COVID-19 information contact and participation analysis and dynamic prediction in the Chinese Sina-microblog

Yin, Fulian; Pang, Hongyu; Xia, Xinyu; Shao, Xueying; Wu, Jianhong

doi:10.1016/j.physa.2021.125788

Cited by 15 publications

(11 citation statements)

References 26 publications

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“…Our research is based on Sina-microblog, one of the most popular social media platforms in China [15]. The platform had over 523 million active users each month and 229 million active users each day in 2020.…”

Section: Methodsmentioning

confidence: 99%

Analysis of public information demand during the COVID-19 pandemic based on four-stage crisis model

Fang

Jia

et al. 2022

Front. Phys.

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At the end of 2019, the outbreak of the Corona Virus Disease 2019 (COVID-19) became a grave global public health emergency. At that time, there was a lack of information about this virus. Nowadays, social media has become the main source for the public to obtain information, especially during the COVID-19 pandemic. Therefore, in order to know about the public of information demand after the outbreak, the research collects the data of hot search on Sina-microblog from 1 January 2020 to 30 December 2020, and then conducts data mining by combining text processing with topic models. Then we show the topics mined in the knowledge map. The results show that with the outbreak of the COVID-19, people’s attention to the topics related to the epidemic reaches the maximum in a short time, and then decreases with fluctuation, but does not disappear immediately. Some topics fluctuate violently due to the emergence of special events. The results conformed to the four-stage crisis model in the emergency management. We analyze the role of social media in four stages for this. The findings of this study could help the government and emergency agencies to better understand the main aspects, which the public’s concern about COVID-19, and accelerate public opinion guidance and emotional reassurance.

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Section: Methodsmentioning

confidence: 99%

Analysis of public information demand during the COVID-19 pandemic based on four-stage crisis model

Fang

Jia

et al. 2022

Front. Phys.

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“…They proposed a model that can predict those who were interested in specific topics in Weibos. In another work [14], they investigated the behaviors of users within the context of Covid-19. In addition to this, they proposed SRFI model to predict the opinions of users about the pandemic through Chinese Sina blogs.…”

Section: Related Workmentioning

confidence: 99%

A Predictive Paradigm for Event Popularity in Event-Based Social Networks

Trinh

VuongThi

2022

IEEE Access

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Recently, event-based social networks (EBSNs) have been used as flexible online platforms that create online groups and make offline events for people. The success of popular offline events depends much on a participant number factor, which contributes to the growth of online groups and social networks. In this paper, we study a research problem of event popularity, where the popularity of an event is relevant to the number of participants of the event. In this work, we propose a predictive paradigm which consists of the procedure of generating features and training regression methods to estimate the popularity of events. We first crawled datasets and then generated features from the datasets. Finally, three famous regression methods, i.e., support vector machine, random forest, and decision tree, were used to predict the popularity of events. Extensive experiments were conducted on three city datasets with two different contexts of using these three datasets. In the city context, each city dataset was converted into a data table. Three regression methods used the data table to build predictive models and estimate the popularity of events. In the other context, each group in one city dataset was transformed into one group data table, and regression models were built on the group data table. Overall, the proposed paradigm with random forest is the best in terms of MAE and RMSE metrics. Moreover, this study has shown that for the city context, the event content is the best contributing factor that pushes people to engage in events. Furthermore, with the group context, the event time factor is very crucial to assist users in planning to join events.INDEX TERMS Social networks, EBSNs, Event popularity.

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“…In Yin's work [13], a modified epidemic model was proposed to predict the dynamics of topic reading, which represents one indicator of event popularity. In their further work [14], considering both public contact and participation on microblog, they proposed the Susceptible-Reading-Forwarding-Immune (SRFI) model to predict the overall microblog event trend in all stages. Meanwhile, Pan et al [15] developed a Stochastic Differential Equation (SDE) to describe the observed collective patterns of the online microblogging behavior and predict the Sina Weibo volume data.…”

Section: Related Workmentioning

confidence: 99%

Trend Prediction of Event Popularity from Microblogs

Zhao

2021

Future Internet

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Owing to rapid development of the Internet and the rise of the big data era, microblog has become the main means for people to spread and obtain information. If people can accurately predict the development trend of a microblog event, it will be of great significance for the government to carry out public relations activities on network event supervision and guide the development of microblog event reasonably for network crisis. This paper presents effective solutions to deal with trend prediction of microblog events’ popularity. Firstly, by selecting the influence factors and quantifying the weight of each factor with an information entropy algorithm, the microblog event popularity is modeled. Secondly, the singular spectrum analysis is carried out to decompose and reconstruct the time series of the popularity of microblog event. Then, the box chart method is used to divide the popularity of microblog event into various trend spaces. In addition, this paper exploits the Bi-LSTM model to deal with trend prediction with a sequence to label model. Finally, the comparative experimental analysis is carried out on two real data sets crawled from Sina Weibo platform. Compared to three comparative methods, the experimental results show that our proposal improves F1-score by up to 39%.

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COVID-19 information contact and participation analysis and dynamic prediction in the Chinese Sina-microblog

Cited by 15 publications

References 26 publications

Analysis of public information demand during the COVID-19 pandemic based on four-stage crisis model

Analysis of public information demand during the COVID-19 pandemic based on four-stage crisis model

A Predictive Paradigm for Event Popularity in Event-Based Social Networks

Trend Prediction of Event Popularity from Microblogs

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