Dynamic Named Entity Recognition model to distinguish authors’ positions relative to mentioned locations

When sending a tweet, Twitter authors refer either to their current locations (in-situ location) or to distant (remote locations). Although essential for several spatial analysis tasks, distinguishing between in-situ and remote location entities poses significant challenges due to the ambiguity of natural language, informal and unstructured tweets, and dynamic location entities. To address these challenges, we developed a Dynamic Named Entity Recognition (DNER) model to differentiate between the two types of location entities in Twitter data. We investigated three annotation approaches and set up an annotation guideline based on strict grammatical rules on a decision tree. Using our custom-trained DNER model, we validated the effectiveness of our model on a validation dataset representative of the most prominent Twitter sources. Although to a marginal extent, our results show the model's effectiveness in distinguishing between in-situ and remote location mentions. Posts generated via native Twitter applications (Twitter for Android, iPhone and iPad) returned 64% and 32%, respectively, for the in-situ and remote locations within the bounding box of the geocoded locations. For Instagram posts, the returned percentages were 87% and 77%, respectively, for the in-situ and remote locations. We conclude that the DNER model is more effective on native applications than on Instagram-generated posts. • A Dynamic Named Entity Recognition (DNER) model performs better on data from native Twitter applications than on Instagram-generated posts. • Decision trees aid in annotating highly ambiguous posts • Group-based annotations are effective for consistent and diverse results in complex NER tasks. • Data composition affects model and validation results