# A Survey on Troll Detection
## 3 Troll Detection Methods
### User-Based Methods  
Other approaches observe the problem from an even more general point of view, without being limited to the information contained in users' posts, and in their threads. They are able to deduce whether a user is a troll or not by considering the overall attitude of the user within the community. In fact, often, an individual enrolled in a particular service, expresses an anti-social behaviour in most of his/her activities within the network. In this case, the malicious actions are not sporadic and limited to particular topics, but they are the result of an _a priori_ attitude that characterises most of the user's online experience. Therefore, the conduct adopted by these users can be studied and catalogued in order to prevent similar attitudes, identifying them early and minimizing their effects. To this purpose, it is useful to study, analyse and characterise the conduct that trolls can take within the online community. The attention is not focused onto an individual post or onto the whole discussion thread, but onto the actions of malicious individuals and the trace of their behaviours throughout their life within the community. Consequently, the methods described in that study do not predict whether a particular comment is malicious or not, but they try to determine if a user takes a negative conduct, based on all his activities. These data mining methodologies, in addition to a general analysis on the behaviour of users, may use certain techniques based on the study of trolls at the comment (or thread) level, through sentiment analysis or cosine similarity method. In this sense, they are considered as hybrid approaches, according to the taxonomy of models presented in this research work. The need for integration of other metrics for the study of this problem has not been assessed empirically, but has emerged from some works available in the literature [31]. In fact, as demonstrated in the works previously described, the results of sentiment analysis must be merged with other more sensitive and specific metrics to ensure that the troll detection is effective. Otherwise, the analysis needs to be improved to take additional aspects into account. Based on these considerations and observing the online history of an individual, some researchers have focused their efforts onto the extraction of general information about the users. The aim has been to study the most significant parameters for the characterization of a troll and obtain a better perspective of their behaviours. In particular, [37] presents a study on anti-social behaviour in online discussion communities, focusing on those users that will eventually be banned. This leads to (_i_) a characterization of malicious users, (_ii_) an investigation on the evolution of their behaviour and (_iii_) the reactions taken by the community. In order to discern respectful users from malicious ones, researchers from Stanford and Cornell rely on the community and its moderators to see who they consider an individual with a malicious behaviour. Hence, they make some aggregate analyses about the users who will be permanently banned from the network, since these appear to be clear examples of users with an antisocial behaviour. This is therefore an approach on a large scale, and data-driven, that studies antisocial behaviour in three large communities (CNN.com, Breitbart.com and IGN.com). Over 18 months, more than 40 million posts have been collected, with the objective of obtaining quantitative specifications and to develop, as a result, tools for the early detection of trolls. The analysis focuses on the users subsequently banned by the moderators, defined as \"Future-Banned Users\" (FBUs), discriminating them against more respectful users, defined as \"Never-Banned Users\" (NBUs). By comparing the data of these two groups, one may notice that the language used in the posts by the two types of users is significantly different.