An evaluation of keyword extraction from online communication for the characterisation of social relations
This work addresses the need for better tools to analyze social relationships in online communication, though it is incremental as it applies standard NLP methods to a specific domain.
The study tackled the problem of characterizing diverse social relationships in online communities by evaluating whether keywords extracted from message content can represent relationship characteristics, finding that human assessments confirmed the keywords conveyed relevant information about the relationships.
The set of interpersonal relationships on a social network service or a similar online community is usually highly heterogenous. The concept of tie strength captures only one aspect of this heterogeneity. Since the unstructured text content of online communication artefacts is a salient source of information about a social relationship, we investigate the utility of keywords extracted from the message body as a representation of the relationship's characteristics as reflected by the conversation topics. Keyword extraction is performed using standard natural language processing methods. Communication data and human assessments of the extracted keywords are obtained from Facebook users via a custom application. The overall positive quality assessment provides evidence that the keywords indeed convey relevant information about the relationship.