Illegal immigration and media exposure: evidence on individual attitudes

Illegal immigration has been the focus of much debate in receiving countries, but little is known about the drivers of individual attitudes towards illegal immigrants. To study this question, we use the CCES survey, which was carried out in 2006 in the USA. We find evidence that—in addition to standard labor market and welfare state considerations—media exposure is significantly correlated with public opinion on illegal immigration. Controlling for education, income, ideology, and other socio-demographic characteristics, individuals watching Fox News are 9 percentage points more likely than CBS viewers to oppose the legalization of undocumented immigrants. We find an effect of the same size and direction for CNN viewers, whereas individuals watching PBS are instead more likely to support legalization. Ideological self-selection into different news programs plays an important role, but cannot entirely explain the correlation between media exposure and attitudes about illegal immigration

Machine Learning Methods for Classification of Unstructured Data

Natural language processing is a field that studies automatic computational processing of human languages. Although natural language is symbolic and full of rules and ontologies, the state-of-the-art approaches are typically based on statistical machine learning. With the invention of word embeddings, researchers have managed to circumvent a problem of sparse feature space and to take into account word semantics learned from large corpora. When it comes to artificial strings, e.g. in source code, the usage of embeddings is restricted due to extremely large vocabulary. This dissertation covers two interesting applications using both embedding based and bag-of-words approaches: one related to industrial scale Android malware classification and another to extraction of soft skills and their impact on occupational gender segregation. Data coming from both applications is unstructured since Android applications consist of a set of files belonging to mainly unstructured data or semi-structured data, while job postings used for soft skill analysis represent free text where no clear structure is defined. The first part of the dissertation is dedicated to industrial scale Android malware classification covering a full pipeline from feature extraction to deployment. Various groups of features are extracted from Android installation package files, resulting in large high-dimensional sparse feature space. We investigated the ways to reduce feature space from millions to thousands of features efficiently and managed to improve the decision boundary. Finally, we addressed the problem of fair model assessment by separating training and test samples in time and evaluated proposed ensemble-based methods accordingly. The second part of the dissertation is dedicated to statistical and machine learning based soft skill analysis and their impact on occupational gender segregation. Soft skills are personality traits facilitating human interaction. Our work is pioneering with respect to large scale soft skill requirements analysis and their impact on salary. We show that not only soft skills are useful in predicting gender ratio estimate of the corresponding job category, but also most of them comply with gender stereotypes. Besides curating a soft skill list using job postings, we also propose various input representations to increase the precision of soft skill extraction using the context where soft skill occurs

An empirical analysis of political activity in Hollywood

Film, Political contribution, Hollywood,