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The genome of the novel coronavirus (COVID-19) disease was first sequenced in January 2020, approximately a month after its emergence in Wuhan, capital of Hubei province, China. COVID-19 genome sequencing is critical to understanding the virus behavior, its origin, how fast it mutates, and for the development of drugs/vaccines and effective preventive strategies. This paper investigates the use of artificial intelligence techniques to learn interesting information from COVID-19 genome sequences. Sequential pattern mining (SPM) is first applied on a computer-understandable corpus of COVID-19 genome sequences to see if interesting hidden patterns can be found, which reveal frequent patterns of nucleotide bases and their relationships with each other. Second, sequence prediction models are applied to the corpus to evaluate if nucleotide base(s) can be predicted from previous ones. Third, for mutation analysis in genome sequences, an algorithm is designed to find the locations in the genome sequences where the nucleotide bases are changed and to calculate the mutation rate. Obtained results suggest that SPM and mutation analysis techniques can reveal interesting information and patterns in COVID-19 genome sequences to examine the evolution and variations in COVID-19 strains respectively.

Purpose The novel Coronavirus (COVID-19) pandemic, which started in late December 2019, has spread to more than 200 countries. As no vaccine is yet available for this pandemic, government and health agencies are taking draconian steps to contain it. This pandemic is also trending on social media, particularly on Twitter. The purpose of this study is to explore and analyze the general public reactions to the COVID-19 outbreak on Twitter. Design/methodology/approach This study conducts a thematic analysis of COVID-19 tweets through VOSviewer to examine people’s reactions related to the COVID-19 outbreak in the world. Moreover, sequential pattern mining (SPM) techniques are used to find frequent words/patterns and their relationship in tweets. Findings Seven clusters (themes) were found through VOSviewer: Cluster 1 (green): public sentiments about COVID-19 in the USA. Cluster 2 (red): public sentiments about COVID-19 in Italy and Iran and a vaccine, Cluster 3 (purple): public sentiments about doomsday and science credibility. Cluster 4 (blue): public sentiments about COVID-19 in India. Cluster 5 (yellow): public sentiments about COVID-19’s emergence. Cluster 6 (light blue): public sentiments about COVID-19 in the Philippines. Cluster 7 (orange): Public sentiments about COVID-19 US Intelligence Report. The most frequent words/patterns discovered with SPM were “COVID-19,” “Coronavirus,” “Chinese virus” and the most frequent and high confidence sequential rules were related to “Coronavirus, testing, lockdown, China and Wuhan.” Research limitations/implications The methodology can be used to analyze the opinions/thoughts of the general public on Twitter and to categorize them accordingly. Moreover, the categories (generated by VOSviewer) can be correlated with the results obtained with pattern mining techniques. Social implications This study has a significant socio-economic impact as Twitter offers content posting and sharing to billions of users worldwide. Originality/value According to the authors’ best knowledge, this may be the first study to carry out a thematic analysis of COVID-19 tweets at a glance and mining the tweets with SPM to investigate how people reacted to the COVID-19 outbreak on Twitter.

The purpose of this study is to conduct a bibliometric analysis to examine the most influential journals, institutions, and countries in social media (SM) publications related to knowledge management (KM). Moreover, various research themes in SM KM publications are also explored. VOSviewer was employed to process 234 SM KM publications retrieved from Web of Science (WoS) in the time period 2009-2019. Different methodologies were used according to the nature of bibliometric analysis and explained in each section. Journal of Knowledge Management was the most influential journal in SM KM publications. USA and England ranked first and second respectively, while the Tampere University of Technology was the most productive institute in SM KM research. Four emerged themes indicated an explicit contribution of SM users in KM through big data, knowledge sharing, innovation, Enterprise 2.0, and social capital. This is the first bibliometric study that explores the overall contribution of SM publications in the KM field.

Social online marketing is expanding fast with the evolution and recent development in the information and communication technology. Investigating how companies are exploiting social media for marketing, advertisement, and consumer's engagement is gaining more and more interest. In this paper, brands/companies data on Twitter is collected and analyzed to compute the overall company response on Twitter. Responsiveness of a company is inferred from three features: company popularity, average company replies, and average followers' replies. Twitter network features are used in calculating the posting frequency for companies and their followers. It is shown that the proposed approach can be used in finding the responsiveness of companies and their followers. Furthermore, useful links for a brand consumer is extracted and the posting behavior of brands and their followers is determined with the help of Twitter network features, such as retweet count and geolocation. This paper contributes to the literature on how Twitter data and its network structure features can be exploited in finding the responsiveness and posting behavior of companies and their followers. We believe that this approach can be used effectively in developing prediction and information-filtering systems, particularly the personalized-recommendation systems.

Purpose Twitter users’ generated data, known as tweets, are now not only used for communication and opinion sharing, but they are considered an important source of trendsetting, future prediction, recommendation systems and marketing. Using network features in tweet modeling and applying data mining and deep learning techniques on tweets is gaining more and more interest. Design/methodology/approach In this paper, user interests are discovered from Twitter Trends using a modeling approach that uses network-based text data (tweets). First, the popular trends are collected and stored in separate documents. These data are then pre-processed, followed by their labeling in respective categories. Data are then modeled and user interest for each Trending topic is calculated by considering positive tweets in that trend, average retweet and favorite count. Findings The proposed approach can be used to infer users’ topics of interest on Twitter and to categorize them. Support vector machine can be used for training and validation purposes. Positive tweets can be further analyzed to find user posting patterns. There is a positive correlation between tweets and Google data. Practical implications The results can be used in the development of information filtering and prediction systems, especially in personalized recommendation systems. Social implications Twitter microblogging platform offers content posting and sharing to billions of internet users worldwide. Therefore, this work has significant socioeconomic impacts. Originality/value This study guides on how Twitter network structure features can be exploited in discovering user interests using tweets. Further, positive correlation of Twitter Trends with Google Trends is reported, which validates the correctness of the authors’ approach.

Social media has become a platform of first choice where one can express his/her feelings with freedom. The sports and matches being played are also discussed on social media such as Twitter. In this article, efforts are made to investigate the feasibility of using collective knowledge obtained from microposts posted on Twitter to predict the winner of a Cricket match. For predictions, we use three different methods that depend on the total number of tweets before the game for each team, fans sentiments toward each team and fans score predictions on Twitter. By combining these three methods, we classify winning team prediction in a Cricket game before the start of game. Our results are promising enough to be used for winning team forecast. Furthermore, the effectiveness of supervised learning algorithms is evaluated where Support Vector Machine (SVM) has shown advantage over other classifiers.

Search engines and social networks are two entirely different data sources that can provide valuable information about Influenza. While search engine hosts can deliver popular queries (or terms) used for searching the Influenza related information, the social networks contain useful links of information sources that people have found valuable. The authors hypothesize that such data sources can provide vital first-hand information. In this article, they have proposed a methodology for detecting the information sources from social networks, particularly Twitter. The data filtering and source finding tasks are posed as classification tasks. Search engine queries are used for extracting related dataset. Results have shown that propose approach can be beneficial for extracting useful information regarding side effects, medications and to track geographical location of epidemics affected area.

Genetic Algorithms (GA) are bio-inspired algorithms that are now used in safety critical systems, robotics, artificial intelligence and bioinformatics, etc. A formal semantic model for GA is needed that can provide a framework for the modeling, reasoning and verification of GA based systems. On the other hand, Unifying Theories of Programming (UTP) offers formal semantic foundations for programming as well as specification languages. UTP is used in this paper to formalize a family of crossover and mutation operators of GA, where operators are defined as design models. UTP design models also allow the establishment of refinement and equivalence relations between GA operators by introducing implication between predicates. As an example, the equivalence relation between operators is proved with the PVS proof assistant.

Cloud computing enables one to access applications through Internet, without explicitly installing them on one single computing device. Formal modeling and verification of cloud services and their composition is fundamental to guarantee the accurate working and availability of cloud applications. In this paper, we use the Prototype Verification System (PVS) for formal modeling and verification of cloud services. Cloud services are modeled in PVS with the combination of logical predicates that represent the relations among the timed data sequences being observed at its input and output ports. Furthermore, composition operators are modeled that can be used to combine different cloud services together to construct more complex services. With our approach, properties of cloud services and refinement relations between different cloud services can be formalized as goals and proved with the PVS proof assistant.

Search engines and social media are two different online data sources where search engines can provide health related queries logs and Internet users' discuss their diseases, symptoms, causes, preventions and even suggest treatment by sharing their views, experiences and opinions on social media. This chapter hypothesizes that online data from Google and Twitter can provide vital first-hand healthcare information. An approach is provided for collecting twitter data by exploring contextual information gleaned from Google search queries logs. Furthermore, it is investigated that whether it is possible to use tweets to track, monitor and predict diseases, especially Influenza epidemics. Obtained results show that healthcare institutes and professional's uses social media to provide up-to date health related information and interact with public. Moreover, proposed approach is beneficial for extracting useful information regarding disease symptoms, side effects, medications and to track geographical location of epidemics affected area.