A Novel Approach for Event Detection by Mining Spatio-temporal Information on Microblogs

Lee, Chung-Hong; Yang, Hsin-Chang; Chien, Tzan-Feng; Wen, Wei-Shiang

doi:10.1109/asonam.2011.74

Cited by 37 publications

(18 citation statements)

References 9 publications

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“…Lee et al [10] use a two-stage approach to extract spatiotemporal event information from Twitter Streams. In stage-1, they apply BursT [11] to dynamically assign a score to each word.…”

Section: Related Workmentioning

confidence: 99%

On the locality of keywords in Twitter streams

Abdelhaq

Gertz

2014

Proceedings of the 5th ACM SIGSPATIAL International Workshop on GeoStreaming

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The continuously increasing popularity of social media sites such as Twitter and Facebook has recently led to a number of approaches to detect and extract event information from social media streams. Such events play an important role, e.g., in supporting location-based services and improving situational awareness. Moreover, the introduction of GPSequipped communication devises has led to an increase in the percentage of geo-tagged messages. These help to detect localized events, i.e., events occurring at a certain location, such as sport events or accidents. The main entities that indicate a localized event are local keywords that exhibit a surge in usage at the event location.In this paper, we propose an approach to extract local keywords from a Twitter stream by (1) identifying local keywords, and (2) estimating the central location of each keyword. This extraction process is performed in an online fashion using a sliding window on the Twitter stream. In addition, we address the problem of spatial outliers that adversely affect a proper identification of local keywords. Outliers occur when people far away from an event location use related keywords in their Tweets. We handle this problem by adjusting the spatial distribution of keywords based on their co-occurrence with place names that may refer to the location of an event. We evaluate the performance of our framework to reliably and efficiently extracting local keywords and estimating their central locations using a Twitter dataset.

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“…Lee et al [10] use a two-stage approach to extract spatiotemporal event information from Twitter Streams. In stage-1, they apply BursT [11] to dynamically assign a score to each word.…”

Section: Related Workmentioning

confidence: 99%

On the locality of keywords in Twitter streams

Abdelhaq

Gertz

2014

Proceedings of the 5th ACM SIGSPATIAL International Workshop on GeoStreaming

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“…Recent researches reveal that information diffusions in social networks exhibit a property of temporal locality, i.e., a meme one focuses on at a time point is more similar to the memes she/he focuses on at close time points than those at distant time points [15], [16], [17]. For example, one may especially focus on a specific movie in May, and in June, and may still care about the comments about the movie, but she/he is unlikely to spread the memes about this movie in December.…”

Section: B Time Window Based Parallel Decomposition Algorithm (Twpda)mentioning

confidence: 99%

Collaborative Inference of Coexisting Information Diffusions

Sun

Cong

Yang

et al. 2017

2017 IEEE International Conference on Data Mining (ICDM)

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Recently, diffusion history inference has become an emerging research topic due to its great benefits for various applications, whose purpose is to reconstruct the missing histories of information diffusion traces according to incomplete observations. The existing methods, however, often focus only on single information diffusion trace, while in a realworld social network, there often coexist multiple information diffusions over the same network. In this paper, we propose a novel approach called Collaborative Inference Model (CIM) for the problem of the inference of coexisting information diffusions. By exploiting the synergism between the coexisting information diffusions, CIM holistically models multiple information diffusions as a sparse 4th-order tensor called Coexisting Diffusions Tensor (CDT) without any prior assumption of diffusion models, and collaboratively infers the histories of the coexisting information diffusions via a low-rank approximation of CDT with a fusion of heterogeneous constraints generated from additional data sources. To improve the efficiency, we further propose an optimal algorithm called Time Window based Parallel Decomposition Algorithm (TWPDA), which can speed up the inference without compromise on the accuracy by utilizing the temporal locality of information diffusions. The extensive experiments conducted on real world datasets and synthetic datasets verify the effectiveness and efficiency of CIM and TWPDA.

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“…Exploiting geospatial data, De Longueville et al [13] used data from Twitter for forest fire detection. Lee et al [19] mined Twitter for information on earthquakes and plotted them on a world map. As most of those tweets had no information on geo-coordinates attached, they used city names to define positions of tweets.…”

Section: Mining Microblog Datamentioning

confidence: 99%

Exploring Geospatial Music Listening Patterns in Microblog Data

Hauger

Schedl

2014

Adaptive Multimedia Retrieval: Semantics, Context, and Adaptation

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Abstract. Microblogs are a steadily growing, valuable, albeit noisy, source of information on interests, preferences, and activities. As music plays an important role in many human lives we aim to leverage microblogs for music listening-related information. Based on this information we present approaches to estimate artist similarity, popularity, and local trends, as well as approaches to cluster artists with respect to additional tag information. Furthermore, we elaborate a novel geo-aware interaction approach that integrates these diverse pieces of information mined from music-related tweets. Including geospatial information at the level of tweets, we also present a web-based user interface to browse the "world of music" as seen by the "Twittersphere".

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A Novel Approach for Event Detection by Mining Spatio-temporal Information on Microblogs

Cited by 37 publications

References 9 publications

On the locality of keywords in Twitter streams

On the locality of keywords in Twitter streams

Collaborative Inference of Coexisting Information Diffusions

Exploring Geospatial Music Listening Patterns in Microblog Data

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