Understanding the Impact of Entity Linking on the Topology of Entity Co-occurrence Networks for Social Media Analysis

A common form of analysis of textual data is entity co-occurrence, where networks of entities and their connections within the text are constructed and their topology analysed. As the analysis is focused on the entities and their relations, the tools used to extract them can have a potentially large effect on the results. A frequently used method as part of these analyses is entity linking, where extracted entities are mapped to a knowledge graph. Many established entity linking tools have been created for long text following standard spelling and grammar rules. As a result, the tools struggle on short, unstructured text such as tweets. On such text, it can be difficult to choose between tools and parameter settings, especially since ground truth is often unavailable. Given these challenges in entity linking on text and the direct influence of extracted entities on subsequent network analysis, we propose the need to apply multiple tools to create a more holistic set of results. We verify this assertion through a set of experiments. Using a dataset of approximately 21 million English-language tweets, we construct multiple entity co-occurrence networks using two tools (Fast Entity Linker and DBpedia Spotlight) and numerous confidence thresholds for each. We find that standard network analysis metrics, such as size, connectivity, and centrality are all heavily influenced by the choice of entity linking tool.