The sustainable development of mobile government social media depends citizens’ continued use. Based on the Stimulus-Organism-Response framework and social response theory, the present study investigated the impacts of perceived similarity and...
Social media allows people to express and share a variety of experiences, opinions, beliefs, interpretations, or viewpoints on a single topic. Summarizing a collection of social media posts (microblogs) on one topic may be challenging and can result...
A microblog is a new type of social media for information publishing, acquiring, and spreading. Finding the significant topics of a microblog is necessary for popularity tracing and public opinion following. This paper puts forward a method to detect...
Typhoon is one of the most destructive natural disasters in the world. Real-time information on the process of typhoon events serves as important reference for disaster emergency. In the era of big data, microblog text has been gradual applied to the...
Microblog is an important platform for mining public opinion, and it is of great value to conduct emotional analysis of microblog texts during the current epidemic. Aiming at the problem that most current emotional classification methods cannot effec...
The sentiment analysis of microblog text has always been a challenging research field due to the limited and complex contextual information. However, most of the existing sentiment analysis methods for microblogs focus on classifying the polarity of...
Information from social media microblogging has been applied to management of emergency situations following disasters. In particular, such blogs contain much information about the public perception of disasters. However, the effective collection and...
Place descriptions record qualitative information related to places and their spatial relationships; thus, the geospatial semantics of a place can be extracted from place descriptions. In this study, geotagged microblog short texts recorded in 2017 f...
30 December 2018
Nowadays, many big cities are suffering from heavy air pollution and continuous haze weather. Compared with the threat on physical health, the influence of haze on people’s mental health is much less discussed in the current literature. Emotion...
Attracting citizens and facilitating their continued usage is critical for the sustainable development of mobile government microblog services. Drawing on the stimulus–organism–response (SOR) framework and the literature related to perceived value, t...
28 July 2021
Social media data are constantly updated, numerous, and characteristically prominent. To quickly extract the needed information from the data to address earthquake emergencies, a topic-words detection model of earthquake emergency microblog messages...
Sentiment analysis of online social media has attracted significant interest recently. Many studies have been performed, but most existing methods focus on either only textual content or only visual content. In this paper, we utilize deep learning mo...
11 September 2015
Background: This study aims to examine the characteristics of people who talk about suicide on Chinese microblogs (referred to as Weibo suicide communication (WSC)), and the psychological antecedents of such behaviors. Methods: An online survey was c...
To address the shortcomings of existing deep learning models and the characteristics of microblog speech, we propose the DCCMM model to improve the effectiveness of microblog sentiment analysis. The model employs WOBERT Plus and ALBERT to dynamically...
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 g...
Haze is a phenomenon caused by excessive PM2.5 (air-borne particulate matter having a diameter of fewer than 2.5 μm) and other pollutants and results from the interaction between specific climatic conditions and human activities. It significantly...
The identification and analysis of sentiment polarity in microblog data has drawn increased attention. Researchers and practitioners attempt to extract knowledge by evaluating public sentiment in response to global events. This study aimed to evaluat...
Microblogs are one of the major social networks in people’s daily life. The increasing amount of timely microblog data brings new opportunities for enterprises to predict short-term product sales based on microblogs because the daily microblogs...
This study analyzed 2421 microblogs posted between the year 2012 to March 2022 reflecting the microbloggers’ attitudes toward different cultures. Results indicated that (1) the number of microblog posts expressing the users’ intercultural...
During public policy information diffusion, policy interpretation on government microblogs and public attention interact, but there are certain differences. We construct a research framework for the heterogeneous diffusion of public policy informatio...
Recently, some appalling events have attracted wide attention, such as the RYB (Red, Yellow and Blue) child abuse incident, the killing of stewardesses by online car-hailing, and the swine fever epidemic. With the development of mobile Internet, Micr...
Market liquidity has an immediate impact on the execution of transactions in financial markets. Informed counterparty risk is often priced into market liquidity. This study investigates whether microblogging data, as a non-financial information tool,...
Adoption of social media systems (SMS), proprietary microblogging platforms in particular, for the purposes of information sharing has been increasingly on the rise among corporations. While Twitter is the preferred microblogging tool by the general...
The conventional machine learning-based method for the prediction of microblogs’ reposting number mainly focuses on the extraction and representation of static features of the source microblogs such as user attributes and content attributes, wi...
21 September 2020
Objective: Coronavirus disease 2019 (COVID-19) has caused substantial panic worldwide since its outbreak in December 2019. This study uses social networks to track the evolution of public emotion during COVID-19 in China and analyzes the root causes...
Micro-blogs, such as Twitter, have become important tools to share opinions and information among users. Messages concerning any topic are daily posted. A message posted by a given user reaches all the users that decided to follow her/him. Some users...
18 October 2022
Large-scale, widespread COVID-19 vaccination is the most effective means of cutting off the spread of the novel coronavirus and establishing an immune barrier. Due to the large population base in China, it has been a very difficult task to establish...
The use of Machine Learning (ML) and Sentiment Analysis (SA) on data from microblogging sites has become a popular method for stock market prediction. In this work, we developed a model for predicting stock movement utilizing SA on Twitter and StockT...
The lack of regulations and oversight on Online Social Networks (OSNs) has resulted in the rise of social spam, which is the dissemination of unsolicited and low-quality content that aims to deceive and manipulate users. Social spam can cause a range...
Sentiment analysis in Chinese microblogs is challenged by complex syntactic structures and fine-grained sentiment shifts. To address these challenges, a Contextually Enriched Graph Neural Network (CE-GNN) is proposed, integrating self-supervised lear...
The public’s acceptance level of recycled water use is a key factor that affects the popularization of this technology; therefore, it is critical to know the public’s attitude in order to make guiding policies effectively and scientifical...
Big data modelling using microblogs is applied to acquire nationwide representative panel data on subjective well-being. The analysis directly validates the influence of China’s Livelihood Index on subjective well-being. Using panel data on sub...
In this paper, based on Sina Weibo data, a natural language processing (NLP) analysis method was used to analyze the temporal and spatial sequence characteristics of people’s attention and the characteristics of text content with the help of mi...
In order to ensure the healthy development of social networks and the harmony and stability of the society, as well as to facilitate effective supervision by regulatory authorities, a classification method of social bots is proposed based on the iden...
Analysis of the spatiotemporal distribution of online public opinion topics can help understand the hotspots of public concern. The topic model is employed widely in public opinion topic clustering for social media data. In order to handle topic-clus...
The rapid development of technology transforms the way researchers conduct projects, communicate with others, and disseminate findings. In addition to traditional presentations of research results, this paper argues that building a digital artifact i...
The Shanghai epidemic (COVID-19) is another large-scale epidemic in China’s central cities in the post-epidemic era from the end of March 2022. It is of great significance to study the public’s attitude towards the Shanghai epidemic to su...
Urban green space has significant social, ecological, cultural and economic value. This study uses social media data to examine the spatiotemporal utilization of major parks in Shanghai and explore the determinants of their recreational attraction. M...
19 January 2023
User-generated contents (UGCs) on social media are a valuable source of emergency information (EI) that can facilitate emergency responses. However, the tremendous amount and heterogeneous quality of social media UGCs make it difficult to extract tru...
In the information age, leaked private information may cause significant physical and mental harm to the relevant parties, leading to a negative social impact. In order to effectively evaluate the impact of such information leakage in today’s s...
The effects of the COVID-19 pandemic are individualized, which means that our emotions and behaviors would experience changes of different degrees. These changes have led to subtle connections within the social media context. This study concentrates...
This paper describes a follow-up Web 2.0 approach to a technology enhanced master course for students of Graz University of Technology. The lecture “Social Aspects of Information Technology” has a long tradition for using new didactical scenarios as...
Integrated learning can be used to combine weak classifiers in order to improve the effect of emotional classification. Existing methods of emotional classification on micro-blogs seldom consider utilizing integrated learning. Personality can signifi...
Disasters do not follow a predictable timetable. Rapid situational awareness is essential for disaster management. People witnessing a disaster in the same area and beyond often use social media to report, inform, summarize, update, or warn each othe...
Analyzing people’s opinions, attitudes, sentiments, and emotions based on user-generated content (UGC) is feasible for identifying the psychological characteristics of social network users. However, most studies focus on identifying the sentiments ca...
In settings wherein discussion topics are not statically assigned, such as in microblogs, a need exists for identifying and separating topics of a given event. We approach the problem by using a novel type of similarity, calculated between the major...
Based on Web 2.0 technology, more and more people tend to express their attitude or opinions on the Internet. Radical ideas, rumors, terrorism, or violent contents are also propagated on the Internet, causing several incidents of social panic every y...
This study develops a contingent mediation model to investigate whether user perception enhances customer stickiness through emotional connection and further assess such mediating effect varies with different adaptivity. A moderated mediation approac...
Green spaces have a positive impact on the mood of urban residents. However, previous studies have focused primarily on parks or residential areas, neglecting the influence of green spaces in different socioeconomic locations on public sentiment. Thi...
Natural disasters cause significant casualties and losses in urban areas every year. Further, the frequency and intensity of natural disasters have increased significantly over the past couple of decades in the context of global climate change. Under...
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