Analysing Social Metrics in an Online Game Site

Understanding real-time coordination behaviour around multiplayer games is important as it allows designers to make informed decisions about supporting player communities. However, studying existing sites is difficult because of the amounts and range of data involved. In this paper, we argue for using social accounting metrics to investigate large game sites. We demonstrate the use of these metrics to explore the social coordination behaviour of the game site PlayOK, and gain a highlevel view of social activity in the game site. Keywords Games, analysis, social metrics. 1. INTRODUCTION There are many popular online multiplayer game sites ranging from reproductions of board and card games – e.g. PlayOK (www.playok.com) – to rich 3D worlds such as World of Warcraft (WoW). These game sites have large numbers of people interacting and gaming together. They are an ideal setting to study meta-game coordination behaviour. However, it is difficult to understand this real time coordination behaviour in these sites because the sites are very large, often with millions of people and activity throughout the day. The wealth of data that results makes them valuable for study but also makes them difficult to investigate. It is important to understand social coordination activity in these game sites. Deeper understanding enables designers to make informed decisions when supporting game communities. Our goal was to take a broad initial look at how people behave in the game site PlayOK; in particular the real time meta-game coordination to establish and support gameplay. PlayOK is a very large game site that supports 38 different types of multiplayer games and has over 5 million registered users. There are various approaches to understanding the social nature of virtual sites. Some sacrifice breadth of understanding and use methods such as questionnaires (e.g. [1,10]) or observations (e.g. [8]) to get a very deep and rich understanding of certain aspects. Others, such as Social Network Analysis [9], computationally process large amounts of data to produce summaries of community structure. Social accounting metrics, as in [2,5], make use of simple statistical summaries of activity data to gain insight into large-scale behaviour. In this paper, we demonstrate how social accounting techniques can be used to gain a broad understanding of the meta-game activity throughout a game site. We investigate the PlayOK game site using three themes that are frequently considered in work about online communities. These are: permanence, the idea that social groups benefit when people are long-term residents of a place (virtual or real) [1]; social interaction, which has been primarily considered in terms of verbal communication, but which can also include shared activity [3]; and forming ties, in which people meet others, associate, and make lasting connections (strong or weak) [3]. 2. PREVIOUS WORK There are many methods for analysing community and social behaviour in virtual settings, both in games and in other online interactive settings. 2.1 Humanities Inspired Methods Many methods for studying virtual settings are directly inspired by fields such as psychology, sociology and anthropology, which have years of experience in investigating social structures. A notable example is Blanchard’s measure for ‘sense of virtual community’ [1], which derives from the physical world ‘sense of community’ psychological measures. The measure is a questionnaire for members of an online group to test for the presence of a community. Surveys tailored to particular questions are also often used, such as in Yee’s study of player types [10]. Participant-observation techniques have been used in most studies of social interaction in multiplayer role playing games. These include studies of Multi-User Dungeons (MUDs) (e.g. [7]) and Massively Multiplayer Online Games (MMOGs) (e.g. [4,8]). Social interaction insights are based on observations of researchers who play the games. The strength of this broad category of technique is that researchers gain a deep understanding of some aspects of the community. The weakness is that, while the understanding gained is deep, it is not very broad. Typical game site populations are large and so questionnaires and surveys can only be applied to a small proportion of the population. Similarly, an observer can only see a small part of the activity. 2.2 Computational Methods The most prominent method of this category is Social Network Analysis (SNA) (see [9] for a full treatment). In brief, SNA is a collection of algorithms for extracting structural information from social network graphs. Graph nodes are typically people in the social network, and the weighted edges are based on measures of relationships between members, such as number of chat messages exchanged. SNA algorithms can be used to discover features of interest such as communities (groups that interact more frequently with each other), people that are central to communities, people that are connectors between different communities, and many other properties of interest. The strength of SNA is that it provides insight into the large scale structure of the community, especially subgroups. Analysis takes into account data from the whole virtual setting. A weakness is that SNA techniques rely on a well-connected social network. The PlayOK social network was too sparse and disconnected. Copyright is held by the author/owner(s). GRAND 2012, May 2-4, 2012,Montréal, QC, Canada 2.3 Social Accounting Metrics Social accounting metrics use low-level traces of activity – such as logging events – reconstruct community behaviour. This is a very broad category of analysis and there is a lot of research that would qualify. We will use two notable examples to illustrate the qualities of our study. Brush et al [2] used social accounting metrics to study Usenet. The main difference with our investigation is that in Usenet the primary purpose is to communicate directly with text. We are studying a game system where there are multiple means to interact – such as chatting or playing – and the primary purpose is gaming. In addition, the interactions in the game site are real-time, while Usenet is (mostly) asynchronous. The second defining example is Ducheneaut et al’s [5] study of grouping behaviour in World of Warcraft (WoW). While grouping behaviour is of interest to us, we are concerned with how game groups are negotiated and coordinated. WoW is a 3D Massively Multiplayer Online Game, and is a fundamentally different experience than PlayOK. We expect to observe correspondingly different community behaviour. We chose this style of analysis because it allows us to target aspects of interest: specifically real-time communication and coordination around the gameplay. 3. METHODOLOGY Our results are based on analysis of three months of system logs from the online gaming site PlayOK. The logs contained events such as logins, games and chat messages (Table 1). 3.1 The Game Site: PlayOK PlayOK was established in Poland in 2001 and has grown steadily, with 5.2 million unique accounts as of June 2010 (accounts are removed after one year of inactivity). PlayOK is a web-based game site that offers 38 different game types, including board games such as Chess and Backgammon, card games such as Hearts and Canasta, and other games such as Dominoes and Dice. All analysed games are player-vs-player only. Free registration is required to play and the site is ad supported. The games are partitioned by language and region, so not all games are available to every person. Figure 1: A Bridge room: player list at right, table list at left, and room chat at bottom left. After logging in and selecting a game type, the player is presented with a list of rooms. Selecting a room starts a java applet in a new window (Figure 1). In this window are a list of active game tables, a list of other people in the room, and text chat. A game table is a virtual area for a single game, and each room can contain many game tables. Users can enter game tables currently in progress or create new tables. Figure 2: Bridge game table: player information at top right, chat area at bottom right. Entering a game table opens a new window (Figure 2) with a view of the game, a list of people at the table, the names of the players (or empty places if the game is not full), and a chat area. If there is space at the game, and the game has not started, users can ‘sit down’ and become players. 3.2 Log Files The PlayOK administrator made daily log files available to the authors. Our data covers 8 April 2010 to 8 July 2010 (91 days) and consists of ~12GB of compressed text files. Table 1 summarises the structure of the events recorded in the log files. Table 1: Log event types (all include a timestamp) Login User ID, Language, Contacts (list of user IDs), Registration date, #people on the game type Logout User ID, Time logged in, #people in the game type Room Chat Sender ID, Room name Table Chat Sender ID, Room name + Table number Private Chat Sender ID, Receiver ID Join Room User ID, Room Name, #people in the room Leave Room User ID, Room Name, #people in the room Join Table User ID, Room name, Table number, #people at table Leave Table User ID, Room name, Table number, #people at table Invite Inviter ID, Invitee ID, Table number Game Start Room name, Table number, Player IDs (w/ rankings) Game End Room name, Table number 4. PLAYOK SOCIAL DYNAMICS Our analyses looked at three themes frequently used to describe communities and social groups – permanence, social interaction, and formation of ties. Our goal was to take a broad initial look at how people behave in PlayOK. Figure 3: Game types by number of games and chat messages. Size shows population. Selected games are black and labeled. For each theme, we identified specific questions that can be answered through our log-based analyses. In some charts that accompany our analyses, we show a subset of games