# An Automatic Participant Detection Framework for Event Tracking on Twitter
## 3 Materials & Methods
### Framework
#### 3.1.4 Resolution  
As the results in Section 4 clearly show, the tweeting habits before events make it infeasible for NER to detect all of an event's participants while simultaneously discarding \begin{table}
\begin{tabular}{l l r} \hline \hline
**Event Query** & **Collected (UTC)** & **Tweets** \\ \hline \#BarcaAtleti, Barca, Barcelona, Atleti, Atletico & 30 June 2020 18:45–19:45 & 6889 \\ \#WOLARS, Wolves, Arsenal & 4 July 2020 15:15–16:15 & 26,467 \\ \#AVLMUN, Villa, Manchester United & 9 July 2020 18:00–19:00 & 16,044 \\ \#LIVBUR, Liverpool, Burnley & 11 July 2020 12:45–13:45 & 9491 \\ \#ARSLIV, Arsenal, Liverpool & 15 July 2020 18:00–19:00 & 26,498 \\ \#TOTLEI, Tottenham, Leicester & 19 July 2020, 13:45–14:45 & 4006 \\ \#LIVCHE, Liverpool, Chelsea & 22 July 2020 18:00–19:00 & 32,360 \\ \hline Nets, Warriors & 22 December 2020 23:00–00:00 & 14,333 \\ Lakers, Clippers & 23 December 2020 02:00–03:00 & 48,141 \\ Celtics, Nets & 25 December 2020 21:00–22:00 & 7030 \\ Lakers, Mavericks & 26 December 2020 00:00–01:00 & 9508 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The datasets used in the evaluation. non-participants. The resolution step allows APD to rely less on NER, and instead confirm which candidates are discriminative and temporally-relevant, thereby making them participants. Our approach exploits Wikipedia to this end. Apart from its extensive coverage, Wikipedia represents each concept as a single article, and we use this aspect to assess the suitability of named entities to be participants. The algorithm fetches Wikipedia articles that could represent named entities by submitting each named entity separately as a query to the MediaWiki API ([[https://mediawiki.org/wiki/API:Main_page](https://mediawiki.org/wiki/API:Main_page)]([https://mediawiki.org/wiki/API:Main_page](https://mediawiki.org/wiki/API:Main_page)), last accessed on 17 February 2021) and retrieving the top five related articles. We exclude articles with a year in the title, such as \"2019-20 Premier League\", unless the year is used to disambiguate the article. To assess whether a Wikipedia article is fit to be a participant, we compare the corpus of tweets with the first sentence in the article. We focus only on its first sentence because it succinctly describes the Wikipedia concept in its present state, and therefore this sentence can be an approximation for the candidate's temporal relevance to the event. For example, the first sentence in [PERSON]'s Wikipedia article describes what he does at present:  
[PERSON] [...] is a French professional footballer who plays as a forward for Premier League club Arsenal and the France national team. He is known for his pace, hold-up play, and work-rate. [...]  
Before computing a score for each article, we vectorise all tweets and the Wikipedia sentences by applying stemming and stopword removal. We also normalise repeating letters, which are more common on Twitter than on Wikipedia. For example, we replace the word _goodo_ with the shorter _go_. Our approach weights vectors using Term Frequency-Inverse Corpus Frequency (TF-ICF). This weighting scheme is similar to Term Frequency-Inverse Document Frequency (TF-IDF), but it approximates IDF using a different, general corpus [25]. As a result, this term-weighting scheme boosts terms that appear more often in the event domain than in general. As a general corpus for the ICF component, we used Twitter's Sample API to collect a large corpus of English tweets over 12 hours. Then, our approach scores the five articles collected earlier for each candidate as follows:  
\[score_{c,a}=cos(c,a_{title})\cdot cos(C,a_{first}). \tag{1}\]  
These two components measure how relevant the Wikipedia article is to the named entity and to the event's domain respectively. We measure relevance using cosine similarity due to its common application to assess document similarity. \(cos(c,a_{title})\) is the cosine similarity between the candidate's name \(c\) and the article's title, \(a_{title}\); intuitively, an article about the candidate mentions them in the title.