Search

The prevalence of cybercrime has emerged as a critical issue in contemporary society because of its far-reaching financial, social, and psychological implications. The negative effects of cyber-attacks extend beyond financial losses and disrupt people's lives on social and psychological levels. Conventional practice involves cyber experts sourcing data from various outlets and applying personal discernment and rational inference to manually formulate cyber intelligence specific to a country. This traditional approach introduces personal bias towards the country-level cyber reports. However, this paper reports a novel approach where country-level cyber intelligence is automatically generated with artificial intelligence (AI), employing cyber-related social media posts and open-source cyber-attack statistics. Our innovative cyber threat intelligence solution examined 37,386 tweets from 30,706 users in 54 languages using sentiment analysis, translation, term frequency–inverse document frequency (TF-IDF), latent Dirichlet allocation (LDA), N-gram, and Porter stemming. Moreover, the presented study utilized 238,220 open-intelligence cyber-attack statistics from eight different web links, to create a historical cyber-attack dataset. Subsequently, AI-based algorithms, like convolutional neural network (CNN), and exponential smoothing were used for AI-driven insights. With the confluence of the voluminous Twitter-derived data and the array of open-intelligence cyber-attack statistics, orchestrated by the AI-driven algorithms, the presented approach generated seven-dimensional cyber intelligence for Australia and China in complete automation. Finally, the topic analysis on the cyber-related social media messages revealed seven main themes for both Australia and China. This methodology possesses the inherent capability to effortlessly engender cyber intelligence for any country, employing an autonomous modality within the realm of pervasive computational platforms. [ABSTRACT FROM AUTHOR]

The popularity of online contents in social media frequently experiences ebb and flow, and thus its evolution often involves different stages, such as burst and valley. Exploring the patterns of popularity evolution, especially how burst forms and decays, and even further, predicting the trends of popularity evolution is both an important research topic and beneficial to support decision making for many applications, such as emergency management, business intelligence, and public security. Previous work on popularity prediction has focused on predicting the popularity volume of online contents, and at most, popularity burst and ignored the exploration of popularity evolution and the prediction of its stages. To fill this gap, in this paper, we propose our method for the popularity stage prediction problem both at the microscopic level and macroscopic level. At the microscopic level, we first extract multiple dynamic factors and infer future evolution stage by considering the contributions of different dynamic factors. At the macroscopic level, we extract the overall evolution patterns of popularity stages and adopt a pattern matching-based method to predict future popularity stages. We evaluate the proposed approach using tweets in SinaWeibo, the most popular Twitter-like social media platform in China. The experimental results show the effectiveness of our proposed approach in predicting popularity evolution stages. [ABSTRACT FROM AUTHOR]

Environmental and social challenges are intensifying globally. Then along comes more demands to deliver successful environmental, social, and governance (ESG) outcomes. Understanding public perceptions of ESG is paramount to gaining public support, advancing ESG actions, and creating a sustainable ESG policy system. This study aims to explore the general public's perceptions of ESG. The setting is China, where ESG is in its infancy and ESG policies are incomplete. Given the richness of social media data, a social media analytics (SMA) framework is applied to obtain public perceptions from Sina Weibo, the largest Chinese social media platform. The SMA framework integrates descriptive analysis, topic modeling, and sentiment analysis to investigate the characteristics of users concerned about ESG, discover latent ESG-based topics from posts and examine corresponding attention, as well as identify public sentiment orientation and opinions on ESG from their comments. The results indicate that there are regional and industry-specific disparities in ESG-conscious users. The latent topics often discussed in posts include ESG investment, ESG disclosure, ESG rating, and ESG notion and practice. It is observed that the increasing number of Weibo posts has not received the corresponding growing attention. The comments containing positive, neutral, and negative sentiments account for 66.03%, 17.37%, and 16.60%, respectively. The negative sentiments might result from the greenwashing effect, inadequate knowledge of ESG, inconsistency among ESG ratings, and lack of transparency in rating methods. The findings can aid policymakers and corporations in proactively comprehending public needs and providing guidance for effective ESG communication on social media and policy improvement. [Display omitted] • A social media analytics (SMA) framework is applied to obtain Chinese public perceptions of ESG from Sina Weibo, the largest Chinese social media platform. • There exist region and industry differences in users concerned about ESG. • The latent topics often discussed in Weibo posts include ESG investment, ESG disclosure, ESG rating, and ESG notion and practice. • The increasing Weibo posts have not received the corresponding growing public attention. • Greenwashing, inadequate knowledge, inconsistent ratings, and opaque rating methods might cause negative comments on ESG. [ABSTRACT FROM AUTHOR]

• Apply social media analytics to measure co-visitation linkages. • Reveal structure and nodal functions of co-visitation network. • Introduce a trend of tourism-driven urbanization in China's peri-urban area. • Propose implications of planning practices based on co-visitation patterns. The peri -urban area has undergone tremendous changes in various aspects. Tourism-driven development in this area, echoing the longing of urban residents for recreational and leisure space, provides an alternative to conventional urbanization approaches. Particularly, in view of heightened conflicts and fierce competitions that often occur in the area, developing the knowledge of the co-visitation network, or how different attractions and villages interact with each other from the visitors' perspective, is key to more informed planning strategies. With the rapid development of online social media platforms and mobile devices, researchers can collect massive real-time data on the basis of online user-generated content. While there are already many attempts on its application on tourism design, few studies to date have explored how peri -urban attractions and villages are organized in same trips. In this paper, we design a new methodology on the basis of social media analytics to construct and map a co-visitation network, and apply NetMiner-enabled social network analysis to explain its structure and nodal functions. The usefulness of this methodology is demonstrated through a detailed case study in Shenzhen, China. Research findings reveal types of existing co-visitation patterns, which help planners predict future trends, design new experiences and take specific measures in line with visitor demands. [ABSTRACT FROM AUTHOR]