# An Automatic Participant Detection Framework for Event Tracking on Twitter
## 3 Materials & Methods
### Framework
#### 3.1.5 Extrapolation  
Extrapolation is analogous to entity set expansion. This step is necessary in APD because, as we show in Section 4, the resolution step inherits the bias of Twitter: Users discuss a few, popular participants and barely mention the rest. Extrapolation minimises the effects of bias by looking for the missing participants, identifying other concepts that are similar to the resolved participants.  
Our approach is based on the hypothesis that any missed participants are tightly-connected with the resolved participants. In reality, this assumption holds true in many domains. For example, the Wikipedia pages of Germany and France have a section with links to the articles of other European Union member states. Therefore we build a graph where the nodes represent Wikipedia articles, and undirected edges between them indicate that one article has an outgoing link to the other.  
The algorithm fetches linked articles twice. First, the algorithm fetches outgoing links from the seed set of participants that were resolved in the previous step, and retains the 100 most linked articles. Our approach creates weighted edges by comparing the first sentence of pairs of articles, similarly to before. Since articles retrieved in the first iteration are semantically closer to the seed set, the graph retains edges between them if their similarity is non-zero.  
Second, the algorithm fetches articles linked from these new pages and retains the 500 most linked articles. This time, the graph retains edges between articles if their cosine similarity is at least 0.5. We note that the more links the algorithm collects, the more candidate participants it considers. Accumulating more links, however, also means more API calls, which brings down the performance of APD to the detriment of real-time applications.  
While constructing the graph, our APD approach again excludes concepts with a year in their title, unless the year is a disambiguation aid. We also exclude \"List of\" articles, such as List of Premier League seasons, on the basis that they rarely represent participants, if at all. By being so selective with the Wikipedia articles it adds to the graph, the algorithm restricts the semantic drift.  
Eventually, the links between articles form clusters of Wikipedia concepts in the graph. We make the structure of the graph clearer using the Girvan-Newman algorithm [26]. We repeatedly remove the most central edge until the number of communities is fewer than the square root of the order of the graph. This step isolates noisy articles, but retains semantically-related concepts as part of the same cluster. Since extrapolation has a similar role to entity set expansion, we assume that graph components with fewer than four nodes are unlikely to represent a class of entities, and we discard them.  
To rank the new candidate participants, we score articles in the remaining graph components using Equation (1) by comparing the first sentence of each Wikipedia article with the corpus of tweets collected before the event. The algorithm again retains articles whose similarity scores exceed 0.05 as participants, thereby excluding concepts that barely overlap with the event domain. Our implementation ranks the remaining participants in descending order of similarity. We note that this step relies on the assumption of interconnectivity for semantic similarity and it only approximates the discriminative and temporal properties from the articles' first sentences.