Showing papers on "Microblogging published in 2022"
TL;DR: Wang et al. as mentioned in this paper proposed a multi-stage risk grading model of Internet public opinion for public health emergencies, which continuously pays attention to the risk level of internet public opinion based on the time scale of regular or major information updates.
Abstract: In the period of Corona Virus Disease 2019 (COVID-19), millions of people participate in the discussion of COVID-19 on the Internet, which can easily trigger public opinion and threaten social stability. This paper creatively proposes a multi-stage risk grading model of Internet public opinion for public health emergencies. On the basis of general public opinion risk grading analysis, the model continuously pays attention to the risk level of Internet public opinion based on the time scale of regular or major information updates. This model combines Analytic Hierarchy Process Sort II (AHPSort II) and Swing Weighting (SW) methods and proposes a new Multi-Criteria Decision Making (MCDM) method - AHPSort II-SW. Intuitionistic fuzzy number and linguistic fuzzy number are introduced into the model to evaluate the criteria that cannot be quantified. The multi-stage model is tested using more than 2,000 textual data about COVID-19 collected from Microblog, a leading social media platform in China. Seven public opinion risk assessments were conducted from January 23 to April 8, 2020. The empirical results show that in the early COVID-19 outbreak, the risk of public opinion is more serious on macroscopic view. In details, the risk of public opinion decreases slowly with time, but the emergence of important events may still increase the risk of public opinion. The analysis results are in line with the actual situation and verify the effectiveness of the method. Comparative analysis indicates the improved method is proved to be superior and effective, sensitivity analysis confirms its stability. Finally, management suggestions was provided, this study contributes to the literature on public opinion risk assessment and provides implications for practice.
20 citations
TL;DR: Wang et al. as mentioned in this paper proposed a multi-stage risk grading model of Internet public opinion for public health emergencies, which continuously pays attention to the risk level of internet public opinion based on the time scale of regular or major information updates.
Abstract: In the period of Corona Virus Disease 2019 (COVID-19), millions of people participate in the discussion of COVID-19 on the Internet, which can easily trigger public opinion and threaten social stability. This paper creatively proposes a multi-stage risk grading model of Internet public opinion for public health emergencies. On the basis of general public opinion risk grading analysis, the model continuously pays attention to the risk level of Internet public opinion based on the time scale of regular or major information updates. This model combines Analytic Hierarchy Process Sort II (AHPSort II) and Swing Weighting (SW) methods and proposes a new Multi-Criteria Decision Making (MCDM) method - AHPSort II-SW. Intuitionistic fuzzy number and linguistic fuzzy number are introduced into the model to evaluate the criteria that cannot be quantified. The multi-stage model is tested using more than 2,000 textual data about COVID-19 collected from Microblog, a leading social media platform in China. Seven public opinion risk assessments were conducted from January 23 to April 8, 2020. The empirical results show that in the early COVID-19 outbreak, the risk of public opinion is more serious on macroscopic view. In details, the risk of public opinion decreases slowly with time, but the emergence of important events may still increase the risk of public opinion. The analysis results are in line with the actual situation and verify the effectiveness of the method. Comparative analysis indicates the improved method is proved to be superior and effective, sensitivity analysis confirms its stability. Finally, management suggestions was provided, this study contributes to the literature on public opinion risk assessment and provides implications for practice.
20 citations
TL;DR: A review study of research papers on social media and information sharing published between 2009 and 2020 was conducted as discussed by the authors , which identified the following characteristics: (1) the primary articles are based on information sharing through social media; (2) the main authors deal with information sharing in social media, (3) the newest topics related to information sharing are demographic characteristics, cognition, sentiment, healthcare, products and services recommendations, tourist recommendations, and COVID-19.
Abstract: Abstract Social media has evolved at a rapid pace, influencing every aspect of the global community. The purpose of this paper is to use bibliometric analysis tools to evaluate valid works on the relationship between social media and information sharing. A review study of research papers on social media and information sharing published between 2009 and 2020 was conducted. The bibliometric analysis identified the following characteristics: (1) the primary articles are based on information sharing through social media; (2) the main authors deal with information sharing in social media; (3) the newestrend topics related to information sharing in social media are demographic characteristics, cognition, sentiment, healthcare, products and services recommendations, tourist recommendations, and COVID-19. The results show significant impact rates in studies of information sharing in social media. Overall, this study serves as a basis for new scientific questions that will contribute to the further development of this research field.
17 citations
TL;DR: In this article , the impact of news on consumer sentiment toward a company in the presence of pre-news sentiment was studied and the type of company (either B2C or B2B) matter.
Abstract: Companies deal with good and bad publicity daily. We study the impact of news on consumer sentiment toward a company in the presence of pre-news sentiment. We use Sina Weibo’s (the Chinese version of Twitter) microblogging data and the full list of news items published in Sina Finance between 2013 and 2014 to measure sentiment. In our study, we address the following research questions: Does negative news have a greater impact than positive news on consumer sentiment change? Does news affect sentiment change to a greater degree when pre-news sentiment matches the news valence? Does the type of company (either B2C or B2B) matter? Our empirical findings show that consumers overreact to negative news and negative pre-news sentiment intensifies such overreaction, leading to negative herding. Further, negative pre-news sentiment is even more damaging for B2B companies than for B2C companies.
15 citations
TL;DR: A novel fine-grained multimodal fusion network (FMFN) to fully fuse textual features and visual features for fake news detection is proposed and scaled dot-product attention is utilized to fuse word embeddings of words in the text and multiple feature vectors representing different features of the image.
Abstract: As one of the most popular social media platforms, microblogs are ideal places for news propagation. In microblogs, tweets with both text and images are more likely to attract attention than text-only tweets. This advantage is exploited by fake news producers to publish fake news, which has a devasting impact on individuals and society. Thus, multimodal fake news detection has attracted the attention of many researchers. For news with text and image, multimodal fake news detection utilizes both text and image information to determine the authenticity of news. Most of the existing methods for multimodal fake news detection obtain a joint representation by simply concatenating a vector representation of the text and a visual representation of the image, which ignores the dependencies between them. Although there are a small number of approaches that use the attention mechanism to fuse them, they are not fine-grained enough in feature fusion. The reason is that, for a given image, there are multiple visual features and certain correlations between these features. They do not use multiple feature vectors representing different visual features to fuse with textual features, and ignore the correlations, resulting in inadequate fusion of textual features and visual features. In this paper, we propose a novel fine-grained multimodal fusion network (FMFN) to fully fuse textual features and visual features for fake news detection. Scaled dot-product attention is utilized to fuse word embeddings of words in the text and multiple feature vectors representing different features of the image, which not only considers the correlations between different visual features but also better captures the dependencies between textual features and visual features. We conduct extensive experiments on a public Weibo dataset. Our approach achieves competitive results compared with other methods for fusing visual representation and text representation, which demonstrates that the joint representation learned by the FMFN (which fuses multiple visual features and multiple textual features) is better than the joint representation obtained by fusing a visual representation and a text representation in determining fake news.
14 citations
TL;DR: Considering the important role of opinion leaders in microblogs and users' interest in microblog information, a SIR model of public opinion propagation is constructed based on the novel coronavirus pneumonia model and micro-blog's public health emergencies information as mentioned in this paper .
Abstract: With the advent of the era of “we media,” many people's opinions have become easily accessible. Public health emergencies have always been an important aspect of public opinion exchange and emotional communication. In view of this sudden group panic, public opinion cannot be effectively monitored, controlled or guided. This makes it easy to amplify the beliefs and irrationality of social emotions, that threaten social security and stability. Considering the important role of opinion leaders in micro-blogs and users’ interest in micro-blog information, a SIR model of public opinion propagation is constructed based on the novel coronavirus pneumonia model and micro-blog's public health emergencies information. The parameters of the model are calculated by combining the actual crawl data from the novel coronavirus pneumonia epidemic period, and the trends in the evolution of public opinion are simulated by MATLAB. The simulation results are consistent with the actual development of public opinion dissemination, which shows the effectiveness of the model. These research findings can help the government understand the principles that guide the propagation of public opinion and advise an appropriate time to control and correctly guide public opinion.
13 citations
TL;DR: In this paper , a sentiment-involved topic-based latent variables search methodology was proposed to incorporate the public discourse in the design of forecasting models particularly targeted for the steep-hill region of an ongoing wave.
12 citations
TL;DR: An empirical study based on the data of microblog contents regarding COVID-19 public opinion in the Sina Weibo platform from January to March 2020 is conducted and shows that when positive emotion is higher than 0.8, the spread of negative public opinion can be blocked.
Abstract: Both the suddenness and seriousness of COVID-19 have caused a variety of public opinions on social media, which becomes the focus of social attention. This paper aims to analyze the strategies regarding the prevention and guidance of public opinion spread under COVID-19 in social networks from the perspective of the emotional characteristics of user texts. Firstly, a model is established to mine text-based emotional tendency based on the Susceptible-Infectious-Susceptible (SIS) model. In addition, a mathematical and simulation analysis of the model is presented. Finally, an empirical study based on the data of microblog contents regarding COVID-19 public opinion in the Sina Weibo platform from January to March 2020 is conducted to analyze the factors that boost and hinder COVID-19 public opinion. The results show that when positive emotion is higher than 0.8, the spread of negative public opinion can be blocked. When the negative emotion and neutral emotion are both below 0.2, the spread of COVID-19 public opinion would be weakened. To accurately guide public opinion on COVID-19, the government authorities should establish a public opinion risk evaluation and an early warning mechanism. Platforms should strengthen public opinion supervision and users should improve their media literacy. The media organizations should insist on positive reporting, improve social cohesion, and guide the trend of public opinion.
12 citations
25 Apr 2022
TL;DR: An interpretable classification-summarization framework that first classifies tweets into different disaster-related categories and then summarizes those tweets, which achieves 5-25%) improvement in terms of ROUGE-1 F-score over most state-of-the-art approaches.
Abstract: Microblogging platforms like Twitter have been heavily leveraged to report and exchange information about natural disasters. The real-time data on these sites is highly helpful in gaining situational awareness and planning aid efforts. However, disaster-related messages are immersed in a high volume of irrelevant information. The situational data of disaster events also vary greatly in terms of information types ranging from general situational awareness (caution, infrastructure damage, casualties) to individual needs or not related to the crisis. It thus requires efficient methods to handle data overload and prioritize various types of information. This paper proposes an interpretable classification-summarization framework that first classifies tweets into different disaster-related categories and then summarizes those tweets. Unlike existing work, our classification model can provide explanations or rationales for its decisions. In the summarization phase, we employ an Integer Linear Programming (ILP) based optimization technique along with the help of rationales to generate summaries of event categories. Extensive evaluation on large-scale disaster events shows (a). our model can classify tweets into disaster-related categories with an 85% Macro F1 score and high interpretability (b). the summarizer achieves (5-25%) improvement in terms of ROUGE-1 F-score over most state-of-the-art approaches.
12 citations
TL;DR: In this article , an emoji-text integrated bidirectional LSTM (ET-BiLSTM) model was proposed for sentiment analysis of microblog reviews with emojis.
Abstract: ABSTRACT To exactly classify sentiments of microblog reviews with emojis in microblog social networks, this paper first proposes an emoji vectorisation method to achieve emoji vectors. Then, an emoji-text integrated bidirectional LSTM (ET-BiLSTM) model for sentiment analysis is proposed. In this model, review text-based sentence representations are extracted by a bidirectional LSTM network. Emoji-based auxiliary representations are obtained by a new attention mechanism. The two representations are further integrated into final review representation vectors. Finally, experimental results indicate that the proposed ET-BiLSTM model improves the performance of sentiment classification evaluated by macro-P, macro-R and macro-F1 scores in microblog social networks.
11 citations
TL;DR: Wang et al. as mentioned in this paper collected data about plant biodiversity assessments from studies on urban forests in the literature of China's national knowledge infrastructure. And they extracted records of the species amount, Shannon index, and Simpson index from 49 urban forest parks in 13 cities across mainland China from 2018 to 2021.
TL;DR: Wang et al. as mentioned in this paper combined a CNN and an attention mechanism to extract context information of words and form the basis of the newly designed sentiment classification framework for Chinese microblogs.
TL;DR: A model for predicting stock movement utilizing SA on Twitter and StockTwits data that integrates multiple SA and ML methods, emphasizing the retrieval of extra features from social media for improving stock prediction accuracy is developed.
Abstract: 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 StockTwits data. Stock movement and sentiment data were used to evaluate this approach and validate it on Microsoft stock. We gathered tweets from Twitter and StockTwits, as well as financial data from Finance Yahoo. SA was applied to tweets, and seven ML classification models were implemented: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), Decision Tree (DT), Random Forest (RF) and Multilayer Perceptron (MLP). The main novelty of this work is that it integrates multiple SA and ML methods, emphasizing the retrieval of extra features from social media (i.e., public sentiment), for improving stock prediction accuracy. The best results were obtained when tweets were analyzed using Valence Aware Dictionary and sEntiment Reasoner (VADER) and SVM. The top F-score was 76.3%, while the top Area Under Curve (AUC) value was 67%.
TL;DR: Wang et al. as discussed by the authors proposed a multichannel network with a Long Short-Term Memory layer (LSTM-layer) and a Convolution layer (Conv-layer), which can extract abstract features from input text.
Abstract: Social media (e.g., Sina Weibo) have the advantage of reflecting traffic information, including the reasons for jams, illegal behaviors, and emergency recourses on roads. However, there remains a challenging issue regarding how to sufficiently mine traffic information. In this paper, we propose a deep learning-based method that uses social media data for traffic jam management. The core ideas of the proposed method are twofold. First, a multichannel network with a Long Short-Term Memory layer (LSTM-layer) and a Convolution layer (Conv-layer) (termed as MC-LSTM-Conv) is proposed. This model consists of two information channels for extracting abstract features from input text. Each channel includes two Conv-layers, and an LSTM-layer is added to one of the four Conv-layers. The MC-LSTM-Conv model is used to extract check-in microblogs reflecting traffic jams from mass Sina Weibo data. Second, a series of matching rules are constructed based on the keywords that are related to traffic-jam scenes. These rules further classify the microblogs extracted by the first step into four classes, and each of the classes reflects a specific road condition (i.e., traffic accidents or large-scale activities, road construction, traffic lights, and the low efficiency of government agencies). Experiments on Sina Weibo data demonstrate that the proposed multichannel network has superior performance in extracting microblogs about traffic jams. The keyword fuzzy matching method can fetch detailed information about traffic jams efficiently.
TL;DR: The authors assessed the business-centered and CSR-centered diversity communication of five American corporations along with the ensuing responses on the micro blogging platform Twitter and found that companies that had longterm consistency in approaching diversity as CSR received more agreeable responses than those that made swift changes in the aftermath of increased pressure from activist groups.
TL;DR: In this article , a dynamic multiple negative emotional susceptible-forwarding-immune (MNE-SFI) model was proposed to examine how negative emotion spreads on social media and how sentiment mutation impacts by fitting the model to real multiple temporal information in messages with sentiments obtained from the Chinese Sina microblog.
TL;DR: Wang et al. as mentioned in this paper investigated the effect of government microblogs on tourism market value by affecting consumers' emotion, and found that the correlation between government microblog emotion, comment emotion and market value was confirmed.
Abstract: The emotion of social media users has been found to impact market value, but the mechanism of emotion transmission needs to be further explored. Based on the signaling theory, this study formulates the hypothesis that government microblog affects tourism market value by affecting consumers' emotion. A total of 10020 pieces of observation data on four famous Chinese scenic spots are analyzed with text analysis technique, and the correlations between government microblog emotion, comment emotion, and market value are confirmed. Besides, the moderating roles played by the COVID-19 epidemic and tourism attention are verified, and the crowding-out effect of tourism attention on comment emotion is validated. This study provides new understandings of signal transmission as well as practical suggestions for promoting the development of tourism through government social media in the context of COVID-19.
TL;DR: This paper analyzes the public attitude and sentiment towards the emergence of the SARS-CoV-2 Omicron variant on Twitter and estimates the strength of positive and negative sentiment with an average of 95% confidence intervals based upon emotion strength scales of 1-5.
Abstract: While different variants of COVID-19 dramatically affected the lives of millions of people across the globe, a new version of COVID-19, "SARS-CoV-2 Omicron," emerged. This paper analyzes the public attitude and sentiment towards the emergence of the SARS-CoV-2 Omicron variant on Twitter. The proposed approach relies on the text analytics of Twitter data considering tweets, retweets, and hashtags' main themes, the pandemic restriction, the efficacy of covid-19 vaccines, transmissible variants, and the surge of infection. A total of 18,737 tweets were pulled via Twitter Application Programming Interface (API) from December 3, 2021, to December 26, 2021, using the SentiStrength software that employs a lexicon of sentiment terms and a set of linguistic rules. The analysis was conducted to distinguish and codify subjective content and estimate the strength of positive and negative sentiment with an average of 95% confidence intervals based upon emotion strength scales of 1-5. It is found that negativity was dominated after the outbreak of Omicron and scored 31.01% for weak, 16.32% for moderate, 5.36% for strong, and 0.35% for very strong sentiment strength. In contrast, positivity decreased gradually and scored 16.48% for weak, 11.19% for moderate, 0.80% for strong, and 0.04% for very strong sentiment strength. Identifying the public emotional status would help the concerned authorities to provide appropriate strategies and communications to relieve public worries about pandemics.
TL;DR: In this article , a two-step sentiment analysis was performed using the VADER model and the syuzhet package to understand the overall sentiments and emotions of the users of Twitter.
Abstract: The outbreak of COVID-19 forced a dramatic shift in education, from in-person learning to an increased use of distance learning over the past 2 years. Opinions and sentiments regarding this switch from traditional to remote classes can be tracked in real time in microblog messages promptly shared by Twitter users, who constitute a large and ever-increasing number of individuals today. Given this framework, the present study aims to investigate sentiments and topics related to distance learning in Italy from March 2020 to November 2021. A two-step sentiment analysis was performed using the VADER model and the syuzhet package to understand the overall sentiments and emotions. A dynamic latent Dirichlet allocation model (DLDA) was built to identify commonly discussed topics in tweets and their evolution over time. The results show a modest majority of negative opinions, which shifted over time until the trend reversed. Among the eight emotions of the syuzhet package, 'trust' was the most positive emotion observed in the tweets, while 'fear' and 'sadness' were the top negative emotions. Our analysis also identified three topics: (1) requests for support measures for distance learning, (2) concerns about distance learning and its application, and (3) anxiety about the government decrees introducing the red zones and the corresponding restrictions. People's attitudes changed over time. The concerns about distance learning and its future applications (topic 2) gained importance in the latter stages of 2021, while the first and third topics, which were ranked highly at first, started a steep descent in the last part of the period. The results indicate that even if current distance learning ends, the Italian people are concerned that any new emergency will bring distance learning back into use again.
TL;DR: In this article , an outlier knowledge management framework based on complex adaptive system theory and information theory for investigating heterogeneous situations was developed for dealing with extreme public health events, where the authors applied advanced natural language processing (NLP) technology to conduct data mining and feature extraction on the microblog data from the Wuhan area and the imported case province (Henan Province) during the high and median operating periods of the epidemic.
Abstract: Based on complex adaptive system theory and information theory for investigating heterogeneous situations, this paper develops an outlier knowledge management framework based on three aspects-dimension, object, and situation-for dealing with extreme public health events. In the context of the COVID-19 pandemic, we apply advanced natural language processing (NLP) technology to conduct data mining and feature extraction on the microblog data from the Wuhan area and the imported case province (Henan Province) during the high and median operating periods of the epidemic. Our experiment indicates that the semantic and sentiment vocabulary of words, the sentiment curve, and the portrait of patients seeking help were all heterogeneous in the context of COVID-19. We extract and acquire the outlier knowledge of COVID-19 and incorporate it into the outlier knowledge base of extreme public health events for knowledge sharing and transformation. The knowledge base serves as a think tank for public opinion guidance and platform suggestions for dealing with extreme public health events. This paper provides novel ideas and methods for outlier knowledge management in healthcare contexts.
TL;DR: In this paper , the authors performed volume analysis and sentiment analysis using LDA (Latent Dirichlet Allocation) and text mining over two datasets collected for different periods, and proposed a hybrid feature engineering approach that elevates the performance of machine learning models.
Abstract: Microblogging websites and social media platforms serve as a potential source for mining public opinions and sentiments on a variety of subjects including the prevailing situations in war-afflicted countries. In particular, Twitter has a large number of geotagged tweets that make the analysis of sentiments across time and space possible. This study performs volume analysis and sentiment analysis using LDA (Latent Dirichlet Allocation) and text mining over two datasets collected for different periods. To increase the adequacy and efficacy of the sentiment analysis, a hybrid feature engineering approach is proposed that elevates the performance of machine learning models. Geotagged tweets are used for volume analysis indicating that the highest number of tweets is originated from India, the US, the UK, Pakistan, and Afghanistan. Analysis of positive and negative tweets reveals that negative tweets are mostly originated from India and the US. On the contrary, positive tweets belong to Pakistan and Afghanistan. LDA is used for topic modeling on two datasets containing tweets about the current situation after the Taliban take control of Afghanistan. Topics extracted through LDA suggest that majority of the Afghanistan people seem satisfied with the Taliban’s takeover while the topics from negative tweets reveal that issues discussed in negative tweets are related to the US concerns in Afghanistan. Sentiment analysis over two different datasets indicates that the trend of the sentiments has been shifted positively over three weeks.
TL;DR: A deep learning-based method to assess the flood severity by using text and image data extracted from the social media posts, which results in reduced usage of valuable computational resources as classification of multimedia data is expensive compared to the classification of microblog text data.
TL;DR: An extensive survey of Artificial Intelligence research, focusing on microblog social media data with applications to Situation Awareness, that gives the seminal work and state-of-the-art approaches across six thematic areas: Crime, Disasters, Finance, Physical Environment, Politics, and Health and Population.
Abstract: The rise of social media platforms provides an unbounded, infinitely rich source of aggregate knowledge of the world around us, both historic and real-time, from a human perspective. The greatest challenge we face is how to process and understand this raw and unstructured data, go beyond individual observations and see the “big picture”—the domain of Situation Awareness. We provide an extensive survey of Artificial Intelligence research, focusing on microblog social media data with applications to Situation Awareness, that gives the seminal work and state-of-the-art approaches across six thematic areas: Crime, Disasters, Finance, Physical Environment, Politics, and Health and Population. We provide a novel, unified methodological perspective, identify key results and challenges, and present ongoing research directions.
TL;DR: A multiclassification method of microblog negative sentiment based on MAML (model-agnostic metalearning) and BiLSTM (bidirectional extended short-term memory network) is proposed to represent the microblog text word vectorization and the combination of MAMl and Bi lSTM is constructed.
Abstract: With the continuous development of social networks, Weibo has become an essential platform for people to share their opinions and feelings in daily life. Analysis of users' emotional tendencies can be effectively applied to public opinion control, public opinion surveys, and product recommendations. However, the traditional deep learning algorithm often needs a large amount of data to be retained to obtain a better accuracy when faced with new work tasks. Given this situation, a multiclassification method of microblog negative sentiment based on MAML (model-agnostic metalearning) and BiLSTM (bidirectional extended short-term memory network) is proposed to represent the microblog text word vectorization and the combination of MAML and BiLSTM is constructed. The model of BiLSTM realizes the classification of negative emotions on Weibo and updates the parameters through machine gradient descent; the metalearner in MAML calculates the sum of the losses of multiple pieces of training, performs a second gradient descent, and updates the metalearner parameters. The updated metalearner can quickly iterate when faced with a new Weibo negative sentiment classification task. The experimental results show that compared with the prepopular model, on the Weibo negative sentiment dataset, the precision rate, recall rate, and F1 value are increased by 1.68 percentage points, 2.86 percentage points, and 2.27 percentage points, respectively.
TL;DR: A public opinion analysis framework based on social media data from the two years following the outbreak of COVID-19, coupled with state-of-the-art natural language processing (NLP) techniques, is developed that can aid governments in developing effective interventions and education campaigns to boost vaccination rates.
Abstract: The COVID-19 pandemic has created unprecedented burdens on people’s health and subjective well-being. While countries around the world have established models to track and predict the affective states of COVID-19, identifying the topics of public discussion and sentiment evolution of the vaccine, particularly the differences in topics of concern between vaccine-support and vaccine-hesitant groups, remains scarce. Using social media data from the two years following the outbreak of COVID-19 (23 January 2020 to 23 January 2022), coupled with state-of-the-art natural language processing (NLP) techniques, we developed a public opinion analysis framework (BertFDA). First, using dynamic topic clustering on Weibo through the latent Dirichlet allocation (LDA) model, a total of 118 topics were generated in 24 months using 2,211,806 microblog posts. Second, by building an improved Bert pre-training model for sentiment classification, we provide evidence that public negative sentiment continued to decline in the early stages of COVID-19 vaccination. Third, by modeling and analyzing the microblog posts from the vaccine-support group and the vaccine-hesitant group, we discover that the vaccine-support group was more concerned about vaccine effectiveness and the reporting of news, reflecting greater group cohesion, whereas the vaccine-hesitant group was particularly concerned about the spread of coronavirus variants and vaccine side effects. Finally, we deployed different machine learning models to predict public opinion. Moreover, functional data analysis (FDA) is developed to build the functional sentiment curve, which can effectively capture the dynamic changes with the explicit function. This study can aid governments in developing effective interventions and education campaigns to boost vaccination rates.
TL;DR: In this article , a hybrid recommendation through community detection (HRTCD) approach is proposed for friend prediction with linear runtime complexity that makes full use of the characteristics of social media based on hybrid information fusion, extracting the content topics of microblog for each participant along with the appraisal of domain-dependent user impact, builds a small-size heterogeneous network for each target user by fusing the interest similarity and social interaction between individuals, discovers all of the implicit clusters of target user via a community detection algorithm, and establishes the recommendation set consisting of a fixed number of potential friends.
TL;DR: The relevance and use of social networks (SN) in people's daily lives make them outstanding elements as valid and supportive online resources for education and training processes as mentioned in this paper , and within these SN, the case of micro blogging (specifically Twitter) is a potential ally to promote communication, debate, and access to information and documentation for both students and teachers.
Abstract: The relevance and use of Social Networks (SN) in people's daily lives make them outstanding elements as valid and supportive online resources for education and training processes. Within these SN, the case of microblogging (specifically Twitter) is a potential ally to promote communication, debate, and access to information and documentation for both students and teachers. In this paper, we conduct an in-depth review of the related literature, where we analyze the scientific articles that have addressed the use of microblogging in connection with educational and training activities. The results obtained show that together with a series of benefits of the integrated use of this resource in the educational field, other possible benefits in the field of the development of meta-skills, the improvement of the motivation of the participants together with greater participation and a reflective and critical spirit are also evident.
TL;DR: Wang et al. as mentioned in this paper proposed a hybrid information fusion approach for friend prediction based on the characteristics of social media, which extracts the content topics of microblog for each participant along with the appraisal of domain-dependent user impact, builds a small-size heterogeneous network for each target user by fusing the interest similarity and social interaction between individuals, discovers all of the implicit clusters of target user via a community detection algorithm, and establishes the recommendation set consisting of a fixed number of potential friends.
TL;DR: In this paper , an emotion role mining approach based on multiview ensemble learning (ERM-ME) is proposed to detect emotion roles in social networks by fusing the information contained in different features.