Bursty Events Research Articles

SSKG: Subject stream knowledge graph, a new approach for event detection from text

Events play a crucial role in shaping societal meaning and discourse. They are recorded in text data streams from social networks or online sources and analyzing them is vital for predicting and managing future occurrences. Methods for event detection from text data streams have been developed to enhance accuracy and efficiency in analyzing large datasets. This study focuses on events published as text data streams in Telegram. Two main perspectives are considered for better results: firstly, major events can overshadow the detection of smaller ones with fewer narrative instances. Secondly, due to diverse narratives surrounding each event, establishing meaningful connections between narrative sets is essential. In this research, two new concepts, “subject stream” and “stream graph,” are introduced. The subject stream aims to process topics to mitigate the impact of bursty events on detecting others. The stream graph models data stream and identifies various narratives associated with each event for better identification and categorization. Combining these approaches accurately represents events and their characteristics in the real world. Implementing the system in three versions demonstrates the effectiveness of using the “stream graph” alongside the “subject stream,” resulting in improved execution speed and accuracy. Evaluation results show a 6% enhancement in topic recall.

Improving CNN-Based Stock Trading by Considering Data Heterogeneity and Burst

In recent years, there have been quite a few attempts to apply intelligent techniques to financial trading, i.e., constructing automatic and intelligent trading framework based on historical stock price. Due to the unpredictable, uncertainty and volatile nature of financial market, researchers have also resorted to deep learning to construct the intelligent trading framework. In this paper, we propose to use CNN as the core functionality of such framework, because it is able to learn the spatial dependency (i.e., between rows and columns) of the input data. However, different with existing deep learning-based trading frameworks, we develop novel normalization process to prepare the stock data. In particular, we first empirically observe that the stock data is intrinsically heterogeneous and bursty, and then validate the heterogeneity and burst nature of stock data from a statistical perspective. Next, we design the data normalization method in a way such that the data heterogeneity is preserved and bursty events are suppressed. We verify out developed CNN-based trading framework plus our new normalization method on 29 stocks. Experiment results show that our approach can outperform other comparing approaches.

A stochastic model of edge-localized modes in magnetically confined plasmas.

Magnetically confined plasmas are far from equilibrium and pose considerable challenges in statistical analysis. We discuss a non-perturbative statistical method, namely a time-dependent probability density function (PDF) approach that is potentially useful for analysing time-varying, large, or non-Gaussian fluctuations and bursty events associated with instabilities in the low-to-high confinement transition and the H-mode. Specifically, we present a stochastic Langevin model of edge-localized modes (ELMs) by including stochastic noise terms in a previous ODE ELM model. We calculate exact time-dependent PDFs by numerically solving the Fokker-Planck equation and characterize time-varying statistical properties of ELMs for different energy fluxes and noise amplitudes. The stochastic noise is shown to introduce phase-mixing and plays a significant role in mitigating extreme bursts of large ELMs. Furthermore, based on time-dependent PDFs, we provide a path-dependent information geometric theory of the ELM dynamics and demonstrate its utility in capturing self-regulatory relaxation oscillations, bursts and a sudden change in the system. This article is part of a discussion meeting issue 'H-mode transition and pedestal studies in fusion plasmas'.

Construction of Optimal Membership Functions for a Fuzzy Routing Scheme in Opportunistic Mobile Networks

This article proposes FRIMF, a fuzzy routing scheme for opportunistic mobile networks (OMNs). In FRIMF, we exploit the pairwise intercontact times to evaluate the connection strength between nodes. Instead of assuming a random movement model, in the present case we consider node contact processes in OMNs as bursty events. Consequently, we introduce a burstiness parameter to characterize the variability in the dynamics of pairwise interactions. This variance metric, along with the statistical mean of pairwise intercontact times, is used to define a single FRIMF routing metric called closeness through a fuzzy inference system. This reflects the tie strength of the pair nodes. To improve the transmission environment, we further propose a method to develop optimal membership functions for the FRIMF’s fuzzy parameters based on the contact information. Particularly, we leverage the membership function elicitation techniques commonly used in collective opinion aggregations based on a direct rating process to establish the relevancy between vagueness estimates of the routing parameters and statistical distributions of the pairwise intercontact times in a way that eventually presents asymmetric triangular fuzzy numbers. In turn, these TFNs are used to properly define the fuzzy sets of the FRIMF’s routing parameters. Through simulations in the real human mobility environments, we show that FRIMF utilizing the enhanced asymmetric TFNs can outperform that using the typical symmetric TFNs developed based on our subjective preferences. Lastly, comparing with several algorithm benchmarks, we confirm the efficiency of FRIMF in transmission cost and delay.

Service quality monitoring in confined spaces through mining Twitter data

Promoting public transport depends on adapting effective tools for concurrent monitoring of perceived service quality. Social media feeds, in general, provide an opportunity to ubiquitously look for service quality events, but when applied to confined geographic area such as a transport node, the sparsity of concurrent social media data leads to two major challenges. Both the limited number of social media messages--leading to biased machine-learning--and the capturing of bursty events in the study period considerably reduce the effectiveness of general event detection methods. In contrast to previous work and to face these challenges, this paper presents a hybrid solution based on a novel fine-tuned BERT language model and aspect-based sentiment analysis. BERT enables extracting aspects from a limited context, where traditional methods such as topic modeling and word embedding fail. Moreover, leveraging aspect-based sentiment analysis improves the sensitivity of event detection. Finally, the efficacy of event detection is further improved by proposing a statistical approach to combine frequency-based and sentiment-based solutions. Experiments on a real-world case study demonstrate that the proposed solution improves the effectiveness of event detection compared to state-of-the-art approaches.

A Sparse Topic Model for Bursty Topic Discovery in Social Networks

Bursty topic discovery aims to automatically identify bursty events and continuously keep track of known events. The existing methods focus on the topic model. However, the sparsity of short text brings the challenge to the traditional topic models because the words are too few to learn from the original corpus. To tackle this problem, we propose a Sparse Topic Model (STM) for bursty topic discovery. First, we distinguish the modeling between the bursty topic and the common topic to detect the change of the words in time and discover the bursty words. Second, we introduce “Spike and Slab” prior to decouple the sparsity and smoothness of a distribution. The bursty words are leveraged to achieve automatic discovery of the bursty topics. Finally, to evaluate the effectiveness of our proposed algorithm, we collect Sina weibo dataset to conduct various experiments. Both qualitative and quantitative evaluations demonstrate that the proposed STM algorithm outperforms favorably against several state-of-the-art methods

Tearing Instability and Periodic Density Perturbations in the Slow Solar Wind

Abstract In contrast with the fast solar wind, which originates in coronal holes, the source of the slow solar wind is still debated. Often intermittent and enriched with low first ionization potential elements—akin to what is observed in closed coronal loops—the slow wind could form in bursty events nearby helmet streamers. Slow winds also exhibit density perturbations that have been shown to be periodic and could be associated with flux ropes ejected from the tip of helmet streamers, as shown recently by the WISPR white-light imager on board Parker Solar Probe (PSP). In this work, we propose that the main mechanism controlling the release of flux ropes is a flow-modified tearing mode at the heliospheric current sheet (HCS). We use magnetohydrodynamic simulations of the solar wind and corona to reproduce realistic configurations and outflows surrounding the HCS. We find that this process is able to explain long (∼10–20 hr) and short (∼1–2 hr) timescales of density structures observed in the slow solar wind. This study also sheds new light on the structure, topology, and composition of the slow solar wind, and could be, in the near future, compared with white light and in situ PSP observations.

Pattern Discovery in Internet Background Radiation

Internet Background Radiation (IBR) is observed in empty network address spaces. No traffic should arrive there, but it does in overwhelming quantities, gathering evidences of attacks, malwares and misconfigurations. The study of IBR helps to detect spreading network problems, common vulnerabilities and attack trends. However, network traffic data evolves quickly and is of high volume and diversity, i.e., an outstanding big data challenge. When used to assist network security, it also requires the online classification of dynamic streaming data. In this paper, we introduce an AGgregation & Mode (AGM) vector to represent network traffic. The AGM format characterizes IP hosts by extracting aggregated and mode values of IP header fields, and without inspecting payloads. We performed clustering and statistical analysis to explore six months of IBR from 2012 with the AGM mapping. The discovered patterns allow building a classification of IBR, which identifies phenomena that have been actively polluting the Internet for years. The AGM representation is light and tailored for monitoring and pattern discovery. We show that AGM vectors are suitable to analyze large volumes of network traffic: they capture permanent operations, such as long term scanning, as well as bursty events from targeted attacks and short term incidents.

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.

Fair Scheduling for Data Collection in Mobile Sensor Networks with Energy Harvesting

We consider the problem of data collection from a network of energy harvesting sensors, applied to tracking mobile assets in rural environments. Our application constraints favor a fair and energy-aware solution, with heavily duty-cycled sensor nodes communicating with powered base stations. We study a novel scheduling optimization problem for energy harvesting mobile sensor network, that maximizes the amount of collected data under the constraints of radio link quality and energy harvesting efficiency, while ensuring a fair data reception. We show that the problem is NP-complete and propose a heuristic algorithm to approximate the optimal scheduling solution in polynomial time. Moreover, our algorithm is flexible in handling progressive energy harvesting events, such as with solar panels, or opportunistic and bursty events, such as with Wireless Power Transfer. We use empirical link quality data, solar energy, and WPT efficiency to evaluate the proposed algorithm in extensive simulations and compare its performance to state-of-the-art. We show that our algorithm achieves high data reception rates, under different fairness and node lifetime constraints.

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.

A New Method to Detect Bursty Events from Micro-blog Posts Based on Bursty Topic Words and Agglomerative Hierarchical Clustering Algorithm

A New Method to Detect Bursty Events from Micro-blog Posts Based on Bursty Topic Words and Agglomerative Hierarchical Clustering Algorithm

TopicSketch: Real-Time Bursty Topic Detection from Twitter

Twitter has become one of the largest microblogging platforms for users around the world to share anything happening around them with friends and beyond. A bursty topic in Twitter is one that triggers a surge of relevant tweets within a short period of time, which often reflects important events of mass interest. How to leverage Twitter for early detection of bursty topics has therefore become an important research problem with immense practical value. Despite the wealth of research work on topic modelling and analysis in Twitter, it remains a challenge to detect bursty topics in real-time. As existing methods can hardly scale to handle the task with the tweet stream in real-time, we propose in this paper $\sf {TopicSketch}$ , a sketch-based topic model together with a set of techniques to achieve real-time detection. We evaluate our solution on a tweet stream with over 30 million tweets. Our experiment results show both efficiency and effectiveness of our approach. Especially it is also demonstrated that $\sf {TopicSketch}$ on a single machine can potentially handle hundreds of millions tweets per day, which is on the same scale of the total number of daily tweets in Twitter, and present bursty events in finer-granularity.

Analyzing and modeling heterogeneous behavior

Recently, it was pointed out that the non-Poisson statistics with heavy tail existed in many scenarios of human behaviors. But most of these studies claimed that power-law characterized diverse aspects of human mobility patterns. In this paper, we suggest that human behavior may not be driven by identical mechanisms and can be modeled as a Semi-Markov Modulated Process. To verify our suggestion and model, we analyzed a total of 1,619,934 records of library visitations (including undergraduate and graduate students). It is found that the distribution of visitation intervals is well fitted with three sections of lines instead of the traditional power law distribution in log–log scale. The results confirm that some human behaviors cannot be simply expressed as power law or any other simple functions. At the same time, we divided the data into groups and extracted period bursty events. Through careful analysis in different groups, we drew a conclusion that aggregate behavior might be composed of heterogeneous behaviors, and even the behaviors of the same type tended to be different in different period. The aggregate behavior is supposed to be formed by “heterogeneous groups”. We performed a series of experiments. Simulation results showed that we just needed to set up two states Semi-Markov Modulated Process to construct proper representation of heterogeneous behavior.

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.

Comprehensive survey of energetic electron events in Mercury's magnetosphere with data from the MESSENGER Gamma‐Ray and Neutron Spectrometer

AbstractData from the MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) Gamma‐Ray and Neutron Spectrometer have been used to detect and characterize energetic electron (EE) events in Mercury's magnetosphere. This instrument detects EE events indirectly via bremsstrahlung photons that are emitted when instrument and spacecraft materials stop electrons having energies of tens to hundreds of keV. From Neutron Spectrometer data taken between 18 March 2011 and 31 December 2013 we have identified 2711 EE events. EE event amplitudes versus energy are distributed as a power law and have a dynamic range of a factor of 400. The duration of the EE events ranges from tens of seconds to nearly 20 min. EE events may be classified as bursty (large variation with time over an event) or smooth (small variation). Almost all EE events are detected inside Mercury's magnetosphere on closed field lines. The precise occurrence times of EE events are stochastic, but the events are located in well‐defined regions with clear boundaries that persist in time and form what we call “quasi‐permanent structures.” Bursty events occur closer to dawn and at higher latitudes than smooth events, which are seen near noon‐to‐dusk local times at lower latitudes. A subset of EE events shows strong periodicities that range from hundreds of seconds to tens of milliseconds. The few‐minute periodicities are consistent with the Dungey cycle timescale for the magnetosphere and the occurrence of substorm events in Mercury's magnetotail region. Shorter periods may be related to phenomena such as north‐south bounce processes for the energetic electrons.

PHCM: A Particle Horizontal Cast Movement Based Model for Bursty Events Detection of Chinese Microblog

PHCM: A Particle Horizontal Cast Movement Based Model for Bursty Events Detection of Chinese Microblog

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.