# An Automatic Participant Detection Framework for Event Tracking on Twitter
## 3 Materials & Methods  
Although APD resembles query expansion, the scope of the expansion terms differs slightly. In this section we describe the new Information Retrieval (IR) task of APD and explain how it is different from query expansion. Mindful of these differences, we propose a framework to identify participants before the event starts and present our own implementation. To conclude this section, we discuss an evaluation methodology for APD.  
Before describing APD, we formally define event participants in the context of events and TDT. Participants are a part of events, so it is only intuitive that the properties of event participants are closely-related to those of events. Definition 1 of events considers only where and when the event is taking place, but we modify it to include participants:  
**Definition 2**.: _An event is a real-word occurrence that happens at a particular place and at a particular time, and that involves any number of participants._  
Participants are related to an event because they participate in it--they either affect the event or the event affects them. Therefore participants inherit the spatial and temporal properties of events:  
**Definition 3**.: _A participant is a real-world concept that affects or is affected by the event while the event is ongoing._  
This definition describes two characteristics: the discriminative and temporal properties of participants. The discriminative property binds participants to be active in the event since they either affect or are affected by it. For a participant to be truly discriminative, normally it is only active in one event at a time. For example, while a debate involving American presidential candidates affects the USA, the presidential debate is not the only event in the USA. Therefore the country itself is not discriminative with regards to the debate, unlike the presidential candidates who are involved in it.  
The temporal property of participation requires that participants are active at the same time as the event. This excludes entities that are not relevant while the event is taking place. In the presidential debate example, election candidates that dropped out and who are not part of the debate are not temporally relevant. This definition considers the presidential candidates and the debate's host to be participants because they are directly affecting the debate while it is ongoing.  
Moreover, the definition is flexible enough such that additional assumptions can restrict or relax these properties. For example, in the analysis of Section 4 we do not consider injured and suspended football players to be participants as they are not actively participating in the football match. However, unavailable players could be perceived as participants because their absence still has consequences on the event, such as by influencing the coach's decisions or the match's outcome.  
Furthermore, we acknowledge that normally, participants describe named entities: Persons, organisations, or locations. We follow this assumption in the rest of this article. However, while we assume that all participants are named entities, not all named entities are participants; according to Definition 3, only named entities that are discriminative and temporally-relevant are participants. This is what makes APD different from other participant-centric TDT approaches [1; 2; 3]. Based on these definitions, we present the APD framework and our implementation of it next.