The evolution of public sentiment toward government management of emergencies: Social media analytics

At present, social media have become the main media of network public opinion (PO) dissemination. By analyzing the trend of emotional development in public emergencies, we can explore the evolution law of PO and identify potential risks, which provide decision support for the guidance and control of government management. First, based on the concept of critical points in the complex system, this study established a public sentiment (PS) evolution model under public emergencies and proposed an algorithm to identify the critical points in PS based on microblog data analysis. In addition, the BC-BIRCH algorithm was used to construct a topic clustering model for public emergencies, which improved the effect of topic discovery by merging multiple topic clusters. The evolution of public emergencies was analyzed by calculating the emotional heat value of different topic events. Finally, experimental results showed that the emotion of netizens' fluctuates greatly in the initial stage of PO under different themes. The method used in this paper achieved good results in topic clustering, critical point prediction, and PO evolution analysis of public emergencies. The main contribution of this paper is to analyze the evolution of the internal mechanism of PS and to identify and predict key nodes such as the outbreak and extinction of netizens' sentiment based on data-driven methods so as to provide the basis and support to the government and related media as the main body of prevention and control to respond in advance and guide in time

Cashtag piggybacking: uncovering spam and bot activity in stock microblogs on Twitter

Microblogs are increasingly exploited for predicting prices and traded volumes of stocks in financial markets. However, it has been demonstrated that much of the content shared in microblogging platforms is created and publicized by bots and spammers. Yet, the presence (or lack thereof) and the impact of fake stock microblogs has never systematically been investigated before. Here, we study 9M tweets related to stocks of the 5 main financial markets in the US. By comparing tweets with financial data from Google Finance, we highlight important characteristics of Twitter stock microblogs. More importantly, we uncover a malicious practice - referred to as cashtag piggybacking - perpetrated by coordinated groups of bots and likely aimed at promoting low-value stocks by exploiting the popularity of high-value ones. Among the findings of our study is that as much as 71% of the authors of suspicious financial tweets are classified as bots by a state-of-the-art spambot detection algorithm. Furthermore, 37% of them were suspended by Twitter a few months after our investigation. Our results call for the adoption of spam and bot detection techniques in all studies and applications that exploit user-generated content for predicting the stock market

Adopting microblogging solutions for interaction with government

Authorities in the People’s Republic of China communicate with citizens using an estimated 600,000 Sina Weibo microblogs. This study reports on a study of Chinese citizens’ adoption of microblogs to interact with the government. Adoption results from trust and peer pressure in smaller-network ties (densely knit, pervasive social networks surrounding individual citizens). Larger-network ties (trust in institutions at large, such as the Chinese Communist Party, executive organizations, the judicial system, the media, etc.) are not associated with the adoption of microblogging. Furthermore, higher levels of anxiety are correlated with lower levels of use intention, and this finding underlines the impact of the Chinese authority’s surveillance and control activities on the lives of individual Chinese citizens. Based on these findings, we outline a theory of why citizens use microblogs to interact with the government and suggest avenues for further research into microblogs, state–citizen communication patterns and technology adoption

Visual analytics of location-based social networks for decision support

Recent advances in technology have enabled people to add location information to social networks called Location-Based Social Networks (LBSNs) where people share their communication and whereabouts not only in their daily lives, but also during abnormal situations, such as crisis events. However, since the volume of the data exceeds the boundaries of human analytical capabilities, it is almost impossible to perform a straightforward qualitative analysis of the data. The emerging field of visual analytics has been introduced to tackle such challenges by integrating the approaches from statistical data analysis and human computer interaction into highly interactive visual environments. Based on the idea of visual analytics, this research contributes the techniques of knowledge discovery in social media data for providing comprehensive situational awareness. We extract valuable hidden information from the huge volume of unstructured social media data and model the extracted information for visualizing meaningful information along with user-centered interactive interfaces. We develop visual analytics techniques and systems for spatial decision support through coupling modeling of spatiotemporal social media data, with scalable and interactive visual environments. These systems allow analysts to detect and examine abnormal events within social media data by integrating automated analytical techniques and visual methods. We provide comprehensive analysis of public behavior response in disaster events through exploring and examining the spatial and temporal distribution of LBSNs. We also propose a trajectory-based visual analytics of LBSNs for anomalous human movement analysis during crises by incorporating a novel classification technique. Finally, we introduce a visual analytics approach for forecasting the overall flow of human crowds

SOCIAL MEDIA ANALYTICS − A UNIFYING DEFINITION, COMPREHENSIVE FRAMEWORK, AND ASSESSMENT OF ALGORITHMS FOR IDENTIFYING INFLUENCERS IN SOCIAL MEDIA

Given its relative infancy, there is a dearth of research on a comprehensive view of business social media analytics (SMA). This dissertation first examines current literature related to SMA and develops an integrated, unifying definition of business SMA, providing a nuanced starting point for future business SMA research. This dissertation identifies several benefits of business SMA, and elaborates on some of them, while presenting recent empirical evidence in support of foregoing observations. The dissertation also describes several challenges facing business SMA today, along with supporting evidence from the literature, some of which also offer mitigating solutions in particular contexts. The second part of this dissertation studies one SMA implication focusing on identifying social influencer. Growing social media usage, accompanied by explosive growth in SMA, has resulted in increasing interest in finding automated ways of discovering influencers in online social interactions. Beginning 2008, many variants of multiple basic approaches have been proposed. Yet, there is no comprehensive study investigating the relative efficacy of these methods in specific settings. This dissertation investigates and reports on the relative performance of multiple methods on Twitter datasets containing between them tens of thousands to hundreds of thousands of tweets. Accordingly, the second part of the dissertation helps further an understanding of business SMA and its many aspects, grounded in recent empirical work, and is a basis for further research and development. This dissertation provides a relatively comprehensive understanding of SMA and the implementation SMA in influencer identification