Spatiotemporal Event Forecasting in Social Media

ACM Transactions on Spatial Algorithms and Systems, 2016

Event forecasting from social media data streams has many applications. Existing approaches focus on forecasting temporal events (such as elections and sports) but as yet cannot forecast spatiotemporal events such as civil unrest and influenza outbreaks, which are much more challenging. To achieve spatiotemporal event forecasting, spatial features that evolve with time and their underlying correlations need to be considered and characterized. In this article, we propose novel batch and online approaches for spatiotemporal event forecasting in social media such as Twitter. Our models characterize the underlying development of future events by simultaneously modeling the structural contexts and their spatiotemporal burstiness based on different strategies. Both batch and online-based inference algorithms are developed to optimize the model parameters. Utilizing the trained model, the alignment likelihood of tweet sequences is calculated by dynamic programming. Extensive experimental e...

Journal of Critical Reviews, 2020

Many modern methodologies are used in recent years' by various researchers and contributed to develop real-time event detection frameworks during crisis events. Initially, the literature was focused on the state of art methods related to Twitter characterization towards event identification based on emerging burst topics specific to location. The two major approaches namely document and feature based event detection are discussed. The state of art methods machine learning and its privacy challenges are also studied. A detailed study of the existing works related to location based burst event detection is discussed as a comprehensive review.

Proceedings of the ACM SIGMOD Workshop on Databases and Social Networks - DBSocial '13, 2013

Unprecedented success and active usage of social media services result in massive amounts of user-generated data. An increasing interest in the contained information from social media data leads to more and more sophisticated analysis and visualization applications. Because of the fast pace and distribution of news in social media data it is an appropriate source to identify events in the data and directly display their occurrence to analysts or other users. This paper presents a method for event identification in local areas using the Twitter data stream. We implement and use a combined log-likelihood ratio approach for the geographic and time dimension of real-life Twitter data in predefined areas of the world to detect events occurring in the message contents. We present a case study with two interesting scenarios to show the usefulness of our approach.

As the social media has gained more attention from users on the Internet, social media has been one of the most important information sources in the world. And, with the increasing popularity of social media, data which is posted on social media sites are rapidly becoming popular, which is a term used to refer to new media that is replacing traditional media. In this paper, we concentrate on geotagged tweets on the Twitter site. These geotagged tweets are known to as georeferenced documents because they include not only a short text message, but also have documents’ which are posting time and location. Many researchers have been handling the development of new data mining techniques for georeferenced documents to recognize and analyze emergency topics, such as natural disasters, weather, diseases, and other incidents. In particular, the utilization of geotagged tweets to recognize and analyze natural disasters has received much attention from administrative agencies recently because some case studies have achieved compelling results. In this paper, we propose a novel real-time analysis application for identifying bursty local areas related to emergency topics. The aim of our application is to provide new platforms that can identify and analyze the localities of emergency topics. The proposed application is of three core computational intelligence techniques: the Naive Bayes classifier technique, the spatiotemporal clustering technique, and the burst detection technique. Also, we have implemented two types of application: a Web application interface and an android application. To evaluate the proposed application, we have implemented a real-time weather observation system embedded the proposed application. We used actual crawling geotagged tweets posted on the Twitter site. The weather detection system

2011

Abstract Social media services like Twitter, Flickr and You Tube publish high volumes of user generated content as a major event occurs, making them a potential data source for event analysis. The large volume and noisy content of social media makes automatic preprocessing essential. Intuitively, the event-related data falls into three major phases: the buildup to the event, the event itself, and the post-event effects and repercussions.

Proceedings of the 29th on Hypertext and Social Media, 2018

Small-scale events are emerging as attractive objects of research. On Twitter, small-scale events represent weak sensors that report things happening in specific times and places. While previous work addressed the issue of detecting such events, very little is known so far about their inherent properties. In this paper, our main objective was to analyse the spatio-temporal peculiarities of small-scale events w.r.t different levels of location granularity, and to understand the general trend of their propagation along their lifetimes. Our findings suggest that (1) users involved in small-scale events mostly gravitate not significantly far from the geographical focus; (2) events do not exhibit major peaks; and (3) there exists distinct events that we can identify from users' posts that significantly differ from topic distribution, focus concentration and propagation distance perspectives across time.

Journal of Big Data

A key challenge in mining social media data streams is to identify events which are actively discussed by a group of people in a specific local or global area. Such events are useful for early warning for accident, protest, election or breaking news. However, neither the list of events nor the resolution of both event time and space is fixed or known beforehand. In this work, we propose an online spatio-temporal event detection system using social media that is able to detect events at different time and space resolutions. First, to address the challenge related to the unknown spatial resolution of events, a quad-tree method is exploited in order to split the geographical space into multiscale regions based on the density of social media data. Then, a statistical unsupervised approach is performed that involves Poisson distribution and a smoothing method for highlighting regions with unexpected density of social posts. Further, event duration is precisely estimated by merging events...

Companion Proceedings of the Web Conference 2022

Understanding and prediction of spreading phenomena are vital for numerous applications. The massive availability of social network data provides a platform for studying spreading phenomena. Past works studying and predicting spreading phenomena have explored the spread in dimensions of time and volume, such as predicting total infected users, predicting popularity, predicting the time when content receives a threshold number of infected users. However, as the information spreads from user to user, it also spreads from location to location. In this paper, we attempt to predict the spread in the dimension of geographic space. In accordance with the past spreading prediction problems, we design our problem to predict the spatial spread at an early stage. For this, we utilized spatial features, social features, and emotion features. We feed these features into existing classification algorithms and evaluate on three datasets from Twitter. CCS CONCEPTS • Information systems → Information retrieval; Web mining; • Social and professional topics → Geographic characteristics.

IEEE Transactions on Visualization and Computer Graphics, 2000

Current visual analytics systems provide users with the means to explore trends in their data. Linked views and interactive displays provide insight into correlations among people, events, and places in space and time. Analysts search for events of interest through statistical tools linked to visual displays, drill down into the data, and form hypotheses based upon the available information. However, current systems stop short of predicting events. In spatiotemporal data, analysts are searching for regions of space and time with unusually high incidences of events (hotspots). In the cases where hotspots are found, analysts would like to predict how these regions may grow in order to plan resource allocation and preventative measures. Furthermore, analysts would also like to predict where future hotspots may occur. To facilitate such forecasting, we have created a predictive visual analytics toolkit that provides analysts with linked spatiotemporal and statistical analytic views. Our system models spatiotemporal events through the combination of kernel density estimation for event distribution and seasonal trend decomposition by loess smoothing for temporal predictions. We provide analysts with estimates of error in our modeling, along with spatial and temporal alerts to indicate the occurrence of statistically significant hotspots. Spatial data are distributed based on a modeling of previous event locations, thereby maintaining a temporal coherence with past events. Such tools allow analysts to perform real-time hypothesis testing, plan intervention strategies, and allocate resources to correspond to perceived threats.

Proceedings of the 26th International Conference on World Wide Web Companion - WWW '17 Companion, 2017

Social media response to catastrophic events, such as natural disasters or terrorist attacks, has received a lot of attention. However, social media are also extremely important in the context of planned events, such as fairs, exhibits, festivals, as they play an essential role in communicating them to fans, interest groups, and the general population. These kinds of events are geo-localized within a city or territory and are scheduled within a public calendar. We consider a specific scenario, the Milano Fashion Week (MFW), which is an important event in our city. We focus our attention on the coverage of social content in space, measuring the propagation of the event in the territory. We build different clusters of fashion brands, we characterize several features of propagation in space and we correlate them to the popularity of involved actors. We show that the clusters along space and popularity dimensions are loosely correlated, and that domain experts are typically able to understand and identify only popularity aspects, while they are completely unaware of spatial dynamics of social media response to the events.