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

Twitter Activity Of Urban And Rural Colleges: A Sentiment Analysis Using The Dialogic Loop

The purpose of the present study is to ascertain if colleges are achieving their ultimate communication goals of maintaining and attracting students through their microblogging activity, which according to Dialogic Loop Theory, is directly correlated to the use of positive and negative sentiment. The study focused on a cross-section of urban and rural community colleges within the United States to identify the sentiment score of their microblogging activity. The study included a content analysis on the Twitter activity of these colleges. A data-mining process was employed to collect a census of the tweets associated with these colleges. Further processing was then applied using data linguistic software that removed all irrelevant text, word abbreviations, emoticons, and other Twitter specific classifiers. The resulting data set was then processed through a Multinomial Naive Bayes Classifier, which refers to a probability of word counts in a text. The classifier was trained using a data source of 1.5 million tweets, called Sentiment140, that qualitatively analyzed the corpus of these tweets, labeling them as positive and negative sentiment. The Multinomial Naive Bayes Classifier distinguished specific wording and phrases from the corpus, comparing the data to a specific database of sentiment word identifiers. The sentiment analysis process categorized the text as being positive or negative. Finally, statistical analysis was conducted on the outcome of the sentiment analysis. A significant contribution of the current work was extending Kent and Taylor\u27s (1998) Dialogic Loop Theory, which was designed specifically for identifying the relationship building capabilities of a Web site, to encompass the microblogging concept used in Twitter. Specifically, Dialogic Loop Theory is applied and enhanced to develop a model for social media communication to augment relationship building capabilities, which the current study established as a new form for evaluating Twitter tweets, labeled in the current body of work as Microblog Dialogic Communication. The implication is that by using Microblog Dialogic Communication, a college can address and correct their microblogging sentiment. The results of the data collected found that rural colleges tweeted more positive sentiment tweets and less negative sentiment tweets when compared to the urban colleges tweets

Domain-based user embedding for competing events on social media

Online social networks offer vast opportunities for computational social science, but effective user embedding is crucial for downstream tasks. Traditionally, researchers have used pre-defined network-based user features, such as degree, and centrality measures, and/or content-based features, such as posts and reposts. However, these measures may not capture the complex characteristics of social media users. In this study, we propose a user embedding method based on the URL domain co-occurrence network, which is simple but effective for representing social media users in competing events. We assessed the performance of this method in binary classification tasks using benchmark datasets that included Twitter users related to COVID-19 infodemic topics (QAnon, Biden, Ivermectin). Our results revealed that user embeddings generated directly from the retweet network, and those based on language, performed below expectations. In contrast, our domain-based embeddings outperformed these methods while reducing computation time. These findings suggest that the domain-based user embedding can serve as an effective tool to characterize social media users participating in competing events, such as political campaigns and public health crises.Comment: Computational social science applicatio