# An Automatic Participant Detection Framework for Event Tracking on Twitter
## 1 Introduction  
For many people, the idea of a football match is a well-defined concept: Two teams of 11 players, playing each other for 90 min of football. Thousands of spectators use social networks to transform what was a traditionally social experience into a lively discussion, and it is not just a matter of football or sports events. Politics, natural disasters and tragedies all attract a lot of attention, and Topic Detection and Tracking (TDT) research pounced on the opportunity, using tweets to build timelines of events in near real-time. However, these timelines remain far below the standards of the news media, partially because machines do not understand events like humans. One of the most basic semantic components of events is their participants: Events either affect participants, or participants affect them. For example, the players directly influence the outcome of a football match, and candidates shape elections. While it is essential for humans to recognize the participants to thoroughly understand the event, few TDT systems give them their due consideration. The traditional definition of an event excludes participants, and even approaches that focus on them [1, 2, 3] never formalize what qualifies an entity to be a participant. Understanding the role of participants in events, and identifying who or what is participating in an event, can be instrumental for TDT and event modelling systems to produce more useful and accurate information about events. For example, [PERSON] et al. [1, 1], [PERSON] and [PERSON] [2], and [PERSON] et al. [3], construct separate timelines for named entities captured by off-the-shelf Named Entity Recognition (NER) tools. The three systems reporta positive impact of this participant-centric view of events on TDT's results, highlighting the value of participants. However neither approach distinguishes between incidental mentions of named entities and actual participants with closer ties to the event. Here, we show that this distinction is not just important, but also necessary when tracking events on Twitter. In this article we extend our previous work [4], where we argued that the definition of events should include participants, and describe Automatic Participant Detection (APD) as a problem to identify the event's participants before the event starts. With this knowledge before the event has even started, our system could collect tweets mentioning the event's participants, leading to a visibly broader coverage, as shown in Figure 1. However, we emphasise that event knowledge is a tool with wide applicability not just in TDT, but also related tasks, such as to summarise events [5], to create explainable events [6], and to model and mine events [7]. In this article we elaborate on the initial idea of APD with the following contributions:  
* We define event participants and propose a six-step framework for APD. Differently from existing approaches that consider participants [1; 2; 3], APD extracts participants before the event starts, not while it is ongoing. This framework first confirms which named entities are relevant to the event. Then it looks for other participants that Twitter users are not discussing, and hence which NER cannot identify;
* We explore what named entities Twitter users talk about before sports events, with a focus on football matches and basketball games. We show that although NER toolkits are relatively capable of navigating Twitter's informal tweeting habits, many of the named entities that users mention before events are spurious and not directly-related to the event. As a result, NER cannot be a stand-alone replacement for APD;
* We implement the APD framework based on the hypothesis that event participants are tightly-interconnected on Wikipedia. Our evaluation on football matches and basketball games shows that our approach is capable of identifying almost twice as many participants as traditional NER approaches.