A conceptual framework for studying collective reactions to events in location-based social media

Dunkel, Alexander; Andrienko, Gennady; Andrienko, Gennady; Burghardt, Dirk; Hauthal, Eva; Purves, Ross S.

doi:10.1080/13658816.2018.1546390

Cited by 41 publications

(31 citation statements)

References 46 publications

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“…This particular type of propagation can be modeled by a network of entities, whose links among entities enable exchange patterns over space and time. These models have been used for instance to analyze disease spread [7,41,50] and diffusion on social media [27].…”

Section: Characterizing Spatio-temporal Dynamicsmentioning

confidence: 99%

A Comparison of Geographical Propagation Visualizations

Peña-Araya

Bezerianos

Pietriga

2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

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Geographical propagation phenomena occur in multiple domains, such as in epidemiology and social media. Propagation dynamics are often complex, and visualizations play a key role in helping subject-matter experts understand and analyze them. However, there is little empirical data about the effectiveness of the various strategies used to visualize geographical propagation. To fill this gap, we conduct an experiment to evaluate the effectiveness of three strategies: an animated map, smallmultiple maps, and a single map with glyphs. We compare them under five tasks that vary in one of the following dimensions: propagation scope, direction, speed, peaks, and spatial jumps. Our results show that small-multiple maps perform best overall, but that the effectiveness of each visualization varies depending on the task considered.

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Section: Characterizing Spatio-temporal Dynamicsmentioning

confidence: 99%

A Comparison of Geographical Propagation Visualizations

Peña-Araya

Bezerianos

Pietriga

2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

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“…The need for new methods to represent the spatiotemporal events and relationships is important due to its impact on STDM modelling Dunkel et al. ( 2019 ). Recent advances in deep learning have introduced spatially-structured networks such as graph convolutional networks Hamdi et al.…”

Section: General Stdm Challenges and Research Gapsmentioning

confidence: 99%

Spatiotemporal data mining: a survey on challenges and open problems

et al. 2021

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Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay between space and time. Several available surveys capture STDM advances and report a wealth of important progress in this field. However, STDM challenges and problems are not thoroughly discussed and presented in articles of their own. We attempt to fill this gap by providing a comprehensive literature survey on state-of-the-art advances in STDM. We describe the challenging issues and their causes and open gaps of multiple STDM directions and aspects. Specifically, we investigate the challenging issues in regards to spatiotemporal relationships, interdisciplinarity, discretisation, and data characteristics. Moreover, we discuss the limitations in the literature and open research problems related to spatiotemporal data representations, modelling and visualisation, and comprehensiveness of approaches. We explain issues related to STDM tasks of classification, clustering, hotspot detection, association and pattern mining, outlier detection, visualisation, visual analytics, and computer vision tasks. We also highlight STDM issues related to multiple applications including crime and public safety, traffic and transportation, earth and environment monitoring, epidemiology, social media, and Internet of Things.

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“…Aside from community engagement , localized opinion mining can be performed using geo-social media, and have been used to elicit a response for participatory urban planning [16]. Opinion mining is often linked to responses towards an event, and collective reactions to an event has been studied [17] to infer a collective reaction across multiple geo-social media users.…”

Section: Introductionmentioning

confidence: 99%

Fusing location and text features for sentiment classification

Lim¹,

Ho²,

Ting³

2020

IJETT

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Geo-tagged Twitter data has been used recently to infer insights on the human aspects of social media. Insights related to demographics, spatial distribution of cultural activities, space-time travel trajectories for humans as well as happiness has been mined from geo-tagged twitter data in recent studies. To date, not much study has been done on the impact of the geo-location features of a Tweet on its sentiment. This observation has inspired us to propose the usage of geo-location features as a method to perform sentiment classification. In this method, the sentiment classification of geo-tagged tweets is performed by concatenating geo-location features and one-hot encoded word vectors as inputs for convolutional neural networks (CNN) and long short-term memory (LSTM) networks. The addition of language-independent features in the form of geo-location features has helped to enrich the tweet representation in order to combat the sparse nature of short tweet message. The results achieved has demonstrated that concatenating geo-location features to one-hot encoded word vectors can achieve higher accuracy as compared to the usage of word vectors alone for the purpose of sentiment classification.

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A conceptual framework for studying collective reactions to events in location-based social media

Cited by 41 publications

References 46 publications

A Comparison of Geographical Propagation Visualizations

A Comparison of Geographical Propagation Visualizations

Spatiotemporal data mining: a survey on challenges and open problems

Fusing location and text features for sentiment classification

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