Scraping news sites and social networks for prejudice term analysis

Abstract

Computer-Mediated Communication (CMC) has paved the way for new patterns of linguistic aggravation. Hidden behind the screen, anyone can comment on any other person's opinion using an offensive or injurious tone. Besides, types of prejudice such as homophobia, sexism, racism, xenophobia, anticlericalism, body/addiction shaming, among others, are easily found nowadays in social networks and other forms of interactive Web sites potentiated by Web 2.0. This increasing violence deserves further investigation from different academic perspectives, among which Sociolinguistics stands out. This paper is concerned with the design and development of a set of computer-based tools to collect articles and posts with the respective comment threads that can be used as sources to extract prejudice terms and allow different analyses to be conducted. These prejudice terms were devised using a sociolinguistic variable stratificat ion approach. We will focus on the filters used to extract the relevant fields from the Web pages collected, and on the converters used to adapt formats to obtain a common format for information representation. We will also introduce the statistical analysis processor that explores the extracted data, in that format, to output a set of indicators.