Using Hashtags to Analyze Purpose and Technology Application of Open-Source Project Related to COVID-19

COVID-19 has had a profound impact on the lives of all human beings. Emerging technologies have made significant contributions to the fight against the pandemic. An extensive review of the application of technology will help facilitate future research and technology development to provide better solutions for future pandemics. In contrast to the extensive surveys of academic communities that have already been conducted, this study explores the IT community of practice. Using GitHub as the study target, we analyzed the main functionalities of the projects submitted during the pandemic. This study examines trends in projects with different functionalities and the relationship between functionalities and technologies. The study results show an imbalance in the number of projects with varying functionalities in the GitHub community, i.e., applications account for more than half of the projects. In contrast, other data analysis and AI projects account for a smaller share. This differs significantly from the survey of the academic community, where the findings focus more on cutting-edge technologies while projects in the community of practice use more mature technologies. The spontaneous behavior of developers may lack organization and make it challenging to target needs.

. An example of  In this section, we review two aspects of the pandemic. We first check the impact of COVID-19 on society in general and then highlight the importance of technology in the context of a pandemic. We then review relevant studies that have examined the application of technology during the pandemics.

Overview of pandemic impact
The pandemic has profoundly impacted the global economy, industrial production, and lifestyles. Assessing this impact and identifying possible challenges is essential for discovering possible mechanisms to address these issues. Numerous studies have examined the effects of pandemics on different aspects of social life.
By mining and analyzing policy texts and social media data, combined with interviews and other methods, the researchers tried to reveal the pandemic's impacts on social life deeply. Researchers try to analyze the effect of the pandemic on the global economy and seek solutions to the crisis (Song and Zhou 2020). The pandemic can bring terrible disruptions to financial markets, and measuring the predictability of market fears is vital to stabilizing financial markets (Ghosh and Sanyal 2021). The pandemic has forced companies to adjust their strategies and goals (Yadav, Kar, and Kashiramka 2021), creating much uncertainty for employment (Koch, Plattfaut, and Kregel 2021). The pandemic limited travel and made people's work and study quickly shift online, changing the way people live (Chakraborty and Kar 2021).
The impact of COVID-19 on the global economy is undoubtedly devastating. But in this crisis, there are both opportunities and challenges. The pandemic accelerated the adoption of emerging technologies, which contributed to saving lives and improving health during the pandemic (Brem, Viardot, and Nylund 2021). Big data technologies help us predict and control the spread of pandemics (e.g., Srinivasa Rao & Vazquez, 2020). Artificial intelligence technology can achieve rapid patient diagnosis and new drug discovery (e.g., Apostolopoulos & Mpesiana, 2020;Randhawa et al., 2020). Emerging technologies are critical for us to beat the pandemic, and open-source projects guarantee the rapid adoption of these emerging technologies.

Investigation of technology application during the pandemic
Innovations and technology applications have provided new solutions to many challenges during the pandemic. Many researchers have examined technological innovations and applications during the pandemic. Most reviews of technology applications during the pandemic have examined numerous technologies during the pandemic through a survey of academic literature, including Industry 4.0 technologies (Javaid et al. 2020), digital technologies (Wang et al. 2021), artificial intelligence, cloud computing (Alharbi and Abdur Rahman 2021), etc. Data for the survey of the academic literature were primarily obtained from numerous literature databases, including Scopus, Google Scholar, Science Direct, and Research Gate (e.g., Javaid et al. 2020;Vaishya et al. 2021). In addition to the academic literature review, some studies have also reviewed technology applications during the pandemic by examining patents (e.g., Solanki et al. 2021;Alshrari et al. 2022). A few studies have examined the use of technology during the pandemic by reviewing news (e.g., Zhao et al. 2021).
Academic literature and patents are the most direct results of scientific research and reflect the innovation and application of technologies during the pandemic. But there is also a need to transfer knowledge from scientific and technological achievements to production applications, which requires sharing technology, skills, and knowledge from one institution to another (de Wit-de Vries et al. 2019). As an essential tool for knowledge sharing and organizational learning (Marsick, Shiotani, and Gephart 2014), communities of practice serve as a bridge between technology and knowledge. As one of the world's largest IT communities of practice, GitHub launched many opensource projects during the pandemic. These open-source projects share code and allow community members to create and build together. Open strategies can yield higher returns (Barge-Gil 2013), while modern software implementations rely entirely on open-source libraries and components (Shrestha et al. 2020). Community plays a unique role in creating new things (Soos and Leazer 2020); it can be seen that the open-source community is the leading practice frontier of open innovation. Open-source projects provided many open-source libraries and components for the rapid adoption of technologies during the pandemic. Learn how the pandemic "mobilized" specific communities of practice by reviewing the functionality of projects started by the open-source community during the pandemic and the technologies used. This will help the community of practice provide better solutions in response to the COVID-19 pandemic and future pandemics.
To discover the functionality of pandemic-related projects in the Github community and apply different technologies, this study uses the Readme and Topics of open-source projects as the data source for analysis. This study first explores the main types of projects through cluster analysis, explores the association between functionality and related technologies through correlation analysis, and analyzes the evolution of open-source projects in the GitHub community during the pandemic.
The specific methods of this study mainly include five steps: data collection, data cleaning and preprocessing, synonym hashtags preprocessing, hashtags extraction, and data analysis. The research method is shown in Figure 2.

Data Collection
GitHub has a large number of low-quality projects that are not managed. These projects cannot be analyzed in this study. The community's response to the project is an appropriate standard to measure the quality of the project. Therefore, this study uses the number of project stars to measure the project quality and limits the number of project stars to be greater than 0. Since the name in the early stage of the epidemic is not clear, we used "2019-ncov", "coronavirus," and "COVID-19" as the search terms to ensure good recall. Limit the project creation time to August 31, 2020, and retrieve it with GitHub's search API 3 . Use the crawler tool to collect the retrieved item data. The data collection time is September 23, 2020, and we ordered 16,706 data.

Data cleaning and pre-processing
This section mainly introduces data cleaning and pre-processing. The functionality of the project and the technology used are hidden in the title, Readme, and hashtag. The title and Readme text were merged into a project introduction to facilitate analysis. Due to the lack of project introduction in some projects, it is difficult for other developers to understand the specific purpose and functionality of the project. This kind of project was not taken as our analysis object, so we screened out 15541 projects whose title and Readme are not empty and whose stars are more significant than 0. The developers in GitHub come from worldwide, so the title and Readme of the project also contain multiple languages 4 . We used Baidu translation API5 to translate all non-English project introductions into English to conduct a unified analysis. The translated project introduction was sorted in descending order according to the number of characters, and the distribution chart of project introduction length, as shown in Figure 3, was obtained. As shown in Figure 3, the length of the project introduction follows the power-law distribution.
Because some project introductions are too short to clearly describe the project purpose and other information, these projects need to be removed. Through manual screening, it was found that the introduction was more complete when the introduction length was more significant than 200.
Therefore, this study excluded projects with introduction lengths below 200. After screening, a total of 12,199 items were retained. For the remaining tasks, we remove the stop words 6 and other special characters from the introduction (i.e., only English and numbers are kept). The data processing results are shown in Table 1

Synonym hashtags processing
Since the project Topics are labeled by different project developers, there is no uniform standard.
There will be many synonymous hashtags, which need to replace the synonymous hashtags with a unified hashtag. Synonymous hashtags mainly include the following four situations: i. Different forms of the same root word, such as "healthy" and "health," "chatbots," and "chatbot".
Given the above four situations, the present study found synonymous hashtags by calculating the semantic similarity and editing distance (Marzal and Vidal 1993) of hashtags and combining them with manual filtering. With the help of the gensim 7 , the Word2Vec is used to train the word vectors with a corpus of the introduction of all projects (Mikolov et al. 2013). For each hashtag in the existing topic collection, the ten tags that are most semantically similar to the hashtag and the hashtags less than one-fifth of the word length away from the edit of that hashtag are selected. The similar hashtags automatically screened out are manually screened to find synonymous hashtags and build a synonym dictionary. The number of synonymous hashtags in different situations is shown in Table 2.

Synonymous hashtag type Proportion
Different forms of the same root word

Spelling errors
Hyphens exist in the same hashtags Synonymous tags

Hashtag extraction
One of the main objectives of this study is to analyze the purpose and functionality of the project.
We will explore and analyze the intended purpose, the functionality, and the programming language with the help of the project hashtags. In the filtered data of this article, 3840 projects have been tagged by developers, and the remaining 8359 items were not. Therefore, this study will train classification models using labeled data and automatically assign tags to unlabeled data.
There are a large number of low-frequency hashtags. This study selected the core hashtags from the existing collection as candidate hashtags for analysis to reduce the interference of low-frequency words and facilitate visualization. The current hashtags were sorted in descending order of word frequency. As shown in Figure 4, the word frequency ranking of these hashtags presents a powerlaw distribution. Formula (1) is the calculation formula of cumulative word frequency proportion.
The Ci is the word frequency of the ith tag with the highest word frequency. The P is greater than or equal to 50%, and the top k hashtags in descending order of word frequency were selected as the core hashtags.
In this paper, we used TfidfVectorizer in sklearn 9 to vectorize the text. This method takes TF-IDF (Salton and Buckley 1988) as feature weight. The present study used chi-square to select features. We tried the commonly used supervised learning models, including SVM(Chang and Lin 2011), NB (Manning et al. 2008), KNN (Keller, Gray, and Givens 1985), Logistic regression (Hosmer et al. 2013), andRandom Forest (Breiman 2001). The best model from the above models was selected to predict the data with missing hashtags.

The functionality identification and technology application analytics
This section mainly introduces the method and process of co-word clustering analysis, word frequency statistical analysis, and association rule mining for hashtags to analyze the project purpose, functionality, and related technology application.

Co-word clustering analysis
The project hashtags contain the expected purpose and functionality of the project, and the cooccurrence of different hashtags can reflect the relevance between the project purpose and functionality. Therefore, the co-word cluster analysis was carried out on the project hashtags to understand the primary goals and functionality of the epidemic-related open-source projects. The basic idea of spectral clustering comes from the spectral graph partition theory. Spectral clustering transforms the clustering problem into a graph partitioning problem. The partitioning into subgraphs has the maximum similarity within the subgraphs and the minimum similarity between the subgraphs (Jianbo Shi and Malik 2000). A co-occurrence matrix is created based on the co-occurrence of the labels, and the co-word matrix is converted into an affinity matrix using Ochiia coefficients. The affinity matrix was spectrally clustered using the sklearn 10, and the optimal number of clusters was judged using the Calinski Harabaz (1974)

Association rules mining
To explore the usage of related technologies in different projects, this article used association analysis to mine the association rules hidden between hashtags. Apriori is a widely used association rule mining algorithm proposed by R. Agrawal and r. Srikant(1994). We used Apriori to mine association rules. Based on the specific tasks of this paper, support, confidence, and lift are explained as follows. The support of hashtag set A is the probability that all hashtags in set A co-occur in the A rule with support and confidence greater than the threshold is a strong association rule. Lift is a reflection of whether the association rule is valuable or not. If it is greater than 1, the association rule is useful, and the greater the lift is, the better the association rule is. The association between project functionality and related technologies between technologies and technologies is further analyzed by mining the association in project hashtags.
This section introduces the purpose and functionality of COVID-19 related projects, the evolution trend of open-source projects on GitHub, and the correlation between functionalities and technologies.

Experiment results of hashtags extraction
The task in this article is a multi-label classification task. The multi-label classification task mainly contains two types of evaluation, i.e., label-based and sample-based (Zhang and Zhou 2014). In this paper, the sample-based is chosen as the evaluation metric. The present study used macro precision Data with developer-tagged projects were used as the training set, and five-fold cross-validation was performed. The evaluation results of SVM, NB, KNN, Logistic Regression, and Random Forest are shown in Table 3. It can be seen from Table 3 that NB and SVM can perform better in this task. Since SVM had a higher accuracy rate, we chose SVM to extract hashtags. The top ten projects marked with stars in the classification results are given in

The purposes and functionalities of COVID-19 related projects on Github
According to the co-occurrence of hashtags, the results of hashtag extraction were clustered. When the number of clusters was equal to 4, the Calinski and Harabasz score was highest. Therefore, the cluster number was determined as 4. The clustering results were imported into vosViewer for visualization display, and the clustering results are shown in Figure 5. According to the clustering in Figure 5, different colors represent different clusters. It can be seen that the purposes and functionalities of COVID-19 related open-source projects on GitHub mainly include the following four contents: i. Application of AI. In the blue part of Figure 5, the functionalities of the projects in this category mainly include the application of AI in the pandemic. Some projects use deep learning, machine learning (machine learning, deep learning), and other technologies to assist the diagnosis of patients with clinical data. Some projects use computer vision technology for mouthpiece detection, control of social distance, etc. In addition, some of the projects provide information sharing portals for drug discovery, the use of machine learning methods to identify potential drugs, etc.
ii. Application of data science. In the green section of Figure  In summary, the GitHub community of practice projects that started during the pandemic wanted to address the following three main areas: web and mobile app-based data information services, applications around all phases of the data science lifecycle, and the application of AI.

Trends of COVID-19 related projects
The average word frequency share of hashtags in the above four categories of functionalities was counted, and the results are shown in Figure 6. As seen in Figure 6, the most significant number of COVID-19-related projects are apps. Apps include mobile apps and web apps, which account for more than 50%. Projects for data science and AI applications account for 30% and 18%. Applications of data science

Web app Mobile app Mean Word Frequency Ratio
Type of Functionality of developers with each skillset is regular, the trend of change in projects across functional types is also steady. Understandably, data science-related applications reached a high peak earlier. Both applications and AI applications need to be built on a data foundation. Only after the initial data collection and storage are completed can a range of applications such as data information services be performed.

Association rules between functionalities and technologies
The minimum support and minimum confidence were set to 0.002 and 0.2. To reduce the complexity, only the frequent binomial set was extracted. Data science and AI application projects are highly correlated, so association rules are removed for the hashtags of data science and AI projects. The network diagram is drawn based on the obtained association rules with a lift greater than 1. Figure 8. Association rules network of data science and AI application projects in GitHub. Figure 8 shows the association rules between hashtags in data science and AI-related applications.
The blue part in the figure is the hashtags for AI-related projects, and the green part is the hashtags for data science related projects. Each node in the graph represents a hashtag. The larger the node, the higher the word frequency. The connection between nodes means that there are association rules between them. The thickness of the link reflects the lift of the association rule. The thicker the link, the higher the lift degree. Whether it is a data science application or an AI-related application, Python is the core programming language that deserves to be used in such applications. The technologies associated with it include various python libraries and frameworks. The R language also has a place in this category, but its use is mainly limited to data analysis. As shown in Figure 8, the pandemic-related data (covid19 data) focuses on openness (open data), and the data storage formats are mainly CSV and JSON. At the same time, these data focus on the timeliness of epidemic tracking (Coronavirus real time). In applying artificial intelligence, deep learning and machine learning are the core technologies in AI-related projects. In applications related to computer vision, convolutional neural networks (CNN) are closely associated with it. TensorFlow is the most commonly used framework for AI-related projects on GitHub, and Pytorch is also used, but less so than TensorFlow. This is different from the academic community, where Pytorh has been used more often in the last two years 13 . This is a reflection of the differences between the academic and practice communities.  Figure 9 shows the association rules between hashtags in the application project, where red is the hashtag of the web application and yellow is the hashtag of the mobile application. This kind of project occupies an important position in the COVID-19 related projects of GitHub. From Figure 9, we can determine the mainstream programming languages and frameworks used by developers in the community of practice for Android and IOS development today. Android developers in the community of practice mostly use Java, dart, and Kotlin as programming languages. Swift is the only programming language highly related to IOS development. The technology related to web applications shown in Figure 9 is mainly front-end technology. As you can see, the react framework occupies an essential position in front-end technology, and some JavaScript libraries can also be found widely used on GitHub.
This study reviews the functionalities of COVID-19-related projects in the IT community of practice in the pandemic and the associations between functionalities and technologies. This section will discuss three aspects of IT applications during the pandemic, the association between functionalities and technologies and implications for practice.

The application of technology in communities of practice during the pandemic
Our findings suggest that projects submitted in the GitHub community during the pandemic were mainly related to data information services, outbreak trend prediction, and diagnostic treatment of the outbreak. The public needs to be informed of the current outbreak transmission status in the pandemic and access and communicate information. Infectious disease scientists need outbreak data to make predictions about the spread and development of an outbreak. Biomedical researchers need patient data to find ways to diagnose and treat patients. An essential foundation for achieving these needs is data, including outbreak statistics (statistical information on confirmed diagnoses, deaths, and cures), patient clinical data, etc. However, the performance of the emerging technology is not yet stable due to insufficient COVID-19 data sets, non-uniform data formats, missing data, and noise (Mbunge et al. 2021). As you can see, data is an important foundation for technology applications during the pandemic. Looking at the projects submitted on GitHub, there was a rapid increase in data science related projects at the beginning of the pandemic. This phenomenon reflects the quick response of the community to the pandemic.
The evolution of the different projects on GitHub shows that the various functional open-source projects have the same evolutionary trends. This may reflect the skill composition of the people in the community of practice. The submission of projects in the community is partly demand-driven of the pandemic but more likely related to the developers' skills in the community. At the time developers join the community, they possess specific skills. This makes them prefer or only implement certain applications. In a community of practice, these preferences and tendencies will influence community members (developers) to create and absorb new knowledge (Roberts, 2006).
Communities of practice are groups of people who share expertise and are passionate about a common cause, connected informally (Wenger & Snyder, 2000). Developers develop and upload related projects spontaneously, and this spontaneous lack of organized development activity may not meet actual demand quickly enough.

Association between functionalities and technologies
This study explores the correlation between project functionalities and technologies through opensource project hashtags correlation analysis. It provides a macro perspective on the current state of adoption of new technologies in the GitHub community of practice. The GitHub community has the highest percentage of apps in its pandemic-related projects. The main programming languages used in developing these applications include Java, Dart, Flutter, JavaScript, etc. These technologies are widely accepted and used in the community of practice, and there are many developers in the community who have mastered the skill. These technologies have a high impact and a high level of technology maturity. Other vital projects in the community are related applications based on data science and artificial intelligence. The technologies involved in these projects mainly include data modeling, predictive analytics, machine learning, and deep learning. Artificial intelligence applications occupy the smallest share of the community. The application of artificial intelligence provides some innovative solutions to combat the pandemic, and this type of technology is more cutting-edge and an important direction for innovation.
The survey of the community of practice obtained different results than the survey of the academic literature. A review of the use of modern technology in the pandemic through the academic literature reveals more discussion of cutting-edge technologies. These technologies mainly include artificial intelligence (AI), telemedicine, blockchain, 5G, Internet of Things (IoT), etc. (e.g., Alharbi & Abdur Rahman, 2021;Mbunge et al., 2021;Vaishya et al., 2021). And a survey of the community of practice revealed that more projects launched during the pandemic were applications. These programs are more likely to provide data information services, and the technology used is more mature.

Implications for practice
All the findings show that emerging technologies are widely used in the pandemic. The pandemic has received widespread attention from developers in the IT community of practice. Data is the cornerstone of the rapid application of emerging technologies. In the early stages of a pandemic, the first thing that relevant developers and scientists need to accomplish should be collecting, storing, and sharing various types of pandemic data. In addition, in the big data environment, we should consider transforming open data into open link data and analyzing the information, conducive to the rapid development and use of applications (Victorino et al. 2018). During the pandemic, the application of technology in the community of practice differs significantly from the application in the academic community. Researchers and developers from different disciplines should collaborate more, which will help knowledge transfer and facilitate the application of emerging technology (Teixeira et al., 2019).
The current GitHub community of developers submitting open-source projects is more likely to be a spontaneous act. Communities of practice should provide relevant services in future crises and organize community members to contribute their skills efficiently. Improving the quality of opensource projects in a pandemic requires organizational management by the community. In the late stages of the pandemic, communities of practice such as GitHub should organize pandemic-related projects more effectively to provide a large pool of pre-existing technologies for responding to possible future crises. For example, use hierarchical tags to manage related projects. The hierarchical topic structure can provide users with more comprehensive and precise topics (Zhang et al. 2019).
The research in this paper has some shortcomings. On the one hand, only relevant projects on GitHub are explored and analyzed. To understand the status of open-source projects during the pandemic, more open source communities may need to be mined and analyzed. On the other hand, it is difficult to understand the true intent of developers through the econometric analysis of projects submitted by developers. Future work may require questionnaires or interview methods to collect data to get a clearer picture of developer intent. In addition, future work could analyze maps of the community from multiple perspectives and provide a more fine-grained analysis of the use of technology. Also, we can consider the study of the collaboration between project development teams and explore the collaboration perspective between developers and scientists.