scholarly journals Streaming Social Event Detection and Evolution Discovery in Heterogeneous Information Networks

2021 ◽  
Vol 15 (5) ◽  
pp. 1-33
Author(s):  
Hao Peng ◽  
Jianxin Li ◽  
Yangqiu Song ◽  
Renyu Yang ◽  
Rajiv Ranjan ◽  
...  
Keyword(s):  
Real Time ◽  
Real World ◽  
Event Detection ◽  
Social Event ◽  
Heterogeneous Information ◽  
Heterogeneous Information Networks ◽  
Social Events ◽  
Meta Path ◽  
Social Event Detection ◽  
Event Based

Events are happening in real world and real time, which can be planned and organized for occasions, such as social gatherings, festival celebrations, influential meetings, or sports activities. Social media platforms generate a lot of real-time text information regarding public events with different topics. However, mining social events is challenging because events typically exhibit heterogeneous texture and metadata are often ambiguous. In this article, we first design a novel event-based meta-schema to characterize the semantic relatedness of social events and then build an event-based heterogeneous information network (HIN) integrating information from external knowledge base. Second, we propose a novel Pairwise Popularity Graph Convolutional Network, named as PP-GCN, based on weighted meta-path instance similarity and textual semantic representation as inputs, to perform fine-grained social event categorization and learn the optimal weights of meta-paths in different tasks. Third, we propose a streaming social event detection and evolution discovery framework for HINs based on meta-path similarity search, historical information about meta-paths, and heterogeneous DBSCAN clustering method. Comprehensive experiments on real-world streaming social text data are conducted to compare various social event detection and evolution discovery algorithms. Experimental results demonstrate that our proposed framework outperforms other alternative social event detection and evolution discovery techniques.

Real-Time Multimedia Social Event Detection in Microblog

2018 ◽  
Vol 48 (11) ◽  
pp. 3218-3231 ◽  
Author(s):  
Sicheng Zhao ◽  
Yue Gao ◽  
Guiguang Ding ◽  
Tat-Seng Chua
Keyword(s):  
Real Time ◽  
Event Detection ◽  
Social Event ◽  
Social Event Detection

Semi-Supervised Multimodal Fusion Model for Social Event Detection on Web Image Collections

2015 ◽  
Vol 6 (4) ◽  
pp. 1-22 ◽  
Author(s):  
Zhenguo Yang ◽  
Qing Li ◽  
Zheng Lu ◽  
Yun Ma ◽  
Zhiguo Gong ◽  
...  
Keyword(s):  
Event Detection ◽  
Missing Values ◽  
Feature Fusion ◽  
Multimodal Fusion ◽  
Fusion Model ◽  
Social Event ◽  
Social Events ◽  
Clustering And Classification ◽  
Self Tuning ◽  
Social Event Detection

In this work, the authors aim to detect social events from Web images by devising a semi-supervised multimodal fusion model, denoted as SMF. With a multimodal feature fusion layer and a feature reinforcement layer, SMF learns feature histograms to represent the images, fusing multiple heterogeneous features seamlessly and efficiently. Particularly, a self-tuning approach is proposed to tune the parameters in the process of feature reinforcement automatically. Furthermore, to deal with missing values in raw features, prior knowledge is utilized to estimate the missing ones as a preprocessing step, and SMF will further extend an extra attribute to indicate if the values in the fused feature are missing. Based on the fused expression achieved by SMF, a series of algorithms are designed by adopting clustering and classification strategies separately. Extensive experiments conducted on the MediaEval social event detection challenge reveal that SMF-based approaches outperform the baselines.

scholarly journals Multityped Community Discovery in Time-Evolving Heterogeneous Information Networks Based on Tensor Decomposition

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Jibing Wu ◽  
Lianfei Yu ◽  
Qun Zhang ◽  
Peiteng Shi ◽  
Lihua Liu ◽  
...  
Keyword(s):  
Real World ◽  
Tensor Decomposition ◽  
Information Networks ◽  
Community Discovery ◽  
Star Network ◽  
Heterogeneous Information ◽  
Heterogeneous Information Networks ◽  
General Network ◽  
Real World Datasets ◽  
Discovery Method

The heterogeneous information networks are omnipresent in real-world applications, which consist of multiple types of objects with various rich semantic meaningful links among them. Community discovery is an effective method to extract the hidden structures in networks. Usually, heterogeneous information networks are time-evolving, whose objects and links are dynamic and varying gradually. In such time-evolving heterogeneous information networks, community discovery is a challenging topic and quite more difficult than that in traditional static homogeneous information networks. In contrast to communities in traditional approaches, which only contain one type of objects and links, communities in heterogeneous information networks contain multiple types of dynamic objects and links. Recently, some studies focus on dynamic heterogeneous information networks and achieve some satisfactory results. However, they assume that heterogeneous information networks usually follow some simple schemas, such as bityped network and star network schema. In this paper, we propose a multityped community discovery method for time-evolving heterogeneous information networks with general network schemas. A tensor decomposition framework, which integrates tensor CP factorization with a temporal evolution regularization term, is designed to model the multityped communities and address their evolution. Experimental results on both synthetic and real-world datasets demonstrate the efficiency of our framework.

Graph-Based Multimodal Clustering for Social Event Detection in Large Collections of Images

2014 ◽  
pp. 146-158 ◽  
Author(s):  
Georgios Petkos ◽  
Symeon Papadopoulos ◽  
Emmanouil Schinas ◽  
Yiannis Kompatsiaris
Keyword(s):  
Event Detection ◽  
Social Event ◽  
Social Event Detection

scholarly journals Sensing And Detecting Small-Scale Events Using Geosocial Media Data

2021 ◽  
Author(s):  
Shishuo Xu
Keyword(s):  
Social Media ◽  
Event Detection ◽  
Human Movement ◽  
Small Scale ◽  
Type I ◽  
Social Event ◽  
Ground Truth Data ◽  
Social Events ◽  
Social Media Platforms ◽  
Media Data

<div>Small-scale events involve interactive human movement in limited space and time. Social media platforms possibly generate large amount of geospatially-referenced information related to small-scale events. It benefits individuals, management departments, and urban systems if small-scale events can be timely detected from social media platforms, where measuring the abnormal patterns of human movement to discover events and analyzing associated texts to interpret the reasons behind abnormal movement are two keys. Through investigating how people move as different events occur and measuring the patterns on social media platforms, small-scale events can be generally classified into two types, namely type I events with abrupt patterns and type II events with random occurrence of key factors, where social events and traffic events are representative correspondingly.</div><div>Despite many studies have been conducted to detect social events and traffic events using geosocial media data, there still are some un-answered questions requiring further research. Most existing studies did not identify occurring events from a full coverage of spatial, temporal, and semantic perspectives. Studies concerning social event detection lack efficient semantic analysis summarizing event content to infer the reasons driving the abnormal movement. The typical classification-based method regarding traffic event detection lacks investigation on how the spatiotemporal distribution of traffic relevant posts associate with the occurring traffic events, and simply assigns the detected events with predefined categories, missing events that indicate traffic anomalies but go beyond the predetermined categories.<br></div><div>In this thesis, spatial-temporal-semantic approaches are proposed to measure spatiotemporal patterns of posts and users of social media platforms to capture abnormal human movement, and analyze the content of associated posts to mine the reasons driving the movement. A variety of techniques including machine learning, natural language processing, and spatiotemporal analysis are adopted to realize effective detection. Based on one-year Twitter data collected in Toronto, 2014 Toronto International Film Festival and traffic anomaly detection are selected as two case studies to evaluate the performance of proposed approaches. Through comparing with the ground truth data, the result reveals that more than 80% of the detected events do refer to real-world events, which illustrates the feasibility and efficiency of proposed approaches.<br></div><div><br></div><div>Keywords: Small-scale event, Event detection, Geosocial media data, Traffic event, Social event, Twitter, Spatiotemporal clustering<br></div>

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