Crisis Narratives and Topics of the COVID-19 Pandemic on Finnish Twitter

Narratives are central to the human experience because they inform how we make sense of the complex world around us. When the COVID-19 pandemic forced people to stay home and avoid physical contact, narratives of the evolving crisis were actively told on Twitter. In this thesis, I analyse crisis narratives and their development on Finnish Twitter. I use a dataset of 375,322 tweets that were collected between January 2020 and August 2021. The data are analysed with the help of topic modelling, a machine-learning method that is used to discover latent topics from large collections of texts. The most common topics and their temporal distribution as well as tweets that are strongly associated with these topics are analysed within the theoretical framework of crisis and narrative studies. The results show that the most common topics were related to the measures put in place to control the spread of the virus. The COVID-19 pandemic appeared to many as a crisis of regulations rather than as a health crisis. As relatively few people were affected by the virus itself, the crisis narratives shared on Twitter were more concerned with the impact the everchanging restrictions and guidelines had on people’s everyday lives than the foreign threat imposed by the virus. These results provide insight into the ways in which crises are constructed in narrative and thus can be used to better understand how future crises emerge and evolve

Essays on the Role of Firms’ Competition Culture in Finance: A Textual Analysis Based Approach

In this thesis, we introduce a measure of firms’ competition culture based on a textual analysis of firms’ 10-K filings. Using this measure, we study the relationship between competition culture and various phenomena in corporate finance for a large sample of US-based financial and nonfinancial firms. The thesis is comprised of three main studies as follows. The first study develops a measure of firms’ competition culture and based on theory and our own reasoning validate the measure by relating it to other well-known indicators of firms’ competition culture. Further, in this study, we argue and provide evidence that transient institutional ownership intensifies firms’ competition culture, while dedicated institutional ownership lessens it. In the second study, we argue that firms with greater levels of competition culture are more prone to meet/ beat analysts forecast and experience idiosyncratic stock price crashes. In this vein, we provide direct evidence that those firms with higher competition culture are able to consistently beat analysts’ forecasts. In addition, we present evidence that firms with more intensive competition culture are susceptible to firm-specific stock price crash risk. Furthermore, we investigate whether firms’ competition culture is a channel through which institutional investors are able to affect crash risk. In doing so, we document a positive relationship between competition culture and crash risk only among those firms with a high proportion of transient and a low proportion of dedicated institutional ownership. What is more, we directly test and find evidence that supports the notion that firms’ competition culture partially mediates the relationship between the composition of firm’s dedicated and transient institutional ownership and firm-specific crash risk. Finally, we examine the effect of competition culture on bank lending and loan loss provisioning. We find evidence that banks with greater levels of competition culture are generally more prone to engage in lending and loan loss provisioning activity. However, we find that during the recent financial crisis banks with higher pre-crisis competition culture reduce lending more and have a more pronounced increase in loan loss provisioning during the crisis. The findings of this thesis have important policy implications since it signals that competition culture is able to affect a number of economic outcomes

Computational Thematic Analysis of Online Communities

Public health researchers can use thematic analysis to develop human understandings of health topics from the lived experiences discussed in online communities. However, thematic analyses of online communities are difficult to conduct because large data sets amplify the resource intensity and complexity of the common phases: Data Collection, Data Familiarization, Coding, and Theme Review. Researchers can manage this amplification by integrating computational techniques that facilitate scalable interaction with large data sets when they converge with tasks completed during a thematic analysis. My thesis’ research explored barriers to integrating computational techniques into thematic analysis through three research questions: RQ1. Could computational techniques be used within a thematic analysis to assist with the analysis of online communities’ data? RQ2. How might tools be developed to not require programming expertise when integrating computational techniques as part of thematic analysis tasks? RQ3. How does a computational thematic analysis that integrates computational techniques compare with a traditional manual thematic analysis? To address these questions, I used a three-staged approach where I first piloted integrating techniques in a thematic analysis of addiction recovery. I then designed artifacts based on my pilot experience that allow qualitative researchers without programming expertise to integrate techniques. Finally, I deployed my artifacts with public health researchers to explore integration’s impact on their real-world thematic analyses. During my Pilot Stage, I conducted a topic-guided thematic analysis of two Reddit addiction recovery communities. Performing this analysis contributed a demonstration of integrating Latent Dirichlet Allocation topic modelling, a computational technique, to guide my reflexive thematic analysis by sampling interesting places in online discussion data sets for coding. Additionally, I discussed how integration benefited my data familiarization by facilitating the identification of patterns while being limited due to balancing metric optimization with interpretive usefulness when creating topic models. In my Design Stage, I created my Computational Thematic Analysis Workflow and Computational Thematic Analysis Toolkit to build upon my pilot stage experiences and support qualitative researchers. My workflow provides researchers with guidance on planning a reflexive thematic analysis of online communities that integrates computational techniques. Similarly, my toolkit supports qualitative researchers by implementing computational techniques as reusable tools in a graphic user interface that integrates into thematic analyses without requiring programmer expertise. My Deploy Stage investigated the impact of integrating computational techniques by collaborating with public health researchers studying COVID-19 news article comments.The researchers independently performed two inductive thematic analyses, one of which used my Computational Thematic Analysis Toolkit. I then work with the researchers to compare their processes and results. From this comparison, I identified that integrating computational techniques to facilitate multiple data interactions aided the analysis by enabling different interpretations. Additionally, despite both analyses developing a convergent set of themes, computational technique integration had subtle influences leading to divergent analysis processes and coding approaches. The contributions from my three stages have collective implications for qualitative research, human-computer interaction, and public health. My work provides qualitative researchers with demonstrations and tools that support integrating computational techniques to research online communities. My research created a base workflow and toolkit that human-computer interaction practitioners can support and extend to facilitate the integration of computational techniques into qualitative methods. Additionally, I addressed calls in human-computer interaction research to include qualitative perspectives in work that impacts qualitative researchers. Finally, public health researchers can use my guidance and toolkit to manage the amplification of resource intensity and complexity to perform thematic analyses on the lived experiences discussed in online communities. As researchers identify online communities’ perspectives on new and existing health issues, they can de- velop health interventions that impact people represented by online communities

EFFECTIVE METHODS AND TOOLS FOR MINING APP STORE REVIEWS

Research on mining user reviews in mobile application (app) stores has noticeably advanced in the past few years. The main objective is to extract useful information that app developers can use to build more sustainable apps. In general, existing research on app store mining can be classified into three genres: classification of user feedback into different types of software maintenance requests (e.g., bug reports and feature requests), building practical tools that are readily available for developers to use, and proposing visions for enhanced mobile app stores that integrate multiple sources of user feedback to ensure app survivability. Despite these major advances, existing tools and techniques still suffer from several drawbacks. Specifically, the majority of techniques rely on the textual content of user reviews for classification. However, due to the inherently diverse and unstructured nature of user-generated online textual reviews, text-based review mining techniques often produce excessively complicated models that are prone to over-fitting. Furthermore, the majority of proposed techniques focus on extracting and classifying the functional requirements in mobile app reviews, providing a little or no support for extracting and synthesizing the non-functional requirements (NFRs) raised in user feedback (e.g., security, reliability, and usability). In terms of tool support, existing tools are still far from being adequate for practical applications. In general, there is a lack of off-the-shelf tools that can be used by researchers and practitioners to accurately mine user reviews. Motivated by these observations, in this dissertation, we explore several research directions aimed at addressing the current issues and shortcomings in app store review mining research. In particular, we introduce a novel semantically aware approach for mining and classifying functional requirements from app store reviews. This approach reduces the dimensionality of the data and enhances the predictive capabilities of the classifier. We then present a two-phase study aimed at automatically capturing the NFRs in user reviews. We also introduce MARC, a tool that enables developers to extract, classify, and summarize user reviews