Perspectives are conveyed in many ways. Explicit opinions or highly subjective terms are easily identified. However, perspectives are also expressed more subtly. For instance, Nick Wing argues that media describe white suspects (e.g. brilliant, athletic) more positively than black victims (e.g. gang member, drug problems). Ivar Vermeulen (p.c.) observes in a small Dutch corpus that Moroccan perpetrators are easily called thieves (implying generic behavior), where other perpetrators from Dutch only stole something (implying incidental behavior). These observations are anecdotal, but reveal how choices concerning what information to include or how to describe someone’s role may display a specific perspective.
This project will investigate how linguistic analyses may be used to identify these more implicit ways of expressing perspectives in text. This research will be carried out in three stages: First, large scale corpus analyses will be applied to identify distributions of semantic roles (what entities do) and other properties assigned to them (their characteristics). In the second stage, generic participants will be linked to the semantic role they imply (e.g. a thief will be linked to the perpetrator of stealing). With these links, we can investigate whether thieves are described differently from people who steal. In the third stage, emotion and sentiment lexica will be used to identify the sentiment associated with descriptions of people enabling research that investigates whether people are depicted positively or negatively.
The research is carried out in the context of digital humanities and social sciences. Evaluation and experimental setup will be guided towards identifying differences in perspective between sources. In addition to correctness of linguistic analyses (intrinsic evaluation), the possibility of using the method for identifying changes in perspective over time (historic research) or differences in perspective between sources (communication science) will be investigated.