Researchers have collected Twitter data to study a wide range of topics. This growing body of literature, however, has not yet been reviewed systematically to synthesize Twitter-related papers. The existing literature review papers have been limited by constraints of traditional methods to manually select and analyze samples of topically related papers. The goals of this retrospective study are to identify dominant topics of Twitter-based research, summarize the temporal trend of topics, and interpret the evolution of topics withing the last ten years. This study systematically mines a large number of Twitter-based studies to characterize the relevant literature by an efficient and effective approach. This study collected relevant papers from three databases and applied text mining and trend analysis to detect semantic patterns and explore the yearly development of research themes across a decade.We found 38 topics in more than 18,000 manuscripts published between 2006 and 2019. By quantifying temporal trends, this study found that while 23.7% of topics did not show a significant trend (P > 0:05), 21% of topics had increasing trends and 55.3% of topics had decreasing trends that these hot and cold topics represent three categories: application, methodology, and technology. The contributions of this paper can be utilized in the growing field of Twitterbased research and are beneficial to researchers, educators, and publishers.

In recent years, we have been faced with a series of natural disasters causing a tremendous amount of financial, environmental and human losses. The unpredictable nature of natural disasters behaviour makes it hard to have a comprehensive situational awareness (SA) to support disaster management. Using opinion surveys is a traditional approach to analyse public concerns during natural disasters; however, this approach is limited, expensive and time-consuming. Luckily, the advent of social media has provided scholars with an alternative means of analysing public concerns. Social media enable users (people) to freely communicate their opinions and disperse information regarding current events including natural disasters. This research emphasises the value of social media analysis and proposes an analytical framework: Twitter Situational Awareness (TwiSA). This framework uses text mining methods including sentiment analysis and topic modelling to create a better SA for disaster preparedness, response and recovery. TwiSA has also effectively deployed on a large number of tweets and tracks the negative concerns of people during the 2015 South Carolina flood.

The understanding of the public response to COVID-19 vaccines is the key success factor to control the COVID-19 pandemic. To understand the public response, there is a need to explore public opinion. Traditional surveys are expensive and time-consuming, address limited health topics, and obtain small-scale data. Twitter can provide a great opportunity to understand public opinion regarding COVID-19 vaccines. The current study proposes an approach using computational and human coding methods to collect and analyze a large number of tweets to provide a wider perspective on the COVID-19 vaccine. This study identifies the sentiment of tweets using a machine learning rule-based approach, discovers major topics, explores temporal trend and compares topics of negative and non-negative tweets using statistical tests, and discloses top topics of tweets having negative and non-negative sentiment. Our findings show that the negative sentiment regarding the COVID-19 vaccine had a decreasing trend between November 2020 and February 2021. We found Twitter users have discussed a wide range of topics from vaccination sites to the 2020 U.S. election between November 2020 and February 2021. The findings show that there was a significant difference between tweets having negative and non-negative sentiment regarding the weight of most topics. Our results also indicate that the negative and non-negative tweets had different topic priorities and focuses. This research illustrates that Twitter data can be used to explore public opinion regarding the COVID-19 vaccine.

Using big data has been a prevailing research trend in various academic fields. However, no studies have explored the scope and structure of big data across disciplines. In this article, we applied topic modeling and word co-occurrence analysis methods to identify key topics from more than 36,000 big data publications across all academic disciplines between 2012 and 2017. The results revealed several topics associated with the storage, collection and analysis of large datasets; the publications were predominantly published in computational fields. Other identified research topics show the influence of big data methods and techniques in areas beyond computer science, such as education, urban informatics, business, health and medical sciences. In fact, the prevalence of these topics has increased over time. In contrast, some themes like parallel computing, network modeling and big data analytic techniques have lost their popularity in recent years. These results probably reflect the maturity of big data core topics and highlight flourishing new research trends pertinent to big data in new domains, especially in social sciences, health and medicine. Findings of this article can be beneficial for researchers and science policymakers to understand the scope and structure of big data in different academic disciplines.