Automatic analysis and identification of verbal aggression and abusive behaviors for online social gamesby Koray Balci, Albert Ali Salah

Computers in Human Behavior

About

Year
2014
DOI
10.1016/j.chb.2014.10.025
Subject
Psychology (all) / Human-Computer Interaction

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Chat analysis

Cyberbullying

Machine learning crea hat gam orti ting social activities in the game are taken into account, as well as player history. This methodology is nteract ocial r world. edia, ehavio game for a better understanding of the players themselves, for particularly whether violent games induce aggression in children or not (Egenfeldt-Nielsen, Smith, & Tosca, 2013; Griffiths, 1999).

However, little research has been done on aggressive behaviors within computer games.We do not deal here with the controversial issues of violent games (Ferguson, 2013). We distinguish here

In addition to verbal messages, we explore in this work a numg in social online achine le ive verbal line game on actual player complaints. While mechanisms for ha player complaints exist in most social games, game mod need to spend time and energy to analyze player complaints to resolve each case individually.1 Subsequently, labeled data are ⇑ Corresponding author at: Aytar Cad. No: 20 D: 12 Levent, Besiktas, 34340

Istanbul, Turkey. Tel.: +90 533 938 66 77; fax: +90 212 287 11 91.

E-mail address: balci@cmpe.boun.edu.tr (K. Balci). 1 One of the bigger Okey sites, run by MyNet (https://apps.facebook.com/canakokey/) has over 1 million monthly active users, and reported receiving about 40 player complaints per hour on the average. Four full-time staff members are hired to deal with these complaints.

Computers in Human Behavior xxx (2014) xxx–xxx

Contents lists available at ScienceDirect

Computers in Human Behavior eviinstance by inferring personality traits from in-game behavior (van Lankveld, Spronck, van den Herik, & Arntz, 2011).

There is a significant body of work that investigates the effects of aggressive and violent content in computer games on the players, ber of features that can be used for player profilin games. In particular, we use a supervised m approach to create models of abusive and aggress ior from labeled instances of abuse in such an onhttp://dx.doi.org/10.1016/j.chb.2014.10.025 0747-5632/ 2014 Elsevier Ltd. All rights reserved.

Please cite this article in press as: Balci, K., & Salah, A. A. Automatic analysis and identification of verbal aggression and abusive behaviors for onlin games. Computers in Human Behavior (2014), http://dx.doi.org/10.1016/j.chb.2014.10.025arning behav, based ndling eratorsit is possible to record user actions, to create or to filter target interactions, and to obtain contextualized behavior instances. With the help of these data, one can either improve the game experience, by for instance adapting the game to maximize player enjoyment (Asteriadis, Shaker, Karpouzis, & Yannakakis, 2012), or use the private messaging, gifting (i.e. sending a virtual gift to another player), message boards, friendship and alliance requests, and such.

Rapid identification and resolution of verbal aggression over these channels is important for the gaming community. For this purpose, the content of verbal messages should be analyzed automatically.1. Introduction

Online social games provide rich i users, and createmicro-worldswith s not completely overlapwith the real and interactions happen over digitalm great opportunities to analyze user bsufficiently generic, and it can be applied to similar gaming platforms, thus describing a useful tool for game companies. We report our results on data collected over a six months period, involving 100,000 users and 800,000 game records, and illustrate the viability of such analysis, while providing insights on the factors associated with verbal aggression and abusive behavior for social games.  2014 Elsevier Ltd. All rights reserved. ion possibilities to their ules that parallel, but do

Sincemost transactions these platforms present r. In online social games avatar aggression, which involves aggression displayed by the virtual characters of a game, from player aggression, which implicates the actual player as the target of aggression. The latter is a form of cyber-aggression, and is often disruptive for gaming experience. In this paper, we deal specifically with verbal player aggression via in-game communication channels. Most social online games provide several communication channels, including in-game chat,Verbal aggression

Abusive behavior in the context of a very popular online social game, called Okey. Our approach relies on the analysis of player behavior and characteristics of offending players. In the proposed system, chat records and otherAutomatic analysis and identification of behaviors for online social games

Koray Balci ⇑, Albert Ali Salah

Computer Engineering Department, Bogazici University, Istanbul, Turkey a r t i c l e i n f o

Article history:

Available online xxxx

Keywords:

Online social games

Sociability a b s t r a c t

Online multiplayer games and ways of expression. W platform, but most online

This usually is tied to a rep we develop tools for valida journal homepage: www.elsrbal aggression and abusive te new social platforms, with their own etiquette, social rules of conduct counts as aggressive and abusing behavior may change depending on the ing companies need to deal with aggressive and abusive players explicitly. ng mechanism where the offended player reports an offense. In this paper, whether a verbal aggression offense report refers to a real offense or not, er .com/locate /comphumbehe social

Humcostly to obtain. We introduce here a labeled corpus for this purpose.

Our study aims to improve the game experience indirectly, by automatically analyzing player complaints, and thus helping game moderators to respond to aggressive and abusive behaviors in the game. At the same time, our analysis may contribute to a better understanding of the factors that underlie such behaviors.

Our approach is based on the analysis of player complaints, player behavior, and player characteristics, including demographic data, game play statistics, and similar features of player history.

The social interactions we analyze include chatting, as well as ingame friendship, offline messaging, and gifting. Our profiling methodology performs with a small number of false positives, and is now being incorporated into an actual game environment.