A Survey on Event Tracking in Social Media Data Streams

Social networks are inevitable parts of our daily life,where an unprecedented amount of complex data corresponding to a diverse range of applications are generated.As such,it is imperative to conduct research on social events and patterns from the perspectives of conventional sociology to optimize services that originate from social networks.Event tracking in social networks finds various applications,such as network security and societal governance,which involves analyzing data generated by user groups on social networks in real time.Moreover,as deep learning techniques continue to advance and make important breakthroughs in various fields,researchers are using this technology to progressively optimize the effectiveness of Event Detection(ED)and tracking algorithms.In this regard,this paper presents an in-depth comprehensive review of the concept and methods involved in ED and tracking in social networks.We introduce mainstream event tracking methods,which involve three primary technical steps:ED,event propagation,and event evolution.Finally,we introduce benchmark datasets and evaluation metrics for ED and tracking,which allow comparative analysis on the performance of mainstream methods.Finally,we present a comprehensive analysis of the main research findings and existing limitations in this field,as well as future research prospects and challenges.

Event detection and evolution in multi-lingual social streams

Real-life events are emerging and evolving in social and news streams.Recent methods have succeeded in capturing designed features of monolingual events,but lack of interpretability and multi-lingual considerations.To this end,we propose a multi-lingual event mining model,namely MLEM,to automatically detect events and generate evolution graph in multilingual hybrid-length text streams including English,Chinese,French,German,Russian and Japanese.Specially,we merge the same entities and similar phrases and present multiple similarity measures by incremental word2vec model.We propose an 8-tuple to describe event for correlation analysis and evolution graph generation.We evaluate the MLEM model using a massive human-generated dataset containing real world events.Experimental results show that our new model MLEM outperforms the baseline method both in efficiency and effectiveness.

Narrative Graph:Telling Evolving Stories Based on Event-centric Temporal Knowledge Graph

As the main channel for people to obtain information and express their opinions,online media generate a huge amount of unstructured news documents every day and make it difficult for people to perceive major societal events and grasp the evolution of events.Previous studies on storyline generation are generally based on document clustering without considering event arguments and relations between events.Event-centric knowledge graph has been used to facilitate the construction of news documents to form structured event representation.Although some studies have attempted to construct timelines based on event-centric knowledge graphs,it is difficult for timelines to depict the complex structures of event evolution.In this paper,we try to represent news documents as an event-centric knowledge graph,and compress the whole knowledge graph into salient complex events in temporal order to generate storylines named narrative graph.We first collect news documents from news platforms,construct an event ontology,and build an event-centric knowledge graph with temporal relations.Graph neural network is used to detect events,while BERT fine-tuning is leveraged to identify temporal relations between events.Then,a novel generation framework of narrative graph with constraints of coherence and coverage is proposed.In addition,a case study is implemented to demonstrate how to utilize narrative graph to analyze real-world event.The experiment results show that our approach significantly outperforms the baseline approaches.

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.