A survey on real-time event detection from the Twitter data stream

2017 ◽  
Vol 44 (4) ◽  
pp. 443-463 ◽  
Author(s):  
Mahmud Hasan ◽  
Mehmet A Orgun ◽  
Rolf Schwitter
Keyword(s):  
Real Time ◽  
Event Detection ◽  
Data Stream ◽  
Detection Methods ◽  
Current State ◽  
Twitter Data ◽  
Starting Point ◽  
State Of Affairs ◽  
Collective Information ◽  
Insight Into

The proliferation of social networking services has resulted in a rapid growth of their user base, spanning across the world. The collective information generated from these online platforms is overwhelming, in terms of both the amount of content produced every moment and the diversity of topics discussed. The real-time nature of the information produced by users has prompted researchers to analyse this content, in order to gain timely insight into the current state of affairs. Specifically, the microblogging service Twitter has been a recent focus of researchers to gather information on events occurring in real time. This article presents a survey of a wide variety of event detection methods applied to streaming Twitter data, classifying them according to shared common traits, and then discusses different aspects of the subtasks and challenges involved in event detection. We believe this survey will act as a guide and starting point for aspiring researchers to gain a structured view on state-of-the-art real-time event detection and spur further research in this direction.

Real-time event detection from the Twitter data stream using the TwitterNews+ Framework

2019 ◽  
Vol 56 (3) ◽  
pp. 1146-1165 ◽  
Author(s):  
Mahmud Hasan ◽  
Mehmet A. Orgun ◽  
Rolf Schwitter
Keyword(s):  
Real Time ◽  
Event Detection ◽  
Data Stream ◽  
Twitter Data

scholarly journals Classification Connection of Twitter Data using K-Means Clustering

2019 ◽  
Vol 8 (6S4) ◽  
pp. 14-22
Keyword(s):  
Social Media ◽  
Real Time ◽  
Clustering Methods ◽  
Time Analysis ◽  
Data Set ◽  
Dynamic Data ◽  
Real Time Analysis ◽  
Twitter Data ◽  
Social Media Platforms ◽  
Insight Into

The rise of social media platforms like Twitter and the increasing adoption by people in order to stay connected provide a large source of data to perform analysis based on the various trends, events and even various personalities. Such analysis also provides insight into a person’s likes and inclinations in real time independent of the data size. Several techniques have been created to retrieve such data however the most efficient technique is clustering. This paper provides an overview of the algorithms of the various clustering methods as well as looking at their efficiency in determining trending information. The clustered data may be further classified by topics for real time analysis on a large dynamic data set. In this paper, data classification is performed and analyzed for flaws followed by another classification on the same data set.

scholarly journals Real-Time Brain-Machine Interface Achieves High-Velocity Prosthetic Finger Movements using a Biologically-Inspired Neural Network Decoder

2021 ◽  
Author(s):  
Matthew S. Willsey ◽  
Samuel R. Nason ◽  
Scott R. Ensel ◽  
Hisham Temmar ◽  
Matthew J. Mender ◽  
...  
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Finger Movements ◽  
Feed Forward Neural Network ◽  
Current Standard ◽  
Biologically Inspired ◽  
Current State ◽  
The Neural Network ◽  
Starting Point ◽  
Prosthetic Finger

AbstractDespite the rapid progress and interest in brain-machine interfaces that restore motor function, the performance of prosthetic fingers and limbs has yet to mimic native function. The algorithm that converts brain signals to a control signal for the prosthetic device is one of the limitations in achieving rapid and realistic finger movements. To achieve more realistic finger movements, we developed a shallow feed-forward neural network, loosely inspired by the biological neural pathway, to decode real-time two-degree-of-freedom finger movements. Using a two-step training method, a recalibrated feedback intention–trained (ReFIT) neural network achieved a higher throughput with higher finger velocities and more natural appearing finger movements than the ReFIT Kalman filter, which represents the current standard. The neural network decoders introduced herein are the first to demonstrate real-time decoding of continuous movements at a level superior to the current state-of-the-art and could provide a starting point to using neural networks for the development of more naturalistic brain-controlled prostheses.

scholarly journals Reconstructing Vindonissa as a living document – A case-study of digital reconstruction for output to pre-rendered and real-time applications

2017 ◽  
Vol 1 (2) ◽  
pp. 396-408
Author(s):  
Jonas Christen
Keyword(s):  
Real Time ◽  
Time Use ◽  
3D Models ◽  
Digital Reconstruction ◽  
Current State ◽  
Starting Point ◽  
Reconstructive Process ◽  
The Alps ◽  
State Of Research

The legion camp “Vindonissa” in Switzerland is considered one of the most important roman sites north of the alps. Research there has been going on for over a century and reconstructive drawings have always been a way to showcase scientific findings of the site, the earliest of them dating back as far as 1909.In 2015, it was decided to produce a new series of illustrations. The whole camp and its surrounding settlements had to be constructed as hand-generated 3D models, allowing for quick changes during the reconstructive process and flexibility in future adaptations. Topographical data, archaeological plans as well as building profiles provided by experts were the basis for the model.The main focus was on a general impression of the camp and not on individual buildings but some landmarks as the newly postulated circus were crafted with a higher level of detail as they are the topic of scientific discourse and it helps the discussion if they have a certain fidelity. The circus also serves as a good example for the value of the imaging process in research: Only after trying to fit it into the topography it was noted that it would overlap with a street that was previously thought to run through this area. In the discussion between archaeologists and illustrators a new path for the street was chosen that fits into the landscape and is scientifically acceptable.The new series of illustrations was originally published in the annual report of the archaeological society Vindonissa. The resulting model represents the current state of research but mainly serves as a starting point for future discussion: All buildings are constructed so that they can easily be adapted for real-time use and a Virtual Reality application is the logical next step for its use. 

Using 3D Convolutional Neural Networks for Real-time Detection of Soccer Events

2021 ◽  
Vol 15 (02) ◽  
pp. 161-187
Author(s):  
Olav A. Nergård Rongved ◽  
Steven A. Hicks ◽  
Vajira Thambawita ◽  
Håkon K. Stensland ◽  
Evi Zouganeli ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Event Detection ◽  
State Of The Art ◽  
Time Estimation ◽  
High Recall ◽  
Current State ◽  
Ablation Study ◽  
Different Parts

Developing systems for the automatic detection of events in video is a task which has gained attention in many areas including sports. More specifically, event detection for soccer videos has been studied widely in the literature. However, there are still a number of shortcomings in the state-of-the-art such as high latency, making it challenging to operate at the live edge. In this paper, we present an algorithm to detect events in soccer videos in real time, using 3D convolutional neural networks. We test our algorithm on three different datasets from SoccerNet, the Swedish Allsvenskan, and the Norwegian Eliteserien. Overall, the results show that we can detect events with high recall, low latency, and accurate time estimation. The trade-off is a slightly lower precision compared to the current state-of-the-art, which has higher latency and performs better when a less accurate time estimation can be accepted. In addition to the presented algorithm, we perform an extensive ablation study on how the different parts of the training pipeline affect the final results.

scholarly journals Real-time traffic event detection using Twitter data

2018 ◽  
Vol 5 (3) ◽  
pp. 77-84 ◽  
Author(s):  
Angelica Salas Jones ◽  
Panagiotis Georgakis ◽  
Yannis Petalas ◽  
Renukappa Suresh
Keyword(s):  
Real Time ◽  
Event Detection ◽  
Real Time Traffic ◽  
Twitter Data

Real-time Event Detection on Social Data Stream

2014 ◽  
Vol 20 (4) ◽  
pp. 475-486 ◽  
Author(s):  
Duc T. Nguyen ◽  
Jason J. Jung
Keyword(s):  
Real Time ◽  
Event Detection ◽  
Data Stream ◽  
Social Data
Sign in / Sign up

Export Citation Format

Share Document