# A Survey on Troll Detection
## 2 Social Media and the Trolling Phenomenon
### Coping with Trolls  
As a result of the complexity of modern social media, identifying and banning trolls is a daunting task. It is important to make users aware of trolling behaviours, but these kinds of warnings do not reduce the phenomenon. A common way to neutralize a troll is to avoid speaking to him; to this purpose, expert users advise novices not to \"feed\" him (\"Do not feed the troll!\"). Usually, online communities comprise a special group of users known as moderators, who have the ability and responsibility to ban those users who are considered malicious. The main benefit of this entirely supervised approach is the possibility to determine if a user is a troll, based on his actions or his comments on the network. This solution has two major drawbacks: subjectivity and scalability. A supervised system can be reliable in medium-sized networks or in small ones, but does not scale to social networks with a large number of members and contents. Some of these communities have tried to overcome these limitations by delegating some control functions to its users. To do so, they have been provided with a mechanism to point out offensive contents or behaviours to moderators. Subsequently, it is up to moderators to determine if the reported user is a troll or not, or if a reported content has been produced by a troll or it was simply a controversial response. In addition, users may be given the opportunity to express their own opinion on others or on their comments. Other examples come from sites like Twitter or Facebook, which allow users to report a tweet or a post as inappropriate and then they automatically remove it, once it reaches a certain number of reports.  
These decentralized approaches reduce the problem of subjectivity, because the decisions are delegated to all network users, and the one of scalability, since there is no authority that operates as a controller [28]. However, even by assigning the control task to different persons, it is possible to come upon other limitations generated by the \"manual\" countermeasure taken in response to the problem. This happens because, generally, the community cannot manage to react instantaneously to the report and the result may be too slow; consequently, the users are not protected from being emotionally hurt by a troll. In view of all these things, it is possible to claim that the best solution is to automatize the process by implementing a troll detection algorithm. Historically, when trolling was still unknown and had not been studied in detail yet, the researchers' focus was on a phenomenon already known and similar to the case taken into exam: the so-called \"fake\", that occurs when a user falsifies his identity.  
Occasionally, this kind of user turns out to be a troll [21]. In fact, the ability to create an online _alter ego_ allows the trolls to publish news, reviews or multimedia material intended to discredit or attack other people that may or may not be aware of the attack. Starting from these observations, still mostly valid, new anti-trolling methods have been implemented over the years, mainly consisting in the identification of accounts that use the same IP address and in blocking the fake ones. These methods are based on spotting names and unusual activities like, for example, the dispatch of many messages to non-friend users or the high rate of declined friendship requests [22] More recently, the solutions considered to solve the troll detection problem rely on artificial intelligence and data mining approaches, ranging from sentiment analysis to social network analysis [28].