# A Survey on Troll Detection
## 1 Introduction  
The extension and pervasiveness that the Internet has reached in recent years has led to the emergence of many platforms specialized in communication services. Social media have been adopted in many different countries by the public [1] as well as by companies [2; 3; 4] In addition, \"being social\", in contrast to \"being a troll\", has been shown to be very important for the quality of human interaction in the digital sphere; this attitude can be assessed in different ways [5; 6; 7].  
A troll is an individual with an antisocial behaviour that incites other users acting within the same social network [8]. In particular, a troll often uses an aggressive or offensive language and has the purpose to slow down the normal evolution of an online discussion and possibly to interrupt it [9]. Only recently has it been possible to pay proper attention to this problem, so that many renowned press bodies and magazines have started to address the issue and to write articles both on the general description of the phenomenon and on particular events that have caused a stir, favoured by the increasing occurrence of behaviours like the one described above.  
This kind of behaviour is not fully characterised and, up to now, it has been difficult to find an accurate description for the word \"troll\", since the act of trolling is strongly subjective. The lack of an agreed-on definition for the term \"troll\" has resulted in poor comprehension and in low interest for the research community. The need for dealing with this problem has therefore emerged over time, along with studies conducted by several universities and research centres.  
After removing applications which are not strictly related to the main topics taken into consideration (social sciences, computer science and engineering), Scopus, as of 4 February 2020, lists 636 papers having the term \"troll\" in the title or abstract or as a keyword, when limiting the search to those three subject areas, 401 of which are related with the two latter topics, and 192 only to \"computer science\". Adding the keyword \"detection\" brings the total down to 51 papers, whose distribution in time shows a clear increment after 2015. Even when limiting the search to this rather narrow \"topic\",it is quite clear that the recent interest in this kind of application has been stimulated by recent events having worldwide resonance. For instance, the hype arisen by the alleged influence of Russia in the latest United States presidential elections has shown that malicious behaviours in social networks are not only a \"local\" menace within limited groups of users of a social network, but can assume world-wide dimensions, up to affecting world politics [10; 11; 12; 13].  
This article presents a short introduction to the problem of trolls and to their detection. Its goal is by no means to provide an exhaustive overview of the approaches to troll detection but to list and discuss a number of paradigmatic examples which can be a good starting point for those who are interested in joining the increasing community of researchers on this topic and providing their own contribution. As such, it has not been originated by a systematic and quantitative \"search and refine\" process. It is rather the result of a top-down incremental browsing of the literature, starting from seminal papers and iteratively following new paths according to the suggestions and discoveries of the newly accessed ones. Substantially, it reflects and expands the strictly application-oriented analysis of the state-of-the-art we performed in the first stage of development of [14].  
The paper is organised as follows: the next section presents the trolling phenomenon. Section 2 discusses the main methods used to detect trolls. Finally, Section 4 concludes the article by discussing the most relevant open problems with troll detection and by suggesting directions for future work.