Abstract-In the early twenty-first century, social networks served only to let the world know our tastes, share our photos and share some thoughts. A decade later, these services are filled with an enormous amount of information. Now, the industry and the academia are exploring this information, in order to extract implicit patterns. TwitterJam is a tool that analyses the contents of the social network Twitter to extract events related to road traffic. To reach this goal, we started by analysing tweets to know those which really contains road traffic information. The second step was to gather official information to confirm the extracted information. With these two types of information (official and general), we correlated them in order to verify the credibility of public tweets. The correlation between the two types of information was done separately in two ways: the first one concerns the amount of tweets in a certain time of day and the second on the localization of these tweets. Two hypothesis were also devised concerning these correlations. The results were not perfect but where reasonable enough. We also analysed tools suitable for the visualization of data to decide what is the best strategy to follow. At the end we developed a web application that shows the results, to help the analysis of results.
On the other hand, Rebelo et al. REF implemented a system capable of extract and analyse events related to road traffic, coined TwitterJam.
TwitterJam: Identification of mobility patterns in urban centers based on tweets
2015 IEEE First International Smart Cities Conference (ISC2)
2015 IEEE First International Smart Cities Conference (ISC2)
Computer Science