Social media event prediction using DNN with feedback mechanism

Online social networks (OSNs) are a rich source of information, and the data (including user-generated content) can be mined to facilitate real-world event prediction. However, the dynamic nature of OSNs and the fast-pace nature of social events or hot topics compound the challenge of event prediction. This is a key limitation in many existing approaches. For example, our evaluations of six baseline approaches (i.e., logistic regression latent Dirichlet allocation (LDA)-based logistic regression (LR), multi-task learning (MTL), long short-term memory (LSTM) and convolutional neural networks, and transformer-based model) on three datasets collected as part of this research (two from Twitter and one from a news collection site1), reveal that the accuracy of these approaches is between 50% and 60%, and they are not capable of utilizing new events in event predictions. Hence, in this article, we develop a novel DNN-based framework (hereafter referred to as event prediction with feedback mechanism— EPFM. Specifically, EPFM makes use of a feedback mechanism based on emerging events detection to improve the performance of event prediction. The feedback mechanism ensembles three outlier detection processes and returns a list of new events. Some of the events will then be chosen by analysts to feed into the fine-tuning process to update the predictive model. To evaluate EPFM, we conduct a series of experiments on the same three datasets, whose findings show that EPFM achieves 80% accuracy in event detection and outperforms the six baseline approaches.We also validate EPFM’s capability of detecting new events by empirically analyzing the feedback mechanism under different thresholds

Real-Time Detection of COVID-19 Events From Twitter: A Spatial-Temporally Bursty-Aware Method

In the last two years, the outbreak of COVID-19 has significantly affected human life, society, and the economy worldwide. To prevent people from contracting COVID-19 and mitigate its spread, it is crucial to timely distribute complete, accurate, and up-to-date information about the pandemic to the public. In this article, we propose a spatial-temporally bursty-aware method called STBA for real-time detection of COVID-19 events from Twitter. STBA has three consecutive stages. In the first stage, STBA identifies a set of keywords that represent COVID-19 events according to the spatiotemporally bursty characteristics of words using Ripley's K function. STBA will also filter out tweets that do not contain the keywords to reduce the interference of noise tweets on event detection. In the second stage, STBA uses online density-based spatial clustering of applications with noise clustering to aggregate tweets that describe the same event as much as possible, which provides more information for event identification. In the third stage, STBA further utilizes the temporal bursty characteristic of event location information in the clusters to identify real-world COVID-19 events. Each stage of STBA can be regarded as a noise filter. It gradually filters out COVID-19-related events from noisy tweet streams. To evaluate the performance of STBA, we collected over 116 million Twitter posts from 36 consecutive days (from March 22, 2020 to April 26, 2020) and labeled 501 real events in this dataset. We compared STBA with three state-of-the-art methods, EvenTweet, event detection via microblog cliques (EDMC), and GeoBurst+ in the evaluation. The experimental results suggest that STBA outperforms GeoBurst+ by 13.8%, 12.7%, and 13.3% in terms of precision, recall, and F₁ score. STBA achieved even more improvements compared with EvenTweet and EDMC