A Deep Multi-View Learning Framework for City Event Extraction from Twitter Data Streams
Table 7: Similarity analysis on London2 dataset: different Twitter class distributions compared against the ground-truth traffic sensor driven data distribution (first row), different Twitter class distributions compared against the ground-truth sociocultural data parsed form TimeOut London (second row).
['Road Rep.', '[ITALIC] μD± [ITALIC] σ', 'Crime 1.00 ± 8.79', 'Cultural 1.00 ± 3.67', 'Food 0.95 ± 5.14', 'Social 0.90 ± 3.74', 'Sport 1.69 ± 8.72', 'Weather 1.72 ± 8.21', 'Trans.   [BOLD] 0.74 ± 2.34']
The comparison results are reported in terms of classes average distance (represented with μ) from their nearest authority (web) record which was described in sec. To compute the similarities from the average distance values, we have first rescaled the averaged distances by the within-class maximum average distance (sport class distance) and then subtracted the result from one. Doing so, the similarity values are confined to be between 0 and 1 where closer to one values will guarantee higher similarity and values close to zero show a higher level of dissimilarity. First row results in The results showed that the smallest average distance of 0.74, considering the variance intervals, correspond to the Traffic events, which is a proof of an acceptable tweet classification performance. Additionally, a comparison of Twitter traffic report times with their nearest neighbour authority traffic record time-stamp, showed that 49.5% of the Twitter traffic alerts are reported on average 297.5 minutes. This is approximately 5 hours prior to the authority’s official reports. This finding highlights the advantage of utilising the social media, particularly Twitter driven knowledge in facilitating and speeding up the city traffic management and potentially smoothing the task-handling. Second row results in In our experiments, we discovered higher similarity measurements for traffic, cultural and social tweet event classes with 1.73, 2.95 and 2.00 respective average distance values. This, in fact, proves that the popular and well-advertised cultural activities are potential high-traffic zones. It is important to point out that the Time Out London sociocultural event set does not separate the social events, such as special events held in pubs and restaurants, from cultural events (i.e. exhibitions and ceremonies) while our proposed pipeline labels these events differently.