Forecasting COVID-19 Vaccination Rates using Social Media Data

The COVID-19 pandemic has had a profound impact on the global community, and vaccination has been recognized as a crucial intervention. To gain insight into public perceptions of COVID-19 vaccines, survey studies and the analysis of social media platforms have been conducted. However, existing methods lack consideration of individual vaccination intentions or status and the relationship between public perceptions and actual vaccine uptake. To address these limitations, this study proposes a text classification approach to identify tweets indicating a user's intent or status on vaccination. A comparative analysis between the proportions of tweets from different categories and real-world vaccination data reveals notable alignment, suggesting that tweets may serve as a precursor to actual vaccination status. Further, regression analysis and time series forecasting were performed to explore the potential of tweet data, demonstrating the significance of incorporating tweet data in predicting future vaccination status. Finally, clustering was applied to the tweet sets with positive and negative labels to gain insights into underlying focuses of each stance. © 2023 ACM.

Mobilizing Social Media Data: Reflections of a Researcher Mediating between Data and Organization

This paper examines the practices involved in mobilizing social media data from their site of production to the institutional context of non-profit organizations. We report on nine months of fieldwork with a transnational and intergovernmental organization using social media data to understand the role of grassroots initiatives in Mexico, in the unique context of the COVID-19 pandemic. We show how different stakeholders negotiate the definition of problems to be addressed with social media data, the collective creation of ground-truth, and the limitations involved in the process of extracting value from data. The meanings of social media data are not defined in advance;instead, they are contingent on the practices and needs of the organization that seeks to extract insights from the analysis. We conclude with a list of reflections and questions for researchers who mediate in the mobilization of social media data into non-profit organizations to inform humanitarian action. © 2023 ACM.

Cross-lingual transfer learning for COVID-19 outbreak alignment

The spread of COVID-19 has become a significant and troubling aspect of society in 2020. With millions of cases reported across countries, new outbreaks have occurred and followed patterns of previously affected areas. Many disease detection models do not incorporate the wealth of social media data that can be utilized for modeling and predicting its spread. It is useful to ask, can we utilize this knowledge in one country to model the outbreak in another? To answer this, we propose the task of cross-lingual transfer learning for epidemiological alignment. Utilizing both macro and micro text features, we train on Italy's early COVID-19 outbreak through Twitter and transfer to several other countries. Our experiments show strong results with up to 0.85 Spearman correlation in cross-country predictions. © ACL 2020.All right reserved.

Cross-language sentiment analysis of European Twitter messages during the COVID-19 pandemic

Social media data can be a very salient source of information during crises. User-generated messages provide a window into people's minds during such times, allowing us insights about their moods and opinions. Due to the vast amounts of such messages, a large-scale analysis of population-wide developments becomes possible. In this paper, we analyze Twitter messages (tweets) collected during the first months of the COVID-19 pandemic in Europe with regard to their sentiment. This is implemented with a neural network for sentiment analysis using multilingual sentence embeddings. We separate the results by country of origin, and correlate their temporal development with events in those countries. This allows us to study the effect of the situation on people's moods. We see, for example, that lockdown announcements correlate with a deterioration of mood in almost all surveyed countries, which recovers within a short time span. © ACL 2020.All right reserved.

Recurrent neural network based sentiment analysis of social media data during corona pandemic under national lockdown

The Corona virus pandemic has affected the normal course of life. People all over the world take the social media to express their opinions and general emotions regarding this phenomenon. In a relatively short period of time, tweets about the new Corona virus increased by an amount never before seen on the social networking site Twitter. In this research work, Sentiment Analysis of Social Media Data to Identify the Feelings of Indians during Corona Pandemic under National Lockdown using recurrent neural network is proposed. The proposed method is analyzed using four steps: that is Data collection, data preparation, Building sentiment analysis model and Visualization of the results. For Data collection, the twitter dataset are collected from social networking platform twitter by application programming interface. For Data preparation, the input data set are pre-processed for removing URL links, removing unnecessary spaces, removing punctuations and numbers. After data cleaning or preprocessing entire particular characters and non-US characters from Standard Code for Information Interchange, apart from hash tag, are extracted as refined tweet text. In addition, entire behaviors less than three alphabets are not assumed at analysis of tweets, lastly, tokenization and derivation was carried out by Porter Stemmer to perform opinion mining. To authenticate the method, categorized the tweets linked to COVID-19 national lockdown. For categorization, recurrent neural method is used. RNN classify the sentiment classification as positive, negative and neutral sentiment scores. The efficiency of the proposed RNN based Sentimental analysis classification of COVID-19 is assessed various performances by evaluation metrics, like sensitivity, precision, recall, f-measure, specificity and accuracy. The proposed method attains 24.51%, 25.35%, 31.45% and 24.53% high accuracy, 43.51%, 52.35%, 21.45% and 28.53% high sensitivity than the existing methods. © 2023 - IOS Press. All rights reserved.

A social media-based framework for quantifying temporal changes to wildlife viewing intensity: Quantifying changes to wildlife viewing intensity

Documenting how human pressure on wildlife changes over time is important to minimise potential adverse effects through implementing appropriate management and policy actions;however, obtaining objective measures of these changes and their potential impacts is often logistically challenging, particularly in the natural environment. Here, we developed a modular stochastic model that infers the ratio of actual viewing pressure on wildlife in consecutive time periods (years) using social media, as this medium is widespread and easily accessible. Pressure was calculated from the number of times individual animals appeared in social media in pre-defined time windows, accounting for time-dependent variables that influence them (e.g. number of people with access to social media). Formulas for the confidence intervals of viewing pressure ratios were rigorously developed and validated, and corresponding uncertainty was quantified. We applied the developed framework to calculate changes to wildlife viewing pressure on loggerhead sea turtles (Caretta caretta) at Zakynthos island (Greece) before and during the COVID-19 pandemic (2019–2021) based on 2646 social media entries. Our model ensured temporal comparability across years of social media data grouped in time window sizes, by correcting for the interannual increase of social media use. Optimal sizes for these windows were delineated, reducing uncertainty while maintaining high time-scale resolution. The optimal time window was around 7-days during the peak tourist season when more data were available in all three years, and >15 days during the low season. In contrast, raw social media data exhibited clear bias when quantifying changes to viewing pressure, with unknown uncertainty. The framework developed here allows widely-available social media data to be used objectively when quantifying temporal changes to wildlife viewing pressure. Its modularity allowed viewing pressure to be quantified for all data combined, or subsets of data (different groups, situations or locations), and could be applied to any site supporting wildlife exposed to tourism. © 2022 The Author(s)

Social Media Mining and Analysis to support authorities in COVID-19 pandemic preparedness

Social media become the main tool for spreading news, discussing ideas and comments on world events. Accordingly, social media represents a precious source to extract insight into public opinion and sentiment. In particular, Twitter has been already recognized as an important source of health-related information, given the amount of news, opinions and information that is shared by both citizens and official sources. Since the very first days of COVID-19 outbreak, people exchanged news, updates, sentiment and opinion about the pandemics. The aim of the study reported in this paper is to explore how social media has been exploited to fight COVID-19. In particular, the attention is given on analyzing engagement and interest in the COVID-19 topics and their evolution on a global scale, identifying infodemics, also analysing people feelings and reactions. © 2022 IEEE.

Mobility Pattern Analysis during 2020 Chinese National Day under the COVID-19 pandemic

With the fast development of new technologies, such as Internet of Things, big data and Internet plus, Intelligent Transportation Systems (ITS) have made remarkable achievements and the intelligence in ITS has also been continuously increased, which a new field, i.e., Social Transportation, is emerging. In social transportation systems, physical and cyber elements are tightly conjoined, coordinated, and integrated with human and social characteristics. In this paper, we collect and analyze traffic data from physical world and social media data from cyberspace to sense the human mobility patterns during holidays under the COVID-19 pandemic. © 2022 IEEE.

Resilience of virtual and real shops under the impact of Covid19 epidemic: an empirical study on the distribution of breakfast service in Beijing

The breakfast service is an important part of local vitality which are traditionally provided by restaurants and street vendors in real space. The booming virtual economy and delivery service provide alternative type. With the outbreak of COVID-19, both the temporary lock-down of many streets and the reduction of travelling have great impact on breakfast service at the beginning of 2020. During this epidemic period, what kind of breakfast service suffers more, if the location matters, these became interesting questions. This paper presents a comparative study on the central city area (160km2) of Beijing before and after the impact of Covid19. Based on two site surveys in 2019 July and 2020 September, over 3000 breakfast service are mapped in 6 categories (Chain restaurant, subcontracted breakfast service, fixed vendor stance, mobile vendor stance, supermarket and bakery) in real space. Cell phone data of 2018 and 2020 are also used to provide other factors such as employment/residential densities and distances of commuting. Additionally, social media data of breakfast distribution from Dazhongdianping.com are collected to study how service in real and virtual space overlap. In general, it can be found that the space with dominant accessibility has stronger resilience. Breakfast services in an advantageous position are more likely to expand new opportunities through the network platform in virtual space. © 2022 Proceedings 13th International Space Syntax Symposium, SSS 2022. All rights reserved.

How the COVID-19 Pandemic Influences Human Mobility? Similarity Analysis Leveraging Social Media Data

This paper aims to understand the impact of the COVID-19 on human mobility. We explore individual traces through spatial-temporal check-ins on social media. In particular, we leverage geo-tagged tweets, to extrapolate people's geo-locations in New York City (NYC) when they check in Twitter. Building on these data, we perform gyration and travel similarity analysis to study the change of travel pattern during the pandemic. We make a comparison of users' gyration and the number of COVID-19 deaths across time. We find that (1) Users' gyration decreased by 35% after the stay-at-home order. (2) Check-in activities on social media is related to the fear of coronavirus: User's gyration has a negative correlation (-0.7) with the number of deaths across time. (3) Travel similarity decreased by 15% from March 2020 to June 2020 because many people did not travel outside after the stay-at-home order. (4) Inter-personal travel similarity among users was lower than 0.2 and individual traces of a majority of people had no overlap during the pandemic. © 2022 IEEE.

Social Media Data-Driven Sentiment Analysis for COVID-19 and COVID-19 Vaccines

In this paper, we analyze social media data (e.g., tweets) related to coronavirus disease 2019 (COVID-19) and COVID-19 vaccines. The main objective is to explore daily COVID-19 cases and vaccine rates in addition to analyzing sentiments and discussions related to COVID-19 vaccination on social media, e.g., Twitter. During the early days of the pandemic, there were rapid developments of vaccines that can prevent the novel COVID-19. However, the potential hurdles of developing COVID-19 vaccines faster than any other conventional vaccine has made some people apprehensive about taking the COVID-19 vaccine. Since social media keeps individuals connected locally and globally, Twitter as a social networking platform is a great way to collect information on tweets related to the coronavirus vaccine. Specifically, this paper studies various data analytic tools that can help study the changes in users' opinions and emotions related to coronavirus vaccines, as well as studying the coronavirus cases and vaccine rates globally. Furthermore, this study will enable individuals to get real-time insights into the sentiments of COVID-19 vaccines based on social media tweets. © 2022 IEEE.

Measuring COVID-19 Vaccine Hesitancy: Consistency of Social Media with Surveys

We validate whether social media data can be used to complement social surveys to monitor the public’s COVID-19 vaccine hesitancy. Taking advantage of recent artificial intelligence advances, we propose a framework to estimate individuals’ vaccine hesitancy from their social media posts. With 745,661 vaccine-related tweets originating from three Western European countries, we compare vaccine hesitancy levels measured with our framework against that collected from multiple consecutive waves of surveys. We successfully validate that Twitter, one popular social media platform, can be used as a data source to calculate consistent public acceptance of COVID-19 vaccines with surveys at both country and region levels. In addition, this consistency persists over time although it varies among socio-demographic sub-populations. Our findings establish the power of social media in complementing social surveys to capture the continuously changing vaccine hesitancy in a global health crisis similar to the COVID-19 pandemic. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

How Do Engineering Students Characterize Their Educational Experience on a Popular Social Media Platform Before and During the Covid-19 Pandemic?

Public social media platforms can supplement our understanding of student perceptions of engineering teaching. Looking to social media can help us build a picture of what steps we can take to improve the learning experience. It has the potential to provide meaningful information without requiring more data collection from students. This is particularly salient in times of crisis when contact with students may be inconsistent and when data such as survey results may be more challenging to obtain. In this study, we analyzed social media data from Reddit towards developing an understanding of engineering students' attitudes and focus areas around their educational experience before and during the Covid-19 pandemic. Students' attitudes were mainly evaluated by sentiment analysis and students' focuses were explored through topic modeling techniques. Both sentiment analysis and topic modeling are a form of natural language processing. Sentiment analysis is a tool to study the feelings expressed in text while topic modeling allows us to look for groups of related phrasings and to obtain a sense of the topics being discussed. Both are readily available through open-source Python packages. Based on the sentiment analysis, findings were categorized as positive, negative, and neutral. Within these three areas, we used topic modeling to categorize and explore the different emphasis areas brought up by students (e.g., extracurricular activities, school assignments). We present the results of the modeling using a topic visualization and interpretation tool. Although this work illustrates computational methods for analyzing social media data, these tools are seen pragmatically as a means to an end and not the sole purpose of our inquiry. Social media analyses have limitations and ethical considerations, and this work is not meant to supersede other forms of evaluation. Rather, our study explores the use of social media as a potential complementary source of data for practitioners. Our work has implications for educators and institutions looking to develop low-impact ways to evaluate educational programming in times of crisis and beyond. We hope that by presenting this work to other researchers and practitioners in engineering education, we will engage in mutually beneficial conversations around the pros and cons of using social media data and its potential applications. © American Society for Engineering Education, 2022

MAISoN'22: 8th International Workshop on Mining Actionable Insights from Social Networks Special Edition on Mental Health and Social Media

The eighth edition of the workshop on Mining Actionable Insights from Social Networks (MAISoN 2022) took place virtually on April 26th, 2022, co-located with the ACM Web Conference 2022 (WWW 2022). This year, we organized a special edition with focus on mental health and social media. The aim of this edition was to bring together researchers from different disciplines to discuss research that goes beyond descriptive analysis of social media data and instead investigate different techniques that use social media data for building diagnostic, predictive and prescriptive analysis models for mental health applications. This topic attracted a lot of interest from the community especially because of all the considerations surrounding the impact of social media during the COVID-19 pandemic which has impacted on people's mental health issues. © 2022 Owner/Author.

Social media and pandemic events: challenges for alert-warning systems

Nowadays, social media platforms generate an immense amount of information in the form of text, images, video, sound, among others. Their capabilities and reliability during adverse situations have made them society's go-to communication method as they continue to operate while more traditional methods fail [1]. With the unexpected arrival of the COVID-19 pandemic, billions of tweets had been generated, bringing both opportunities and challenges to emergency managers when seeking to leverage social media data as a source of information. Therefore, this research investigates how emergency managers could utilize social media data for monitoring public sentiment to enhance their strategic decision-making process. To achieve our end objective, we have adapted a visual analytics framework that has been developed for alerting and monitoring public sentiment during product recalls [2]. The proposed work understands that by developing an alert warning system based on collective sentiment analysis, decision makers will be able to identify scenarios where significant levels of negative or positive sentiment are being disseminated. The alert warning system framework includes concepts on data analytics, natural language processing, and machine learning techniques as mechanisms to generate inferences from social media applications. To illustrate our work, we extracted a sample of 24.7 millions of COVID-19 related tweets from the region of El Paso, TX, which in November 2020 was one of the most dangerous COVID-19 hotspots in the United States [3]. Our results indicate that the adapted framework is an initial step when seeking to assist emergency managers when seeking to utilize social media data;however, it has been found that additional challenges must be addressed before emergency domain decision makers can fully adopt it into their management strategies. © 2022 IEEE.

AMUSED: An Annotation Framework of Multimodal Social Media Data

Social media has become popular among users for social interaction and news sources. Users spread misinformation in multiple data formats. However, systematic studying of social media phenomena has been challenging due to the lack of labelled data. This paper presents a semi-automated annotation framework AMUSED for gathering multilingual multimodal annotated data from social networking sites. The framework is designed to mitigate the workload in collecting and annotating social media data by cohesively combining machines and humans in the data collection process. AMUSED detects links to social media posts from a given list of news articles and then downloads the data from the respective social networking sites and labels them. The framework gathers the annotated data from multiple platforms like Twitter, YouTube, and Reddit. For the use case, we have implemented the framework for collecting COVID-19 misinformation data from different social media sites and have categorised 8,077 fact-checked articles into four different classes of misinformation. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Improving Text Clustering Using a New Technique for Selecting Trustworthy Content in Social Networks

Today’s information society has led to the emergence of a large number of applications that generate and consume digital data. Many of these applications are based on social networks, and therefore their information often comes in the form of unstructured text. This text from social media also tends to contain a high level of noise and untrustworthy content. Therefore, having systems capable of dealing with it efficiently is a very relevant issue. In order to verify the trustworthiness of the social media content, it is necessary to analyse and explore social media data by using text mining techniques. One of the most widespread techniques in the field of text mining is text clustering, that allows us to automatically group similar documents into categories. Text clustering is very sensitive to the presence of noise and so in this paper we propose a pre-processing pipeline based on word embedding that allows selecting trustworthy content and discarding noise in a way that improves clustering results. To validate the proposed pipeline, a real use case is provided on a Twitter dataset related to COVID-19. © 2022, Springer Nature Switzerland AG.

Social Response to COVID-19 SMART Dashboard: Proposal for Case Study

The COVID-19 pandemic took a toll on the world’s healthcare infrastructure as well as its social, economic, and psychological well-being. In particular, Italy’s unexpectedly high COVID-19 case and death rate from March to June, 2020, captured headlines due to its speed and virulence. Many governments are currently implementing measures to help contain and slow down the spread of COVID-19. The Social Response to Covid-19 Smart Dashboard was built by researchers at the Metabolism of Cities Living Lab, Center for Human Dynamics in the Mobile Age at San Diego State University and Politecnico di Milano. This dashboard provides an aggregated view of what people in 10 Italian metropolitan cities (Milan, Venice, Turin, Bologna, Florence, Rome, Naples, Bari, Palermo, and Cagliari) tweet during the pandemic by monitoring social media behaviors in the north, center, south, and islands. Moreover, the dashboard is a geo-targeted search tool for Twitter messages to monitor the diffusion of information and social behavior changes which provides an automatic procedure to help researchers to: associate tweets based on geography differences, filter noises such as removing redundant retweets and using machine learning methods to improve precisions, analyze social media data from a spatiotemporal perspective, and visualize social media data in various aspects such as weekly trends, top urls, top retweets, top mentions, and top hashtags. The Social Response to Covid-19 SMART Dashboard provides a useful tool for policy makers, city planners, research organizations, and health officials to monitor real-time societal perceptions using social media. © 2022, The Author(s).

Impact of COVID-19 on Tourists’ Travel Intentions and Behaviors: The Case Study of Hong Kong, China

Over the past two years, the COVID-19 pandemic had a major worldwide health, economic and daily life impact. Amongst many dramatic consequences, such as major human mobility disruptions at all scales, the tourism sector has been largely affected. This raises the need for the development of quantitative and qualitative research to favor a better understanding of the impact of the pandemic on human travel behaviors. This study introduces a computational approach that combines inference mechanisms and statistics to quantify tourists’ travel behaviors before and during the pandemic by exploring the evolution of the patterns extracted from a local tourism social network from 2019 to 2020 in the city of Hong Kong. The results show that the COVID-19 pandemic: 1) has a major influence on travel intentions that mainly swift from journeys with generally long sequences of attractions to rather single attractions;2) lead to a decline when considering connections between popular attractions, while the strength of connections within other attractions increase;3) generates novel patterns such as tourists preferring relaxing visits and even minor attractions. © 2022, Springer Nature Switzerland AG.

Visualization of POI Competitiveness Using Extracted Map Tiles from Social Media Response Since COVID-19

Since the spread of COVID-19 around the world, a series of policies and measures are adopted by the Japanese government to control the epidemic. As a result of these policies, people's daily life and the functional division of society have changed. In order to understand the changes in urban function and people's daily behavior over the past year, we collected and analyzed over 1.13 million social media data (Twitter in our example) containing geographic information. We propose regional competitiveness, which represents the access frequency of social data in each raster unit to several attributes. In order to analyze the regional competitiveness in different categories and map tiles, we applied an improved spatio-temporal graph attention network model (ST-GAT) based on unstructured POI (point of interest) data and Twitter data in different levels of the map to the city-regional competitiveness. We have developed and evaluated the competitiveness map tiles based on 5 attributes utilized Twitter data at 2020 of Kyoto in Japan. As the spread of COVID-19 disease and government anti-epidemic measures change the frequency of visits to the core of the city and the trend of regional competitiveness, and our results showed that the regional competitiveness in the map tiles obtained by social media data and POI data visualizes the dynamic change analysis of crowd behavior activities and urban social functions. This research enlightens the promising future of spatio-temporal GAT in users' dynamic responses with geographic information. © 2021 ACM.