Doctor of Philosophy

dissertationDue to the popularity of Web 2.0 and Social Media in the last decade, the percolation of user generated content (UGC) has rapidly increased. In the financial realm, this results in the emergence of virtual investing communities (VIC) to the investing public. There is an on-going debate among scholars and practitioners on whether such UGC contain valuable investing information or mainly noise. I investigate two major studies in my dissertation. First I examine the relationship between peer influence and information quality in the context of individual characteristics in stock microblogging. Surprisingly, I discover that the set of individual characteristics that relate to peer influence is not synonymous with those that relate to high information quality. In relating to information quality, influentials who are frequently mentioned by peers due to their name value are likely to possess higher information quality while those who are better at diffusing information via retweets are likely to associate with lower information quality. Second I propose a study to explore predictability of stock microblog dimensions and features over stock price directional movements using data mining classification techniques. I find that author-ticker-day dimension produces the highest predictive accuracy inferring that this dimension is able to capture both relevant author and ticker information as compared to author-day and ticker-day. In addition to these two studies, I also explore two topics: network structure of co-tweeted tickers and sentiment annotation via crowdsourcing. I do this in order to understand and uncover new features as well as new outcome indicators with the objective of improving predictive accuracy of the classification or saliency of the explanatory models. My dissertation work extends the frontier in understanding the relationship between financial UGC, specifically stock microblogging with relevant phenomena as well as predictive outcomes

Does social network sentiment influence S&P 500 environmental & socially responsible index?

The influence of social network sentiment on stock market indices and companies has been proven in several studies. However, the influence of social network sentiment on sustainability indices and sustainable companies has not been analyzed so far. Therefore, this study analyzed the influence of social network sentiment on sustainability indices (S&P 500 Environmental & Socially Responsible Index) and focused on variations of this influence on sustainable and non-sustainable companies, namely, in companies included in the Information Technology sector. To this end, two methodologies were used: GARCH (1,1) models and logit-probit models. The results showed that social network sentiment influences S&P 500 Environmental & Socially Responsible Index’s volatility; this influence was greater than the influence of social network sentiment when considering the S&P 500 Index. Additionally, the results showed that social network sentiment influences sustainable companies’ returns but had no effect on unsustainable companies’ returns. These results highlighted the importance of managing the companies’ profiles in social networks and their corporate image in general, because investors will consider these aspects to design their investment strategiesS

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

AI approaches to understand human deceptions, perceptions, and perspectives in social media

Social media platforms have created virtual space for sharing user generated information, connecting, and interacting among users. However, there are research and societal challenges: 1) The users are generating and sharing the disinformation 2) It is difficult to understand citizens\u27 perceptions or opinions expressed on wide variety of topics; and 3) There are overloaded information and echo chamber problems without overall understanding of the different perspectives taken by different people or groups. This dissertation addresses these three research challenges with advanced AI and Machine Learning approaches. To address the fake news, as deceptions on the facts, this dissertation presents Machine Learning approaches for fake news detection models, and a hybrid method for topic identification, whether they are fake or real. To understand the user\u27s perceptions or attitude toward some topics, this study analyzes the sentiments expressed in social media text. The sentiment analysis of posts can be used as an indicator to measure how topics are perceived by the users and how their perceptions as a whole can affect decision makers in government and industry, especially during the COVID-19 pandemic. It is difficult to measure the public perception of government policies issued during the pandemic. The citizen responses to the government policies are diverse, ranging from security or goodwill to confusion, fear, or anger. This dissertation provides a near real-time approach to track and monitor public reactions toward government policies by continuously collecting and analyzing Twitter posts about the COVID-19 pandemic. To address the social media\u27s overwhelming number of posts, content echo-chamber, and information isolation issue, this dissertation provides a multiple view-based summarization framework where the same contents can be summarized according to different perspectives. This framework includes components of choosing the perspectives, and advanced text summarization approaches. The proposed approaches in this dissertation are demonstrated with a prototype system to continuously collect Twitter data about COVID-19 government health policies and provide analysis of citizen concerns toward the policies, and the data is analyzed for fake news detection and for generating multiple-view summaries

Re-examining the Indirect Myth of Chinese Rhetoric on Social Media

It has long been asserted that a major difference between Chinese rhetoric and Western rhetoric lies in the preference for rhetorical strategies. The Chinese are believed to prefer indirect approaches in communication whereas the Americans tend to be more direct. This perception has been widely discussed and accepted by scholars and practitioners of inter-cultural communication. However, as globalization and technology are bringing about substantial changes to our communication experience, it is necessary to reexamine the “indirect myth” of Chinese rhetoric in a contemporary context characterized by the ubiquitous use of social media. In this dissertation, I strive to answer two major research questions about the use of rhetorical strategies on social media. The two questions are as follows: 1) Generally speaking, does a preference for directness or indirectness exist in Weibo postings? If not, are the two strategies equally prevalent on Weibo? 2) What direct strategies are most preferred by Chinese Weibo users? I employ a content analysis based on quantitative data and rhetorical analysis. I collected 25,316 pieces of authentic Weibo postings and had coders categorize the postings according to a coding scheme of nine rhetorical strategies with each strategy divided into directness and indirectness. The research findings show that Weibo users have an overall preference for directness although users with academic backgrounds tend to use indirect strategies more often. “To argue or to comment” is the most frequently used rhetorical strategy, and direct comment or argument with opinion proceeding evidence is the most used direct strategy. I argue that the anonymity and word limit are two major factors affecting users’ preferences for direct rhetorical strategies. I also argue that the low level of user participation indicated by the overly simplistic and lopsided preference of directness mean that Weibo is primarily disseminating information but has not fully evolved to a public sphere

Localized Events in Social Media Streams: Detection, Tracking, and Recommendation

From the recent proliferation of social media channels to the immense amount of user-generated content, an increasing interest in social media mining is currently being witnessed. Messages continuously posted via these channels report a broad range of topics from daily life to global and local events. As a consequence, this has opened new opportunities for mining event information crucial in many application domains, especially in increasing the situational awareness in critical scenarios. Interestingly, many of these messages are enriched with location information, due to the wide- spread of mobile devices and the recent advancements of today’s location acquisition techniques. This enables location-aware event mining, i.e., the detection and tracking of localized events. In this thesis, we propose novel frameworks and models that digest social media content for localized event detection, tracking, and recommendation. We first develop KeyPicker, a framework to extract and score event-related keywords in an online fashion, accounting for high levels of noise, temporal heterogeneity and outliers in the data. Then, LocEvent is proposed to incrementally detect and track events using a 4-stage procedure. That is, LocEvent receives the keywords extracted by KeyPicker, identifies local keywords, spatially clusters them, and finally scores the generated clusters. For each detected event, a set of descriptive keywords, a location, and a time interval are estimated at a fine-grained resolution. In addition to the sparsity of geo-tagged messages, people sometimes post about events far away from an event’s location. Such spatial problems are handled by novel spatial regularization techniques, namely, graph- and gazetteer-based regularization. To ensure scalability, we utilize a hierarchical spatial index in addition to a multi-stage filtering procedure that gradually suppresses noisy words and considers only event-related ones for complex spatial computations. As for recommendation applications, we propose an event recommender system built upon model-based collaborative filtering. Our model is able to suggest events to users, taking into account a number of contextual features including the social links between users, the topical similarities of events, and the spatio-temporal proximity between users and events. To realize this model, we employ and adapt matrix factorization, which allows for uncovering latent user-event patterns. Our proposed features contribute to directing the learning process towards recommendations that better suit the taste of users, in particular when new users have very sparse (or even no) event attendance history. To evaluate the effectiveness and efficiency of our proposed approaches, extensive comparative experiments are conducted using datasets collected from social media channels. Our analysis of the experimental results reveals the superiority and advantages of our frameworks over existing methods in terms of the relevancy and precision of the obtained results

A Quantitative Investigation into the Design Trade-offs in Decision Support Systems

Users frequently make decisions about which information systems they incorporate into their information analysis and they abandon tools that they perceive as untrustworthy or ineffective. Decision support systems - automated agents that provide complex algorithms - are often effective but simultaneously opaque; meanwhile, simple tools are transparent and predictable but limited in their usefulness. Tool creators have responded by increasing transparency (via explanation) and customizability (via control parameters) of complex algorithms or by improving the effectiveness of simple algorithms (such as adding personalization to keyword search). Unfortunately, requiring user input or attention requires cognitive bandwidth, which could hurt performance in time-sensitive operations. Simultaneously, improving the performance of algorithms typically makes the underlying computations more complex, reducing predictability, increasing potential mistrust, and sometimes resulting in user performance degradation. Ideally, software engineers could create systems that accommodate human cognition, however, not all of the factors that affect decision making in human-agent interaction (HAI) are known. In this work, we conduct a quantitative investigation into the role of human insight, awareness of system operations, cognitive load, and trust in the context of decision support systems. We conduct several experiments with different task parameters that shed light on the relationship between human cognition and the availability of system explanation/control under varying degrees of algorithm error. Human decision making behavior is quantified in terms of which information tools are used, which information is incorporated, and domain decision success. The measurement of intermediate cognitive variables allows for the testing of mediation effects, which facilitates the explanation of effects related to system explanation, control, and error. Key findings are 1) a simple, reliable, domain independent profiling test can predict human decision behavior in the HAI context, 2) correct user beliefs about information systems mediate the effects of system explanations to predict adherence to advice, and 3) explanations from and control over complex algorithms increase trust, satisfaction, interaction, and adherence, but they also cause humans to form incorrect beliefs about data

Social media mining under the COVID-19 context: Progress, challenges, and opportunities

Social media platforms allow users worldwide to create and share information, forging vast sensing networks that allow information on certain topics to be collected, stored, mined, and analyzed in a rapid manner. During the COVID-19 pandemic, extensive social media mining efforts have been undertaken to tackle COVID-19 challenges from various perspectives. This review summarizes the progress of social media data mining studies in the COVID-19 contexts and categorizes them into six major domains, including early warning and detection, human mobility monitoring, communication and information conveying, public attitudes and emotions, infodemic and misinformation, and hatred and violence. We further document essential features of publicly available COVID-19 related social media data archives that will benefit research communities in conducting replicable and repro�ducible studies. In addition, we discuss seven challenges in social media analytics associated with their potential impacts on derived COVID-19 findings, followed by our visions for the possible paths forward in regard to social media-based COVID-19 investigations. This review serves as a valuable reference that recaps social media mining efforts in COVID-19 related studies and provides future directions along which the information harnessed from social media can be used to address public health emergencies

Spatial and Temporal Sentiment Analysis of Twitter data

The public have used Twitter world wide for expressing opinions. This study focuses on spatio-temporal variation of georeferenced Tweets’ sentiment polarity, with a view to understanding how opinions evolve on Twitter over space and time and across communities of users. More specifically, the question this study tested is whether sentiment polarity on Twitter exhibits specific time-location patterns. The aim of the study is to investigate the spatial and temporal distribution of georeferenced Twitter sentiment polarity within the area of 1 km buffer around the Curtin Bentley campus boundary in Perth, Western Australia. Tweets posted in campus were assigned into six spatial zones and four time zones. A sentiment analysis was then conducted for each zone using the sentiment analyser tool in the Starlight Visual Information System software. The Feature Manipulation Engine was employed to convert non-spatial files into spatial and temporal feature class. The spatial and temporal distribution of Twitter sentiment polarity patterns over space and time was mapped using Geographic Information Systems (GIS). Some interesting results were identified. For example, the highest percentage of positive Tweets occurred in the social science area, while science and engineering and dormitory areas had the highest percentage of negative postings. The number of negative Tweets increases in the library and science and engineering areas as the end of the semester approaches, reaching a peak around an exam period, while the percentage of negative Tweets drops at the end of the semester in the entertainment and sport and dormitory area. This study will provide some insights into understanding students and staff ’s sentiment variation on Twitter, which could be useful for university teaching and learning management