Forecasting location-based events with spatio-temporal storytelling

Santos, Raimundo dos; Shah, Sumit; Chen, Feng; Boedihardjo, Arnold P.; Lu, Chang-Tien; Ramakrishnan, Naren

doi:10.1145/2755492.2755496

Cited by 6 publications

(4 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In line with other studies of civil unrest, our analysis uses data from newspaper reports of protests as a proxy for ground truth data on protest occurrences [ 34 – 36 ]. As a result, we cannot rule out the possibility that Flickr users are posting photographs labelled with a word signifying “protest” as a result of reading an article about protests in their country and region in The Guardian , or another news source.…”

Section: Resultsmentioning

confidence: 99%

“…Such ground truth data can be difficult to obtain. Most studies of civil unrest therefore rely on data from newspaper reports as a proxy for ground truth [ 22 , 34 – 36 ]. Following this approach, here we determine how many protest related articles for each of the 244 countries and regions were published in the online edition of The Guardian in each week in 2013.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Tracking Protests Using Geotagged Flickr Photographs

2016

View full text Add to dashboard Cite

Recent years have witnessed waves of protests sweeping across countries and continents, in some cases resulting in political and governmental change. Much media attention has been focused on the increasing usage of social media to coordinate and provide instantly available reports on these protests. Here, we investigate whether it is possible to identify protest outbreaks through quantitative analysis of activity on the photo sharing site Flickr. We analyse 25 million photos uploaded to Flickr in 2013 across 244 countries and regions, and determine for each week in each country and region what proportion of the photographs are tagged with the word “protest” in 34 different languages. We find that higher proportions of “protest”-tagged photographs in a given country and region in a given week correspond to greater numbers of reports of protests in that country and region and week in the newspaper The Guardian. Our findings underline the potential value of photographs uploaded to the Internet as a source of global, cheap and rapidly available measurements of human behaviour in the real world.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Tracking Protests Using Geotagged Flickr Photographs

2016

View full text Add to dashboard Cite

show abstract

“…MIL-GAN model is designed to learn the reward patterns given user-provided storylines and then applies the learned policy to unseen data. Santos et al [24] combined storytelling and Spatio-logical Inference (SLI) to generate rules of interaction among entities and measure how well they forecast a real-world event.…”

Section: St-storytelling Componentmentioning

confidence: 99%

K2: A Novel Data Analysis Framework to Understand US Emotions in Space and Time

Banerjee

Elgarroussi

Wang

et al. 2019

Int. J. Semantic Computing

View full text Add to dashboard Cite

Twitter is one of the most popular social media platforms used by millions of users daily to post their opinions and emotions. Consequently, Twitter tweets have become a valuable knowledge source for emotion analysis. In this paper, we present a new framework, K2, for tweet emotion mapping and emotion change analysis. It introduces a novel, generic spatio-temporal data analysis and storytelling framework that can be used to understand the emotional evolution of a specific section of population. The input for our framework is the location and time of where and when the tweets were posted and an emotion assessment score in the range [Formula: see text], with [Formula: see text] representing a very high positive emotion and [Formula: see text] representing a very high negative emotion. Our framework first segments the input dataset into a number of batches with each batch representing a specific time interval. This time interval can be a week, a month or a day. By generalizing existing kernel density estimation techniques in the next step, we transform each batch into a continuous function that takes positive and negative values. We have used contouring algorithms to find the contiguous regions with highly positive and highly negative emotions belonging to each member of the batch. Finally, we apply a generic, change analysis framework that monitors how positive and negative emotion regions evolve over time. In particular, using this framework, unary and binary change predicate are defined and matched against the identified spatial clusters, and change relationships will then be recorded, for those spatial clusters for which a match occurs. We also propose animation techniques to facilitate spatio-temporal data storytelling based on the obtained spatio-temporal data analysis results. We demo our approach using tweets collected in the state of New York in the month of June 2014.

show abstract

“…ConceptRank based storytelling is explored in Santos et al (2013). Forecasting events based on spatio-temporal storytelling is described in Santos et al (2014).…”

Section: Storytellingmentioning

confidence: 99%

DISCRN: A Distributed Storytelling Framework for Intelligence Analysis

et al. 2017

Self Cite

View full text Add to dashboard Cite

Storytelling connects entities (people, organizations) using their observed relationships to establish meaningful storylines. This can be extended to spatiotemporal storytelling that incorporates locations, time, and graph computations to enhance coherence and meaning. But when performed sequentially these computations become a bottleneck because the massive number of entities make space and time complexity untenable. This article presents DISCRN, or distributed spatiotemporal ConceptSearch-based storytelling, a distributed framework for performing spatiotemporal storytelling. The framework extracts entities from microblogs and event data, and links these entities using a novel ConceptSearch to derive storylines in a distributed fashion utilizing key-value pair paradigm. Performing these operations at scale allows deeper and broader analysis of storylines. The novel parallelization techniques speed up the generation and filtering of storylines on massive datasets. Experiments with microblog posts such as Twitter data and Global Database of Events, Language, and Tone events show the efficiency of the techniques in DISCRN.

show abstract

Forecasting location-based events with spatio-temporal storytelling

Cited by 6 publications

References 17 publications

Tracking Protests Using Geotagged Flickr Photographs

Tracking Protests Using Geotagged Flickr Photographs

K2: A Novel Data Analysis Framework to Understand US Emotions in Space and Time

DISCRN: A Distributed Storytelling Framework for Intelligence Analysis

Contact Info

Product

Resources

About