The role of textual data in finance: methodological issues and empirical evidence

This thesis investigates the role of textual data in the financial field. Textual data fall into the more extensive category of alternative data. These types of data, such as reviews, blog post, tweet, are constantly growing, and this reinforces the importance in several domains. The thesis explores different applications of textual data in finance to highlight how it is possible to use this type of data and how this implementation can add value to financial analysis. The first application concerns the use of a lexicon-based approach in the credit scoring model. The second application proposes a causality detection between financial and sentiment data using an information-theoretic measure, the transfer entropy. The last application concerns the use of sentiment analysis in a network model, called BGVAR, to analyze the financial impact of the Covid-19 Pandemic. Overall, this thesis shows that combining textual data with traditional financial data can lead to a more insightful knowledge and, therefore, to a more in-depth analysis, allowing for a broader understanding of economic events and financial relationships among economic entities of any kind

Automated fact-checking: A survey

As online false information continues to grow, automated fact-checking has gained an increasing amount of attention in recent years. Researchers in the field of Natural Language Processing (NLP) have contributed to the task by building fact-checking datasets, devising automated fact-checking pipelines and proposing NLP methods to further research in the development of different components. This article reviews relevant research on automated fact-checking covering both the claim detection and claim validation components

Helicopter parenting through the lens of reddit: A text mining study

The study aimed to understand Reddit users’ experience with helicopter parenting through first- hand accounts. Text mining and natural language processing techniques were employed to extract data from the subreddit r/helicopterparents. A total of 713 original posts were processed from unstructured texts to tidy formats. Latent Dirichlet Allocation (LDA), a popular topic modeling method, was used to discover hidden themes within the corpus. The data revealed common environmental contexts of helicopter parenting (i.e., school, college, work, and home) and its implication on college decisions, privacy, and social relationships. These collectively suggested the importance of autonomy-supportive parenting and mindfulness interventions as viable solutions to the problems posed by helicopter parenting. In addition, findings lent support to past research that has identified more maternal than paternal models of helicopter parenting. Further research on the implications of the COVID-19 pandemic on helicopter parenting is warranted

Galileo, a data platform for viewing news on social networks

This article aims to introduce Galileo, a platform for extracting and organizing news media data on social networks. Galileo integrates publications made on the main social networks used in the information ecosystem, namely Facebook, Twitter, and Instagram. Currently, the system includes 97 media outlets from nine countries: Brazil, Chile, Germany, Japan, Mexico, South Korea, Spain, United Kingdom, and United States. Galileo uses a Twitter API and the service CrowdTangle to download Facebook and Instagram posts. This data is stored in a local database and can be accessed through a user-friendly interface, which allows for the analysis of different characteristics of the posts, such as their text, source popularity, and temporal dimension. Galileo is a tool for researchers interested in understanding news cycles and analyzing news content on social networks.

ELAINE: rELiAbility and evIdence-aware News vErifier

Disinformation is one of the main problems of today’s society, and specifically the viralization of fake news. This research presents ELAINE, a hybrid proposal to detect the veracity of news items that combines content reliability information with external evidence. The external evidence is extracted from a scientific knowledge base that contains medical information associated with coronavirus, organized in a knowledge graph created from a CORD-19 corpus. The information is accessed using Natural Language Question Answering and a set of evidences are extracted and their relevance measured. By combining both reliability and evidence information, the veracity of the news items can be predicted, improving both accuracy and F1 compared with using only reliability information. These results prove that the approach presented is very promising for the veracity detection task