Overview of the FIRE 2022 track: Information Retrieval from Microblogs during Disasters (IRMiDis)

Microblogging sites such as Twitter play an important role in dealing with various mass emergencies including natural disasters and pandemics. The FIRE 2022 track on Information Retrieval from Microblogs during Disasters (IRMiDis) focused on two important tasks – (i) to detect the vaccine-related stance of tweets related to COVID-19 vaccines, and (ii) to detect reporting of COVID-19 symptom in tweets. © 2022 Copyright for this paper by its authors.

Classification of Covid-19 Vaccine Opinion and Detection of Symptom-Reporting on Twitter Using Neural Networks

This paper describes my work for the Information Retrieval from Microblogs during Disasters.This track is divided into two sub-tasks. Task 1 is to build an effective classifier for 3-class classification on tweets with respect to the stance reflected towards COVID-19 vaccines.Task 2 is to devise an effective classifier for 4-class classification on tweets that can detect tweets that report someone experiencing COVID-19 symptoms.This paper proposes a classification method based on MLP classifier model.The evaluation shows the performance of our approach, which achieved 0.304 on F-Score in Task 1 and 0.239 on F-Score in Task 2. © 2022 Copyright for this paper by its authors.

CTC: COVID-19 Tweet Classification using CT-BERT

CTC is my submitted work to the Information Retrieval from Microblogs during Disasters (IRMiDis) Track at the Forum for Information Retrieval Evaluation (FIRE) 2022. Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Most people infected with the virus experience a mild to moderate respiratory illness and recover without requiring special treatment. However, some become seriously ill and require medical attention. Vaccines against coronavirus and prompt reporting of symptoms saved many lives during the pandemic. The analysis of COVID-19-related tweets can provide valuable insights regarding the stance of people toward the new vaccine. It can also help the authorities to plan their strategies based on people's opinions about the vaccine and ensure the effectiveness of vaccination campaigns. Tweets describing symptoms can also aid in identifying high-alert zones and determining quarantine regulations. The IRMiDis track focuses on these COVID-19-related tweets that flooded Twitter. I developed an effective classifier for both Tasks 1 and 2. The evaluation score of my submitted run is reported in terms of accuracy and macro-F1 score. I achieved an accuracy of 0.770, a macro-F1 score of 0.773 in Task 1, and an accuracy of 0.820, a macro-F1 score of 0.746 in Task 2. I enjoyed the first rank among other submissions in both the tasks. © 2022 Copyright for this paper by its authors.

Detecting COVID-19 Vaccine Stance and Symptom Reporting from Tweets using Contextual Embeddings

Determining sentiments of the public with regard to COVID-19 vaccines is crucial for nations to efficiently carry out vaccination drives and spread awareness. Hence, it is a field requiring accurate analysis and captures the interest of many researchers. Microblogs from social media websites such as Twitter sometimes contain colloquial expressions or terminology difficult to interpret making the task a challenging one. In this paper, we propose a method for multi-label text classification for the track of”Information Retrieval from Microblogs during Disasters (IRMiDis)” presented by the”Forum of Information Retrieval Evaluation” in 2022, related to vaccine sentiment among the public and reporting of someone experiencing COVID-19 symptoms. The following methodologies have been utilised: (i) Word2Vec and (ii) BERT, which uses contextual embedding rather than the fixed embedding used by conventional natural language models. For Task 1, the overall F1 score and Accuracy are 0.503 and 0.529, respectively, placing us fourth among all the teams, while for Task 2, they are 0.740 and 0.790, placing us second among all the teams who submitted their work. Our code is openly accessible through GitHub. 1 © 2022 Copyright for this paper by its authors.

Emotion Recognition and Analysis Of Netizens Based On Micro-Blog During Covid-19 Epidemic

The research is about emotion recognition and analysis based on Micro-blog short text. Emotion recognition is an important field of text classification in Natural Language Processing. The data of this research comes from Micro-blog 100K record related to COVID-19 theme collected by Data fountain platform, the data are manually labeled, and the emotional tendencies of the text are negative, positive and neutral. The empirical part adopts dictionary emotion recognition method and machine learning emotion recognition respectively. The algorithms used include support vector machine and naive Bayes based on TFIDF, support vector machine and LSTM based on wod2vec. The five results are compared. Combined with statistical analysis methods, the emotions of netizens in the early stage of the epidemic are analyzed for public opinion. This research uses machine learning algorithm combined with statistical analysis to analyze current events in real time. It will be of great significance for the introduction and implementation of national policies.

Digital Transformation in the Smart City During Covid-19: Perceptions of Citizens

The article presents an analysis of the communicative behavior of actors in cyberspace during Covid-19. The novelty of this study lies in the fact that an algorithm is presented for determining the perception and track opinions and attitude changes of metropolitan residents in terms of digital transformation. The material included Russian-language data from social networks, video hosting services, microblogs, messengers, blogs, news, reviews, and forums. The data was collected at the beginning of the third wave of Covid-19 in Russia from June 2, 2021 to June 29, 2021. The study enabled identification of digital transformation aspects that were positively perceived by the residents of Moscow (RF) and found their support;and it also made possible to identify resources the emergence and development of which could lead to an increase in social tension. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

When falsehood wins? Varied effects of sensational elements on users' engagement with real and fake posts

Based on a quantitative content analysis of microblogs on COVID-19 that is linked to the actual Weibo user engagement (comments, reposts, and likes) they received, this study investigates the role of generic/formal frames, emotional appeals, and visual elements in people's varied levels of engagement with fake and real posts. Results revealed that relative to real posts, fake posts tended to focus more on COVID incidents that happened outside of China, utilize more episodic and human-interest frames, rely more on anger and disgust emotions, and feature more pictures. More importantly, although fake posts initially received fewer user responses than real posts, they earned significantly more reactions through employing sensational elements such as anger, conflict, and morality in their content. Most of these post-level characteristics, however, exerted minimal impact on real microblogs. Consequently, as the emotionally charged and sensationally framed fake posts drive more users to comment on and repost them, fake news may diffuse faster than real news and reach a larger audience. © 2023 Elsevier Ltd

Analysis of Public Emotion and Disseminator-Audience Framework during Outbreak of Public Crisis Events: A case study of Microblog Account Xiake Island

This study crawled the cross-sectional data of the contents and comments from Microblog Account Xiake Island during the outbreak of coronavirus pneumonia as subjects, to examine the deviation and resonance association among affective fluctuations of the Chinese public, media framework, and audiences' cognitive framework. Using SnowNLP to conduct sentiment analysis of text comments, we found that during the outbreak of coronavirus pneumonia, the public spent most of the time in low-intensity negative affectivity, and the average affective propensity in response to individual microblog fluctuated greatly, and the public was easily caught in an emotional frenzy, which reduces the level of trust in government. Through a comparison of public affectivity and related epidemic data, Xiake Island focuses on reporting emotional facts, whose construction of social reality contains obvious emotional trajectories. Clustering analysis of thematic framework by LDA algorithm reveals that in terms of framework, the framework Xiake Island uses resonates to a large degree with the framework users focus on. In terms of the level of concerns over the framework, Xiake Island deviates to a certain extent from the public. This deviation, together with the strategy of focusing on reporting emotional facts, is a discursive strategy adopted by the new mainstream media to seek the reconstruction of cultural leadership. © 2022 Owner/Author.

Topic Analysis of Public Welfare Microblogs in the Early Period of the COVID-19 Epidemic Based on LDA Model

Due to the outbreak of COVID-19 in early 2020, a flood of information and rumors about the epidemic have filled the internet, causing panic in people’s lives. During the early period of the epidemic, public welfare information with active energy had played a key role in influencing online public opinion, alleviating public anxiety and mobilizing the entire society to fight against the epidemic. Therefore, analyzing the characteristics of public welfare communication in the early period can help us better develop strategies of public welfare communication in the post-epidemic era. In China, Sina Weibo is a microblog platform based on user relationships, and it is widely used by Chinese people. In this paper, we take the public welfare microblogs released by the Weibo public welfare account “@微公益” (Micro public welfare) in the early period of the epidemic as the research object. Firstly, we collected a total of 1863 blog posts from this account from January to April in 2020, and divided them into four stages by combining the Life Cycle Theory. Then the top 10 keywords from the blog posts of different stages were extracted using word frequency statistics. Finally, the LDA topic model were utilized to find out the topics of each stage whose characteristics of public welfare communication were analyzed in detail. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Research on Microblog Sentiment Analysis Based on Multimodal and Multiscale Feature Fusion

The pneumonia epidemic spread by the 2019 new coronavirus(2019-nCoV) has affected people's lives in any aspects, and has aroused widespread concern in global public opinion. In order to better grasp the real public opinion situation on the Internet and ensure the progress of epidemic prevention and public opinion analysis, this paper conducts research on netizen sentiment analysis for epidemic-related topics in the Internet community, and proposes a multimodal feature fusion solution. For the fusion of image and text modalities, Bi-LSTM and Bi-GRU are used to further learn the intrinsic correlation between modalities on the basis of bidirectional transformer feature fusion, and an image-based multi-scale feature fusion method is proposed, which can better solve the problem in this task. Experiments show that the method proposed in this paper is better than the current mainstream multimodal sentiment analysis methods. © 2022 IEEE.

Let the Big Data Speak: Collaborative Model of Topic Extract and Sentiment Analysis COVID-19 Based on Weibo Data

Micro-blog is an important medium of emergency communication. The topic and emotion analysis of micro-blog is of great significance in identifying and predicting potential problems and risks. In this paper, a collaborative analysis model of emotion and topic mining is constructed to analyze the users’ sentiment and the topics they care about, Firstly, we use SO-PMI to construct domain sentiment lexicon and extract topics with LDA. Then we use the collaborative model to analyze sentiment and topic. The results showed that the model we proposed can present the features of sentiment and topic of user concerns. And through text clustering and sentiment analysis, it is found that the attitude of users towards the COVID-19 has gone through three stages, namely, a period of fluctuating tension and anxiety, a period of slowly rising solidarity and a period of stable self-confidence with little fluctuation, on the whole, positive is greater than negative, positive than negative state. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Retrieval of Actionable Information during Mass Emergency: A Classification Approach

This paper discusses our work submitted to FIRE 2021 IRMiDis Track. The goal was identification of claim or fact-checkable tweets, i.e., tweets that report some verifiable fact or claim.The two tasks addressed in this work are, first, Identifying claims or fact-checkable tweets and second,COVID Vaccine Stance Classification.The evaluation scores of the submitted runs are reported in terms of Precision@100, Recall@1000 and MAP@100. The average MAP score is 0.1587. The score for Vaccine Stance Classification is reported in terms of accuracy and macro-F1-score which came out to be 0.472 and 0.461 respectively. © 2021 Copyright for this paper by its authors.

COVID Vaccine Stance Classification

This paper discusses the work submitted by us for IRMiDis FIRE 2021 Task[2].The goal of this task was to classify tweets related to COVID19 vaccines into three different sentiment classes.Our approach is based on using machine learning techniques to complete this 3-class sentiment classification problem.The evaluation scores of the submitted runs are reported in terms of accuracy and macro-f1 score.The accuracy reported for our classification was 0.448 and the macro-f1 score came out as 0.442. © 2021 Copyright for this paper by its authors.

Analyzing COVID-19 Vaccination

The outbreak of the coronavirus has resulted in unprecedented action, which has led authorities to decide to begin the blockade of the areas most hit by the infectious disease. Social media has been an important support for people during this difficult time. On November 9, 2020, when the first vaccine with an infection rate of 90% or higher was announced, social media responded with, and people around the world began to express the feelings of vaccination. It was no longer a hypothesis, but closer to,every day to become a reality Therefore, it becomes imperative to verify some of the information posted on social media during the pandemic situation, specially related to Covid vaccines. To this end, it is necessary to correctly identify fact-checkable posts, so that their information content can be verified.In this work, we have addressed the problem to identify 3 types of classification on the Twitter microblogging site. We organized a shared task in the FIRE 2021 conference to study the problem of identifyefficient classifier for prediction tweets posted during a particular pandemic scenario (the Covid 19). This paper describes the dataset used in the shared task, and compares the performance of different classification that are provax, antivax and last neutraal for identifying effective tweets related to Covid vaccines.We experimented with a classification-based approach. Our experiment shows that SVM classification performs well in order to effiective posts.Using this support vector machine in order to solve the antivax, provax,neutral classification of twets .We’re going to do this because vaccination is an important step for Covid19 so people can easily fix the news about the vaccine and grab their own slot. © 2021 Forum for Information Retrieval Evaluation, December 13-17, 2021, India.

Effects of Microblog Comments on Chinese User's Sentiment with COVID-19 Epidemic Topics

Social media is one of the most significant sources of information in modern people’s life. Due to the large quantity of user base and public opinions, when people read a blog post, the different tendencies of comments may affect their views on the event to a certain extent. This paper, taking the COVID-19 epidemic as an example, investigated the impact of Weibo (a popular social software in China) comments on readers’ sentiments. In this paper, text mining technology was adopted to collect data including the blogs and the comments under each blog, and the NLPIR-Parser platform was used to analyze the sentiment of the comments. Finally, the conclusion that the sentiments of other comments tend to follow the sentiments of the first comments was drawn. Based on the research results, this paper also gave some enlightenment on social media management and suggestions of public opinions oversight. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

How to Save Lives with Microblogs? Lessons From the Usage of Weibo for Requests for Medical Assistance During COVID-19

During recent crises like COVID-19, microblogging platforms have become popular channels for affected people seeking assistance such as medical supplies and rescue operations from emergency responders and the public. Despite this common practice, the affordances of microblogging services for help-seeking during crises that needs immediate attention are not well understood. To fill this gap, we analyzed 8K posts from COVID-19 patients or caregivers requesting urgent medical assistance on Weibo, the largest microblogging site in China. Our mixed-methods analyses suggest that existing microblogging functions need to be improved in multiple aspects to sufficiently facilitate help-seeking in emergencies, including capabilities of search and tracking requests, ease of use, and privacy protection. We also find that people tend to stick to certain well-established functions for publishing requests, even after better alternatives emerge. These findings have implications for designing microblogging tools to better support help requesting and responding during crises. © 2022 ACM.

Identifying the Adoption or Rejection of Misinformation Targeting COVID-19 Vaccines in Twitter Discourse

Although billions of COVID-19 vaccines have been administered, too many people remain hesitant. Misinformation about the COVID-19 vaccines, propagating on social media, is believed to drive hesitancy towards vaccination. However, exposure to misinformation does not necessarily indicate misinformation adoption. In this paper we describe a novel framework for identifying the stance towards misinformation, relying on attitude consistency and its properties. The interactions between attitude consistency, adoption or rejection of misinformation and the content of microblogs are exploited in a novel neural architecture, where the stance towards misinformation is organized in a knowledge graph. This new neural framework is enabling the identification of stance towards misinformation about COVID-19 vaccines with state-of-the-art results. The experiments are performed on a new dataset of misinformation towards COVID-19 vaccines, called CoVaxLies, collected from recent Twitter discourse. Because CoVaxLies provides a taxonomy of the misinformation about COVID-19 vaccines, we are able to show which type of misinformation is mostly adopted and which is mostly rejected. © 2022 ACM.

Text classification of COVID-19 reviews based on pre-training language model

This experiment analyzed 100,000 epidemic-related microblogs officially provided by the CCF. Using Enhanced Representation through Knowledge Integration (ERNIE), the effect of pre-training model on extracting Chinese semantic information was improved. After that, the deep pyramid network (DPCNN) was merged with ERNIE to save computing costs. Enhanced feature extraction performance for long-distance text. This model was the most effective in the comparison test of six emotional three-category tasks, which improved the accuracy of BERT pre-training model by 7%. © 2022 IEEE.

Multi-Media Content Clustering and Computer Intelligent Analysis by Text Mining

The epidemic situation of covid-19 spread all over the world, which is not optimistic. In order to extract valuable information for the epidemic from the numerous Internet data. With data mining technology, this paper crawls more than 10000 pieces of data from the microblog platform of overseas anti epidemic diary topic, and preprocesses the obtained text data set with word segmentation, removing stop words and other data, extracts the keywords of each microblog through word vector model, counts word frequency, and clustes text. In addition, the emotional value of the text is analyzed. Finally, the data were grouped into seven categories, and the trend chart of emotion value was drawn, and each result was displayed in the way of graph. By analysing, on the one hand, valuable information can be extracted from the micro blog data generated by overseas Chinese to help the domestic people understand the real situation of the overseas epidemic and adjust the risk response measures;on the other hand, the general situation of social media data during the epidemic can be generally understood from the macro perspective to provide reference for government departments in terms of management of entry-exit and epidemic prevention and control. It is helpful to further improve the governance system and the modernization of governance capacity in response to public health emergencies in China. © 2021 ACM.

Overview of the FIRE 2021 track: Information Retrieval from Microblogs during Disasters (IRMiDis)

Microblogging sites such as Twitter play an important role in dealing with various mass emergencies including natural disasters and pandemics. The FIRE2021 track on Information Retrieval from Microblogs during Disasters (IRMiDis) focused on two important tasks - (i) to identify claims or fact-checkable tweets, which is the first step towards verifying information posted on social media, and (ii) to detect the vaccine-related stance of tweets related to COVID-19 vaccines. © 2021 Owner/Author.