The world is witnessing an unpredictable COVID-19 pandemic that has impacted all levels of online education, shaping future trends. However, this shift was so sudden and drastic that unrevealed puzzles exist regarding the public’s authentic opinion towards online classes, even though three years have passed. Many experts and policymakers have conducted qualitative and quantitative research to explore effective pedagogies, the satisfaction of different stakeholders, and factors influential on learners’ performance. However, scant studies have examined personal opinions and concerns toward online classes hidden behind people’s anonymous posts on social media. This research investigates the sentiments, concerns, and their variance with time regarding online classes by learners and educators on Reddit, which is a dominant social network among them. Data were collected via the official API from identified relevant subreddits and keyword search results across Reddit. Sentiment analysis was applied to reveal their emotions and their changes. Topic modeling was conducted to discover the concerns hidden in the posts. The results revealed the concerns about online classes, such as severe cheating behaviors, and showed doubts about previous strategies to solve disadvantages in online classes. In addition, the results verified the habitual difference and motivations of social media usage between educators and learners.

With the evolution of China's market economy, the securities market is increasingly anchoring a pivotal role in the nation's economic landscape. Consequently, stock trend forecasting has garnered heightened attention among scholars and practitioners. This research pioneers the use of multimodal information to predict stock market fluctuations. Based on our experimental results, LSTM + Transformer performs better in handling multimodal data for stock movement prediction tasks regarding accuracy, F1-score, precision, and recall. Additionally, we employed the Granger causality test and Impulse response test to investigate the causal relationships between sentiment and stock trends, as well as the interplay between COVID-related indicators and stock trajectories. We identified discernible causal links between sentiments, COVID indicators, and stock trends for select pharmaceutical stocks. Our findings can provide valuable guidance for investors and market regulators, especially within the pharmaceutical industry. Understanding investor sentiment and the impact of the pandemic on severity can assist in effective stock commentary management and improve investment strategies.

This study analyzes 15,453 math topic-based Scratch projects of the online Scratch community to find the relations among K–12 math topics, usage of 13 programming elements, and project popularity. Among the six math topics, only statistics and calculus significantly contribute to projecting popularity. In addition, noncore programming elements, including sound and look, are widely adapted across various math topics and contribute significantly to project popularity. Although some studies discussed the integration of computation thinking (CT) and math in the classrooms, scant research focuses on the effect of math topics on CT element adoption for achieving better integration in the creative learning context. Moreover, few studies involve a large sample size for generalizable findings.

This research studies the motivational factors used in educational video games through the lens of 6 C’s learning motivation model with text mining of the players’ reviews and comments. This research seeks to offer insight for game producers and educational institutions to investigate the effectiveness of these motivators for increasing player motivations and thus improving the quality of learning. Sentiment analysis and LDA topic modeling were used to analyze reviews of five selected video games on the Steam platform. The 6 C’s Learning Motivation Model guided text mining to analyze the motivational factors used in the games and how they contribute to user learning. The effectiveness of these motivational factors was discussed in conjunction with categorized text mining. Results show that the major motivation factors of educational games are ‘construction meaning’, ‘challenge’, and ‘control’ in the 6 C’s learning motivation model. Among them, users focus on whether the game’s content meets their interests and the construction of the educational meaning of the game. The advantage of control, a high degree of motivational factor in video games, may turn out to be a factor that leads to user churning when the game is not interesting or attractive enough. Previous educational game research seldom involved a large sample size for generalizable findings. In addition, this research extends the application of the 6 C’s learning motivation model to the digital educational gaming arena, providing a novel player-centric perspective. Based on the results, we provide recommendations and design considerations for educational game developers to enhance players’ experience and motivations.

This study examines the evolving use of social software to combat online disinformation by investigating Weibo users’ attitudes toward IP location disclosure as a measure of transparency and trustworthiness. We analyzed 49,579 posts (April 2022 to May 2023) from Weibo users about IP location disclosure, categorized the topics using LDA topic modeling within the frameworks of communication privacy management, the networked public sphere, and digital democracy, and conducted sentiment analysis. We constructed separate semantic networks for positive and negative terms to examine co-occurrence patterns. The results show that Weibo users are generally negative about this policy, as IP location may reveal personally identifiable information about individuals involved in discussions of online social/political events. Mandatory transparency, while intended to enhance accountability, functions as a mandatory visibility regime that reshapes privacy boundaries and undermines inclusive deliberation. The findings contribute to the exploration of the impact of government-mandatory information privacy disclosure policies on the implementation of platform functionality, as well as changes in user sentiment, information behavior, and components of social media discourse.