# An Automatic Participant Detection Framework for Event Tracking on Twitter
## 4 Results
### Discussion
Other contexts are more unique. The passing of ex-Boston Celtics player [PERSON]. [PERSON] shortly before the NBA game opposing Boston Celtics and Brooklyn Nets generated many tributes, which led to all algorithms mistaking him as a participant. Another explanation for the poor precision is that there are several ways of referring to participants, many of them colloquial and redundant. For example, TwitterNER captured three different references to Liverpool in the top 10 ranking of the match between Liverpool and Burnley: Liverpool, the reds, and LFC. The tweeting behaviour does not only impact precision, but recall too. Logically, the fact that football clubs release the line-ups on Twitter should make it easier to capture participants. However, it has become common practice to announce the players using images or videos, which NER techniques cannot parse. In NBA, the starting five players are published much later, so sometimes the participants remain uncertain until the very start of the game, making for an even more challenging task. There are also inequalities among participants, with star players generally attracting far more attention than the others. For example, out of 6889 tweets in the corpus of the match between Barcelona and Atletic Madrid, only 4 mention Atletic Madrid's defender, [PERSON]. All of these factors explain why NER is inadequate to solve the APD problem on its own, no matter how accurate the library is at identifying named entities. APD's two steps that go beyond NER, resolution and extrapolation, solve these challenges by rejecting non-participant named entities and looking for missed participants respectively. The resolution step in our APD approach is relatively successful in overcoming the low precision values, as shown in Table 2. Naturally, the recall values at the resolution stage cannot exceed the baseline's results, but precision increases drastically at a small expense to recall. In other words, our APD implementation correctly resolves most valid participants while simultaneously filtering out noisy named entities. While the role of resolution is to automatically-generate a seed set of participants with high precision, extrapolation looks for the missing participants to improve recall. The final rankings of our APD algorithms is the list of resolved participants followed by the list of extrapolated participants. We retain the top 50 participants in this ranking, and Table 3 shows how our APD algorithms' final rankings see a sharp increase in recall with a smaller drop in precision. Extrapolation achieves its goal of improving recall, often needing very few examples of participants to identify others. At the same time, while precision decreases from resolution, it remains much higher than the baselines' precision values. We used the one-tailed paired samples \(t\)-test to evaluate the improvements of our APD approaches over the baselines' results of Table 2. With \(p\)-values well below 0.01, the improvements in precision and recall  
Figure 3: Discussion increases immediately after football clubs release their line-ups. are all statistically-significant at the 99% confidence level. We attribute the drops in precision to two factors. First, extrapolation captures several entities that would normally be participants. For example, [PERSON] plays regularly for Barcelona, but he was injured for the match against Atletic Madrid. Even his absence, [PERSON] could be interpreted as being an indirect participant, as described in Section 3. Regardless of the interpretation, since our APD approach was unaware of his status, it identified him as a participant along the other Barcelona players. Second, extrapolation captures several tangential concepts. For example, the teams and players in English football matches are related to the Premier League. Therefore when extrapolation could not find more relevant players, it continued listing concepts related to the Premier League and English teams in general.