MVGAN: Multi-View Graph Attention Network for Social Event Detection

Social networks are critical sources for event detection thanks to the characteristics of publicity and dissemination. Unfortunately, the randomness and semantic sparsity of the social network text bring significant challenges to the event detection task. In addition to text, time is another vital element in reflecting events since events are often followed for a while. Therefore, in this article, we propose a novel method named Multi-View Graph Attention Network (MVGAN) for event detection in social networks. It enriches event semantics through both neighbor aggregation and multi-view fusion in a heterogeneous social event graph. Specifically, we first construct a heterogeneous graph by adding the hashtag to associate the isolated short texts and describe events comprehensively. Then, we learn view-specific representations of events through graph convolutional networks from the perspectives of text semantics and time distribution, respectively. Finally, we design a hashtag-based multi-view graph attention mechanism to capture the intrinsic interaction across different views and integrate the feature representations to discover events. Extensive experiments on public benchmark datasets demonstrate that MVGAN performs favorably against many state-of-the-art social network event detection algorithms. It also proves that more meaningful signals can contribute to improving the event detection effect in social networks, such as published time and hashtags.

Streaming Social Event Detection and Evolution Discovery in Heterogeneous Information Networks

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.

Robust Image Hashing with Low-Rank Representation and Ring Partition

Image hashing has attracted much attention of the community of multimedia security in the past years. It has been successfully used in social event detection, image authentication, copy detection, image quality assessment, and so on. This paper presents a novel image hashing with low-rank representation (LRR) and ring partition. The proposed hashing finds the saliency map by the spectral residual model and exploits it to construct the visual representation of the preprocessed image. Next, the proposed hashing calculates the low-rank recovery of the visual representation by LRR and extracts the rotation-invariant hash from the low-rank recovery by ring partition. Hash similarity is finally determined by L2 norm. Extensive experiments are done to validate effectiveness of the proposed hashing. The results demonstrate that the proposed hashing can reach a good balance between robustness and discrimination and is superior to some state-of-the-art hashing algorithms in terms of the area under the receiver operating characteristic curve.

Intelligent Transportation Cyber-Physical System: On Crowd through Event Detection for Public Urban Transportation

Intelligent Transportation System (ITS) has become the popular area of research since the last decade. Knowing the crowd density in every region of the city is of high importance for an ITS in delivering adequate transport facility to the public. Further, the occurrence of social events draw public crowd occasionally, in any specific region of the city. Thus, the combination of both the identification of crowd density and the social event detection lays a path to an interesting research in ITS. Having said, this paper proposes a methodology for the effective design of ITS with a primary focus on crowd sensing. The paper also presents a taxonomy of methods used to gather crowd density information through various sources. Furthermore, the research works that focused on event detection and crowd analysis are studied. Finally, the open challenges are identified and outlined which are promising research directions for ITS.

A Distance-Dependent Chinese Restaurant Process Based Method for Event Detection on Social Media

In this paper, we propose a method for event detection on social media, which aims at clustering media items into groups of events based on their textural information as well as available metadata. Our approach is based on distance-dependent Chinese Restaurant Process (ddCRP), a clustering approach resembling Dirichlet process algorithm. Furthermore, we scrutinize the effectiveness of a series of pre-processing steps in improving the detection performance. We experimentally evaluated our method using the Social Event Detection (SED) dataset of MediaEval 2013 benchmarking workshop, which pertains to the discovery of social events and their grouping in event-specific clusters. The obtained results indicate that the proposed method attains very good performance rates compared to existing approaches.