# Making Sense of Language Signals for Monitoring Radicalization
## 3 Semantic Modeling
### Semantic Vocabulary for LIWC (Sliwc)  
The widespread use of LIWC for social-psychological analysis has established its psychometric properties as a de facto standard, both in industry and in academia. LIWC annotations can be separated into two distinct parts: the annotation model and the specific dimensions (and sub-dimensions) used in each specific version of the LIWC dictionary. LIWC's popularity has led several projects to adapt its annotation model when publishing their own LIWC-aligned lexicons, as will be seen in Section 3.2. Hence, our goal is to provide not only a vocabulary that models LIWC annotations, but a model that can be reused and extended by other vocabularies that model LIWC-aligned vocabularies. The result is the SLIWC [50], which covers both the annotation model and the dimensions in different versions of LIWC. The problem of annotations is hardly limited to LIWC. In fact, there are multiple competing vocabularies in the space (e.g., NIF). Unfortunately, none of the existing vocabularies is a perfect fit in this case due to the restrictions imposed by the use of JSON-LD in several parts of the system and the need for a clean JSON schema. Hence, we decided to design a very minimal annotation model, which can both be integrated with NIF and generate a friendlier JSON schema. The model has been inspired by previous experience with Onyx [41]. Most notably, it is also aligned with the provenance ontology [54], which allows for annotations to be linked to the process and/or agent that generated them. This is especially useful when dealing with multiple NIF services, all of which may add their own annotations to a piece of content. A summary of the result is shown in Figure 1. The main elements are: the annotation activity (AnnotationActivity), the annotation set (ASet), the annotation (Annotation), and the category (Category). When a piece of content is annotated, either manually or through an automatic process, one or more annotation sets will be generated, linking both to the original piece of content and to the activity that generated the annotation. Each annotation set will contain one or more annotations. When an annotation set contains more than one annotation, they should be considered as related and processed together. For instance, an activity may generate an annotation set with a count of positive and negative words within some piece of content. If we were to process them individually, we might reach an ambiguous conclusion that the content is both positive and negative. By including them inside the same annotation set,  
Figure 1: Main concepts related to annotation in the SLIWC vocabulary. we are signaling that neither of them should be considered individually, and we allow for other activities to produce further annotation sets that aggregate existing multi-annotation sets into simpler sets with a single annotation. In our example, a new activity might add a new annotation set that produces the net count, subtracting the negative words from the positive ones. An annotation will have a category, depending on the type of dictionary used and multiple properties. Depending on the type of activity, the annotation may express how many occurrences (typically, words) were found of the given category in the content (count) and the ratio of occurrences of that category over the total (ratio). More generally, annotation can provide a normalized score (score). The second part of the vocabulary consists of a taxonomy of all the annotation dimensions and labels in LIWC. The list of dimensions and labels were extracted from the LIWC website [55]. Furthermore, the dimensions are structured in a hierarchical manner. Hence, the dimensions in the vocabulary have been expressed as a Simple Knowledge Organization System (SKOS) taxonomy [56]. The main dimensions categories measured by LIWC for our work are grouped in sets depending on the psychological process they are describing. Four of them were taken into account for this analysis: drives, affective, personal concerns, and social.