Modeling Trolling in Social Media Conversations

Determining Trolling in Textual Comments Luis Gerardo Mojica de la Vega and Vincent Ng Human Language Technology Research Institute University of Texas at Dallas Richardson, TX 75083-0688, USA {mojica,vince}@hlt.utdallas.edu Abstract Social media websites, electronic newspapers and Internet forums allow visitors to leave comments for others to read and interact. This exchange is not free from participants with malicious intentions, who troll others by positing messages that are intended to be provocative, offensive, or menacing. With the goal of facilitating the computational modeling of trolling, we propose a trolling categorization that is novel in the sense that it allows comment-based analysis from both the trolls’ and the responders’ perspectives, characterizing these two perspectives using four aspects, namely, the troll’s intention and his intention disclosure, as well as the responder’s interpretation of the troll’s intention and her response strategy. Using this categorization, we annotate and release a dataset containing excerpts of Reddit conversations involving suspected trolls and their interactions with other users. For the final version of the paper, we plan to identify the difficult-to-classify cases in our corpus and suggest potential solutions for them. Keywords: social media, text categorization, trolling, cyberbullying 1. Introduction In contrast to traditional content distribution channels like television, radio and newspapers, Internet opened the door for direct interaction between the content creator and its audience. Young people are now gaining more frequent access to online, networked media. Although most of the time, their Internet use is harmless, there are some risks as- sociated with these online activities, such as the use of so- cial networking sites (e.g., Twitter, Facebook, Reddit). The anonymity and freedom provided by social networks makes them vulnerable to threatening situations on the Web, such as trolling. Trolling is “the activity of posting messages via a com- munications network that are intended to be provocative, offensive or menacing” (Bishop, 2013). People who post such comments are known as trolls. According to Hardaker (2010), a troll’s “real intention(s) is/are to cause disruption and/or trigger or exacerbate conflict for the purpose of their own amusement”. Worse still, the troll’s comments may have a negative psychological impact on his target/victim and possibly others who participated in the same conver- sation. It is therefore imperative to identify such com- ments and perhaps even terminate the conversation before it evolves into something psychologically disruptive for the participants. Monitoring conversations is a labor-intensive task: it can potentially place a severe burden on the moder- ators, and it may not be an effective solution when traffic is heavy. This calls for the need to develop automatic methods for identifying malicious comments, which we will refer to as trolling attempts. In fact, there have recently been some attempts to automat- ically identify comments containing cyberbullying (e.g., Van Hee et al. (2015)), which corresponds to the most se- vere cases of trolling (Bishop, 2013). However, we believe that it is important not only to identify trolling attempts, but also comments that could have a negative psychological im- pact on their recipients. As an …

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