## 2 Related Works
Moreover, the large-scale distributed nature of WSN has fostered many proposals which consider the cooperation of multiple nodes to detect and follow particular event sources [30]. Indeed, when many sensors are deployed in near places, it is possible that a single event is detected by more than one node. Correctly exploited, this feature can be useful in many ways: first, it helps to prevent possible mistakes in single-node based detection because of measuring imprecisions or unavailability issues; second, it helps to enhance the estimation of the event source location; and, third, it helps to follow the dynamics of the event (movement, extension variations, etc.). Many works allowing event detection and location through WSN have been reported, considering multiple real-world issues, like resource constraints, measurement errors, and information unavailability (see, for example, works in [31; 32; 33]). Of course, those works can be implemented to successfully allow event detection, with some restrictions in the WSN implementation, like (in most of cases) a dense network topology, enough sensing range or other infrastructure features. In fact, in the existence of WSN infrastructures in Smart Cities, our work attempts to consider an additional layer of information provided by citizens, further improving event detection or even opening new possibilities in this area. Indeed, the many events reported by people (in multiple ways) using mobile devices can be automatically detected and classified for multiple uses, and this concept has been proposed as a potential tool for Smart Cities [34]. Social media have become popular in recent years [10]. Among them, _Twitter_ has received a lot of attention on the data mining field due to its real-time nature. In _Twitter_, users share an enormous amount of real-time data containing information about different types of everyday events (e.g., movie premiere, traffic jams, quality of a restaurant) in different contexts and viewpoints. Using data mining techniques, this information can be extracted and used to improve or create new products or services. For example, the work in [35] builds social network models to design more effective marketing plans. However, the real-time nature of _Twitter_ also poses challenges in the process of extracting information,such as the dynamic and increasing content and the high amount of trivial information flooding the data flow [36]. Some recent works have exploited _Twitter_ to provide relevant information. In [37], using the real-time (textual, spatial and temporal) information spontaneously provided by _Twitter_ users, earthquakes and their routes (including the epicenter) can be identified. In that real-life application, the system detected almost every earthquake identified by the traditional ways by just monitoring _tweets_ (_Twitter_ messages). In another work, tools were provided to analyze the status of public health (e.g., flu or insomnia statistics) and track the evolution illnesses over time [38]. In fact, some events are seasonal or linked to seasonal conditions (e.g., seasonal migration, and increasing of flu rates on cold months) and it follows a pattern (e.g., if flu rate increases in cold months, it decreases in hot months). Using _Twitter_, information were collected and the regularity of several events in a city was measured [39]. Comparing the new events with the reference events, possible unexpected events could be detected in that work. Other important topic surrounding the analysis of social network data is the targeted advertising. A user of a social network, while using it, submits lots of information to it such as age, visited places, favorite artists and preferred product brands. Usually a text is associated with the mentioned information, providing some value judgment or expectation upon a product, event or service (e.g., a post saying \"_My brother will graduate next week, can't wait for it!\"_ followed by the user location).