Bursty Event Research Articles

Markov-modulated Hawkes processes for modeling sporadic and bursty event occurrences in social interactions

Modeling event dynamics is central to many disciplines. Patterns in observed social interaction events can be commonly modeled using point processes. Such social interaction event data often exhibit self-exciting, heterogeneous and sporadic trends which is challenging for conventional models. It is reasonable to assume that there exists a hidden state process that drives different event dynamics at different states. In this paper we propose a Markov modulated Hawkes process (MMHP) model for learning such a mixture of social interaction event dynamics and develop corresponding inference algorithms. Numerical experiments using synthetic data demonstrate that MMHP with the proposed estimation algorithms consistently recover the true hidden state process in simulations, while email data from a large university and data from an animal behavior study show that the procedure captures distinct event dynamics that reveal interesting social structures in the real data.

High star cluster formation efficiency in the strongly lensed Sunburst Lyman-continuum galaxy at z = 2.37

We investigate the strongly lensed (μ ≃ ×10 − 100) Lyman continuum (LyC) galaxy, dubbed Sunburst, at z = 2.37, taking advantage of a new accurate model of the lens. A characterization of the intrinsic (delensed) properties of the system yields a size of ≃3 sq. kpc, a luminosity of MUV = −20.3, and a stellar mass of M ≃ 109 M⊙; 16% of the ultraviolet light is located in a 3 Myr old gravitationally bound young massive star cluster (YMC), with an effective radius of ∼8 pc (corresponding to 1 milliarcsec without lensing) and a dynamical mass of ∼107 M⊙ (similar to the stellar mass) – from which LyC radiation is detected (λ < 912 Å). The star formation rate and stellar mass surface densities for the YMC are Log10(ΣSFR[M⊙ yr−1 kpc−2]) ≃ 3.7 and Log10(ΣM[M⊙ pc−2]) ≃ 4.1, with sSFR > 330 Gyr−1, consistent with the values observed in local young massive star clusters. The inferred outflowing gas velocity (> 300 km s−1) exceeds the escape velocity of the cluster. The resulting relative escape fraction of the ionizing radiation emerging from the entire galaxy is higher than 6−12%, whilst it is ≳46 − 93% if inferred from the YMC multiple line of sights. At least 12 additional unresolved star-forming knots with radii spanning the interval 3 − 20 pc (the majority of them likely gravitationally bound star clusters) are identified in the galaxy. A significant fraction (40−60%) of the ultraviolet light of the entire galaxy is located in such bound star clusters. In adopting a formation timescale of the star clusters of 20 Myr, a cluster formation efficiency Γ ≳ 30%. The star formation rate surface density of the Sunburst galaxy (Log10(ΣSFR) = 0.5−0.2+0.3) is consistent with the high inferred Γ, as observed in local galaxies experiencing extreme gas physical conditions. Overall, the presence of a bursty event (i.e., the 3 Myr old YMC with large sSFR) significantly influences the morphology (nucleation), photometry (photometric jumps), and spectroscopic output (nebular emission) of the entire galaxy. Without lensing magnification, the YMC would be associated to an unresolved 0.5 kpc–size star-forming clump. The delensed LyC and UV magnitude m1600 (at 1600 Å) of the YMC are ≃30.6 and ≃26.9, whilst the entire galaxy has m1600 ≃ 24.8. The Sunburst galaxy shows a relatively large rest-frame equivalent width of EWrest(Hβ + [O III]λλ4959, 5007) ≃ 450 Å, with the YMC contributing to ∼30% (having a local EWrest ≃ 1100 Å) and ∼1% of the total stellar mass. If O-type (ionizing) stars are mainly forged in star clusters, then such engines were the key ionizing agents during reionization and the increasing occurrence of high equivalent width lines (Hβ + [O III]) observed at z > 6.5 might be an indirect signature of a high frequency of forming massive star clusters (or high Γ) at reionization. Future facilities, which will perform at few tens milliarcsec resolution (e.g., VLT/MAVIS or ELT), will probe bound clusters on moderately magnified (μ < 5 − 10) galaxies across cosmic epochs up to reionization.

An approach of Bursty event detection in social networks based on topological features

An approach of Bursty event detection in social networks based on topological features

Burst: real-time events burst detection in social text stream.

Gigantic growth of social media and unbeatable trend of progress in the direction of the web seeking user’s interests have generated a storm of social text streams. Seeking information to know the phenomenon of various events in the early stages is quite interesting. Various kinds of social media live streams attract users to participate in real-time events to become a part of an immense crowd. However, the vast amount of text is present on social media, the unnecessary information bogs a social text stream filtering to extract the appropriate topics and events effectively. Therefore, detecting, classifying, and identifying burst events is quite challenging due to the sparse and noisy text of Twitter. The researchers' significant open challenges are the effective cleaning and profound representation of the text stream data. This research article's main contribution is to provide a detailed study and explore bursty event detection in the social text stream. Thus, this work's main motive is to present a concise approach that classifies and detects the event keywords and maintains the record of the event based on related features. These features permit the approach to successfully determine the booming pattern of events scrupulously at different time span. Experiments are conducted and compared with the state-of-the-art methods, which reveals that the proposed approach is proficient to detect valuable patterns of interest and also achieve better scoresto extract burst events on social media posted by various users.

Bursty Event Detection in Twitter Streams

Social media, in recent years, have become an invaluable source of information for both public and private organizations to enhance the comprehension of people interests and the onset of new events. Twitter, especially, allows a fast spread of news and events happening real time that can contribute to situation awareness during emergency situations, but also to understand trending topics of a period. The article proposes an online algorithm that incrementally groups tweet streams into clusters. The approach summarizes the examined tweets into the cluster centroid by maintaining a number of textual and temporal features that allow the method to effectively discover groups of interest on particular themes. Experiments on messages posted by users addressing different issues, and a comparison with state-of-the-art approaches show that the method is capable to detect discussions regarding topics of interest, but also to distinguish bursty events revealed by a sudden spreading of attention on messages published by users.

Sina Weibo Bursty Event Detection Method

In response to the recent frequent outbreaks of bursty events, if the supervision is not strengthened, the grievances caused by it will have an extremely negative impact on society. Therefore, how to effectively detect bursty events in social networks has become the focus of research. In order to eliminate the interference of local daily events generated by noisy data, this paper proposes a bursty event detection method for quantifying the influence of microblog text. Through the analysis of high-impact microblogs, the burst words are mined, the potential bursty event data sets are constructed, and the k-means cluster analysis method is applied to detect the event. In order to verify the method, this paper compares with the commonly used detection methods on the real dataset. The experimental results show that the method has certain improvement in accuracy and efficiency.

Sentiment-based and hashtag-based Chinese online bursty event detection

How to detect bursty events in data streams on social media is a hot research topic in natural language processing. However, current methods for extracting bursty events suffer from poor accuracy a...

Sentiment-based and hashtag-based Chinese online bursty event detection

How to detect bursty events in data streams on social media is a hot research topic in natural language processing. However, current methods for extracting bursty events suffer from poor accuracy and low efficiency. Fortunately, sentiment analysis has been applied to event detection, which has improved the performance greatly. Inspired by this, this paper proposes a new model which utilizes sentiment analysis for Chinese bursty event detection. First, we build a sentiment co-occurrence graph offline and apply it to analyze microblog sentiment. Plutchik’s emotion wheel is the base for the sentiment classification of the graph. Second, sentiment is used as features to detect bursts in microblog streams online. At last, we exploit regular expressions to extract hashtags in bursty periods and segment hashtags into keywords. By using mutual information and frequent patterns, we fetch words relevant to hashtags as keywords to form events. This approach can detect bursty events online while analyzing the sentiment of microblogs. The experimental results on a large real dataset show that our method can detect bursty events with higher accuracy in a shorter time than traditional methods.

Bursty event detection from microblog: a distributed and incremental approach

SummaryAs a new form of social media, microblogs (e.g., Twitter and Weibo) are playing an important role in people's daily life. With the rise in popularity and size of microblogs, there is a need for distributed approaches that can detect bursty event with low latency from the short‐text data stream. In this paper, we propose a distributed and incremental temporal topic model for microblogs called Bursty Event dEtection (BEE+). BEE+ is able to detect bursty events from short‐text dataset and model the temporal information. And BEE+ processes the post‐stream incrementally to track the topic drifting of events over time. Therefore, the latent semantic indices are preserved from one time period to the next. In order to achieve real‐time processing, we design a distributed execution framework based on Spark engine. To verify its ability to detect bursty event, we conduct experiments on a Weibo dataset of 6,360,125 posts. The results show that BEE+ can outperform the baselines for detecting the meaningful bursty events and track the topic drifting. Copyright © 2015 John Wiley & Sons, Ltd.

CLEar

We describe our demonstration of CLEar ( Cl airaudient Ear ), a real-time online platform for detecting, monitoring, summarizing, contextualizing and visualizing bursty and viral events, those triggering a sudden surge of public interest and going viral on micro-blogging platforms. This task is challenging for existing methods as they either use complicated topic models to analyze topics in a off-line manner or define temporal structure of fixed granularity on the data stream for online topic learning, leaving them hardly scalable for real-time stream like that of Twitter. In this demonstration of CLEar, we present a three-stage system: First, we show a real-time bursty event detection module based on a data-sketch topic model which makes use of acceleration of certain stream quantities as the indicators of topic burstiness to trigger efficient topic inference. Second, we demonstrate popularity prediction for the detected bursty topics and event summarization based on clustering related topics detected in successive time periods. Third, we illustrate CLEar's module for contextualizing and visualizing the event evolution both along time-line and across other news media to offer an easier understanding of the events.

Whisper: Tracing the Spatiotemporal Process of Information Diffusion in Real Time.

When and where is an idea dispersed? Social media, like Twitter, has been increasingly used for exchanging information, opinions and emotions about events that are happening across the world. Here we propose a novel visualization design, "Whisper", for tracing the process of information diffusion in social media in real time. Our design highlights three major characteristics of diffusion processes in social media: the temporal trend, social-spatial extent, and community response of a topic of interest. Such social, spatiotemporal processes are conveyed based on a sunflower metaphor whose seeds are often dispersed far away. In Whisper, we summarize the collective responses of communities on a given topic based on how tweets were retweeted by groups of users, through representing the sentiments extracted from the tweets, and tracing the pathways of retweets on a spatial hierarchical layout. We use an efficient flux line-drawing algorithm to trace multiple pathways so the temporal and spatial patterns can be identified even for a bursty event. A focused diffusion series highlights key roles such as opinion leaders in the diffusion process. We demonstrate how our design facilitates the understanding of when and where a piece of information is dispersed and what are the social responses of the crowd, for large-scale events including political campaigns and natural disasters. Initial feedback from domain experts suggests promising use for today's information consumption and dispersion in the wild.

Bursty event detection from collaborative tags

Collaborative tagging have emerged as a ubiquitous way to annotate and organize online resources. As a kind of descriptive keyword, large amount of tags are created and associated to multiple types...

Online detection of bursty events and their evolution in news streams

Online monitoring of temporally-sequenced news streams for interesting patterns and trends has gained popularity in the last decade. In this paper, we study a particular news stream monitoring task: timely detection of bursty events which have happened recently and discovery of their evolutionary patterns along the timeline. Here, a news stream is represented as feature streams of tens of thousands of features (i.e., keyword. Each news story consists of a set of keywords.). A bursty event therefore is composed of a group of bursty features, which show bursty rises in frequency as the related event emerges. In this paper, we give a formal definition to the above problem and present a solution with the following steps: (1) applying an online multi-resolution burst detection method to identify bursty features with different bursty durations within a recent time period; (2) clustering bursty features to form bursty events and associating each event with a power value which reflects its bursty level; (3) applying an information retrieval method based on cosine similarity to discover the event’s evolution (i.e., highly related bursty events in history) along the timeline. We extensively evaluate the proposed methods on the Reuters Corpus Volume 1. Experimental results show that our methods can detect bursty events in a timely way and effectively discover their evolution. The power values used in our model not only measure event’s bursty level or relative importance well at a certain time point but also show relative strengths of events along the same evolution.