IoT sensing data, each of which is annotated with a valid spatial and temporal region. Our idea is to incorporate spatiotemporal clustering with the frequent itemset (pattern) discovery process to reduce spatiotemporal bias of event distributions and we repeat this process in greedy approach in order to capture patterns with difference scales. By this way, our algorithm improves accuracy of the frequent itemsets. We applied our method to discovery and prediction of traffic disaster events co-occurring with torrential rain events in Kansai area, Japan. Our experimental result shows 31% improvement of prediction performance on F-measure against a baseline.

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Detecting Local Events by Analyzing Spatiotemporal Locality of Tweets

Cited by 14 publications

References 6 publications

Research on event perception based on geo-tagged social media data

Research on event perception based on geo-tagged social media data

Geo-Tagged Social Media Data-Based Analytical Approach for Perceiving Impacts of Social Events

Finding Spatiotemporal Co-occurrence Patterns of Heterogeneous Events for Prediction

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