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

Stock Prediction Analyzing Investor Sentiments

We are going through a phase of data evolution where a major portion of the data from our daily lives is now been stored on social media platforms. In recent years, social media has become ubiquitous and important for social networking and content sharing. Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. In the financial sector, sentiments are also of paramount importance, and this dissertation mainly focuses on the effect of sentiments from investors [3] on the behavior of stocks. The dissertation work leverages social data from Twitter and seeks the sentiment of certain investors. Once the sentiment of the tweets is calculated using an advanced sentiment analyzer, it is used as an additional attribute to the other fundamental properties of the stock. This dissertation demonstrates how incorporating the sentiments improves forecasting accuracy of predicting stock valuation. In addition, various experimental analysis on regression based statistical models are considered which show statistical measures to consider for effectively predicting the closing price of the stock. The Efficient Market Hypothesis (EMH) states that stock market prices are largely driven by additional information and follow a random walk pattern [7, 8, 37, 39, 41]. Though this hypothesis is widely accepted by the research community as a central paradigm governing the markets in general, several people have attempted to extract patterns in the way stock markets behave and respond to external stimuli. We test a hypothesis based on the premise of behavioral economics, that the emotions and moods of individuals basically the sentiments affect their decision-making process, thus, leading to a direct correlation between ?public sentiment? and ?market sentiment? [42, 43, 44, 45]. We first select key investors from Twitter [27, 28] whose sentiments matter and do sentiment analysis on the tweets pertaining to stock related information. Once we retrieve the sentiment for every stock, we combine this information with the other fundamental information about stocks and build different regression-based prediction models to predict their closing price

Community-Based Behavioral Understanding of Mobility Trends and Public Attitude through Transportation User and Agency Interactions on Social Media in the Emergence of Covid-19

The increased availability of technology-enabled transportation options and modern communication devices (smartphones, in particular) is transforming travel-related decision-making in the population differently at different places, points in time, modes of transportation, and socio-economic groups. The emergence of COVID-19 made the dynamics of passenger travel behavior more complex, forcing a worldwide, unparalleled change in human travel behavior and introducing a new normal into their existence. This dissertation explores the potential of social media platforms (SMPs) as a viable alternative to traditional approaches (e.g., travel surveys) to understand the complex dynamics of people’s mobility patterns in the emergence of COVID-19. In this dissertation, we focus on three objectives. First, a novel approach to developing comparative infographics of emerging transportation trends is introduced by natural language processing and data-driven techniques using large-scale social media data. Second, a methodology has been developed to model community-based travel behavior under different socioeconomic and demographic factors at the community level in the emergence of COVID-19 on Twitter, inferring users’ demographics to overcome sampling bias. Third, the communication patterns of different transportation agencies on Twitter regarding message kinds, communication sufficiency, consistency, and coordination were examined by applying text mining techniques and dynamic network analysis. The methodologies and findings of the dissertation will allow real-time monitoring of transportation trends by agencies, researchers, and professionals. Potential applications of the work may include: (1) identifying spatial diversity of public mobility needs and concerns through social media platforms; (2) developing new policies that would satisfy the diverse needs at different locations; (3) introducing new plans to support and celebrate equity, diversity, and inclusion in the transportation sector that would improve the efficient flow of goods and services; (4) designing new methods to model community-based travel behavior at different scales (e.g., census block, zip code, etc.) using social media data inferring users’ socio-economic and demographic properties; and (5) implementing efficient policies to improve existing communication plans, critical information dissemination efficacy, and coordination of different transportation actors to raise awareness among passengers in general and during unprecedented health crises in the fragmented communication world