# Local Event Detection Scheme by Analyzing Relevant Documents in Social Networks
## 4 Experiments
This means that tweets contained more regional information than geo-tagged tweets. The proposed scheme shows the best performance in terms of precision. The \begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Value** \\ \hline Processor & Intel i5-3570k, 3.4 GHz, 4 Core \\ Memory & 16 GB \\ No. of tweets & 119,243 \\ No. of relevant documents & 137,942 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The experimental environment. Figure 7: Comparison of \(NM_{G}\) (network modularity) according to different thresholds. proposed scheme improved performance by about 20% through the relevant document analysis compared to OnlyTweet. Figure 9 shows a comparison of recall according to the local event detection schemes. Similar to the experiment results of precision, our scheme achieves the best performance over the existing schemes in improving performance by about 22%. The recall performance of TBEM, OnlyTweet, GTBEM, and the proposed scheme achieved about 40%, 61%, 36%, and 83%, respectively. As shown above, the recall of GTBEM is also much lower than the others, since it is hard to find local events, as it takes only geo-tag information into account, i.e., the detected events with at least 1 or more geo-tag information. The recall performance of TBEM achieved 42% or more, since it is hard to extract regional information. The proposed scheme achieved the highest recall of about 0.83 (83%). This means that relevant local events were well detected, since we utilized not only a geographical dictionary but also relevant documents to complement spare geographical data. Figure 10 shows a comparison of F-measure according to the local event detection schemes. F-measure is the harmonic mean of precision and recall. Therefore, the performance results of F-measure are reasonable since the proposed scheme already achieved the highest precision and recall over the existing schemes as shown in Figures 10 and 11. The F-measures of TBEM, OnlyTweet, GTBEM, and the proposed scheme achieved about 41%, 62%, 37%, and 85%, respectively. As a result, the proposed scheme achieves higher performance than the existing schemes by about 40% or more since it considers both the relevant document analysis based on text mining and geo-tagging. Figure 8: Comparison of precision according to the local event detection schemes. Figure 9: Comparison of recall according to the local event detection schemes. Figure 11 shows the F-measure according to the number of top-k events. When \(k\) is 20, all schemes have a high F-measure. The F-measure of TBEM, OnltyTweet, and the proposed scheme achieved about 60%, 65%, and 73%, respectively. The proposed scheme achieves higher performance than the existing schemes by about an average 10% or more when \(k\) is 20. The F-measures of all the schemes decrease as \(k\) decreases, since recall decreases as the number of detectable events decreases. Given that TBEM cannot detect regional events as it only utilizes text mining techniques, performance results are very low as \(k\) decreases. Although TBEM and OnlyTweet can provide the top 20 meaningful events, most of the results did not involve \"local\" events since it is hard to extract regional information precisely. As a result, the proposed scheme significantly improves the performance of the local event detection in terms of precision, recall, and F-measure.