The spread of COVID-19 across the globe has produced global and possibly persistent economic disruption. This study follows the design science research process and conducts qualitative and quantitative analysis to identify and investigate Canadian agri-food company responses to COVID-19. The results show the possibility of capturing companies’ responses from web-based data, the breadth of responses, and the relationships between the communication of corporate responses and their reception among social media users. Divergences of regression results across different languages are also discussed in this paper. The findings will help academic researchers, business leaders and policymakers understand corporate responses and subsequent reactions better.

abstract: Stock market news and investing tips are popular topics in Twitter. In this dissertation, first I utilize a 5-year financial news corpus comprising over 50,000 articles collected from the NASDAQ website matching the 30 stock symbols in Dow Jones Index (DJI) to train a directional stock price prediction system based on news content. Next, I proceed to show that information in articles indicated by breaking Tweet volumes leads to a statistically significant boost in the hourly directional prediction accuracies for the DJI stock prices mentioned in these articles. Secondly, I show that using document-level sentiment extraction does not yield a statistically significant boost in the directional predictive accuracies in the presence of other 1-gram keyword features. Thirdly I test the performance of the system on several time-frames and identify the 4 hour time-frame for both the price charts and for Tweet breakout detection as the best time-frame combination. Finally, I develop a set of price momentum based trade exit rules to cut losing trades early and to allow the winning trades run longer. I show that the Tweet volume breakout based trading system with the price momentum based exit rules not only improves the winning accuracy and the return on investment, but it also lowers the maximum drawdown and achieves the highest overall return over maximum drawdown. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2016

Pew Research Center estimates that as of 2014, 74% of the Internet Users used social media, i.e., more than 2.4 billion users. With the growing popularity of social media where Internet users exchange their opinions on many things including their daily life encounters, it is not surprising that many organizations are interested in learning what users say about their products and services. To be able to play a proactive role in steering what user’s say, many organizations have engaged in efforts aiming at identifying efficient ways of marketing certain products and services, and making sure user reviews are somewhat favorable. Favorable reviews are typically achieved through identifying users on social networks who have a strong influence power over a large number of other users, i.e. influential users. Twitter has emerged as one of the prominent social network services with 320 million monthly active users worldwide. Based on the literature, influential Twitter users have been typically analyzed using the following three models: topic-based model, topology-based model, and user characteristics-based model. The topology-based model is criticized for being static, i.e., it does not adapt to the social network changes such as user’s new posts, or new relationships. The user characteristics-based model was presented as an alternative approach; however, it was criticized for discounting the impact of interactions between users, and users’ interests. Lastly, the topic-based model, while sensitive to users’ interests, typically suffers from ignoring the inclusion of inter-user interactions. This thesis research introduces a dynamic, comprehensive and topic-sensitive approach for identifying social network influencers leveraging the strengths of the aforementioned models. Three separate experiments were conducted to evaluate the new approach using the information diffusion measure. In these experiments, software was developed to capture users’ tweets pertinent to a topic over a period of time, and store the tweet’s metadata in a relational database. A graph representing users was extracted from the database. The new approach was applied to the users’ graph to compute an influence score for each user. Results show that the new composite influence score is more accurate in comprehensively identifying true influential users, when compared to scores calculated using the characteristics-based, topic-based, and topology-based models. Also, this research shows that the new approach could leverage a variety of machine learning algorithms to accurately identify influencers. Last, while the focus of this research was on Twitter, our approach may be applicable to other social networks and micro-blogging services.

Social media today is a dominant communication tool, which structures not only our social interactions but also filter the information users are getting displayed. The big social media platforms use our interaction data to analyse our behaviour and sell the data for commercial interest. But not only the pure interaction data is valuable for these platforms. Also hidden information, which can be derived from our interactive networks, about our social structures, social classifications and social status are gathered and monetised. This research attempts on the one hand to uncover some of these methods used by social media platforms, and on the other hand, also wants to show how useful these new methods can be for research on social phenomena. Therefore, this study goes beyond the confining limits of traditional sociology, where either qualitative or quantitative methods are applied. Following the idea of Critical Realism, the positivist and constructivist methods are applied in combination in order to provide thick accounts of the studied material. In this study, varying socioeconomic classification systems (like the Sinus-Milieu models) are investigated and evaluated against the background of Bourdieu’s ideas on cultural and social forms of capital. The present study uses a mixed method approach (Social Network Analysis and Sentiment Analysis) to analyse quantitative data from Twitter conversations which were collected during the Austrian EU Election 2019. In conclusion, one could say that the overall purpose of this study is to demonstrate the usefulness of Critical Realism for social media research, since this approach can create a thicker account of the studied material than other, more traditional methods. This undertaking is demonstrated by the findings of the study. These findings are the building of specific sub-clusters of EU candidates which are not related to the same political background and traditional demographics but whose relation can be detected and described using Bourdieu’s concepts of social and cultural capital. As a mean for gathering empirical data, Twitter turned out to be a useful and accessible tool for this study.