No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Predicting Social Ties in Massively Multiplayer Online Games
Abstract
Social media has allowed researchers to induce large social networks from easily accessible online data. However, relationships inferred from social media data may not always reflect the true underlying relationship. The main question of this work is: How does the public social network reflect the private social network? We begin to address this question by studying interactions between players in a Massively Multiplayer Online Game. We trained a number of classifiers to predict the social ties between players using data on public forum posts, private messages exchanged between players, and their relationship information. Results show that using public interaction knowledge significantly improves the prediction of social ties between two players and including a richer set of information on their relationship further improves this prediction.
... These artificial intelligence/machine learning AI/ML developments, com-bined with many items in the MITRE report of results of DoD's earlier Human, Social and Culture Behavior Modeling (HSCB) program (Egeth et al., 2014), are highly relevant. Table 6.1 shows some of the chapters and their emphasis (Jima and Lakkaraju, 2014). ...
... Some applications have been attempted in more social-behavioral settings, such as research by RAND and Lawrence Livermore National Laboratory on trader be hav iors in the security industry (Dreyer et al., 2016), and in the analy sis of data from massively multiplayer online games (Jima and Lakkaraju, 2014). Other applications of agentbased modeling in electric-power networks are connecting phenomena at diff er ent scales, something of considerable interest to economists as well as regulators (Tesfatsion, 2018). ...
... There is no mention about extract humans interests from Geotagged photo logs and GPS logs. Few studies intro- duced an effect of GPS logs to identify humans relationship 10,11,12 . There are no studies about Geotagged photos and their logs for the identification of humans relationship. ...
Geotagged photos carry hidden data about the surrounding area, and the owner of the photo. Moreover; Geotagged photos have background information about the user, where the alternative resources of Geo-spatial data lack background information. In this study, we propose identification for the circles of relations of the smartphone user from Geotagged photos. The proposed solution mainly depends on a framework, which is based on smartphone photo gallery. The framework extracts a degree of relation between smartphone user and circles of relations entities. Circles of relations incorporate closest people, places, where the participant visits, and interests. The circles of relations are represented in a social graph, which shows the clusters of social relations and interests of smartphone user. The social graph clarifies the nature and the degree of the relations for the participants. The results of framework introduced the relation between the level of variety of participant social relations, and the degree of relations.
There is a wealth of psychological theory regarding the drive for individuals to congregate and form social groups, positing that people may organize out of fear, social pressure, or even to manage their self-esteem. We evaluate three such theories for multi-scale validity by studying them not only at the individual scale for which they were originally developed, but also for applicability to group interactions and behavior. We implement this multi-scale analysis using a dataset of communications and group membership derived from a long-running online game, matching the intent behind the theories to quantitative measures that describe players’ behavior. Once we establish that the theories hold for the dataset, we increase the scope to test the theories at the higher scale of group interactions. Despite being formulated to describe individual cognition and motivation, we show that some group dynamics theories hold at the higher level of group cognition and can effectively describe the behavior of joint decision making and higher-level interactions.
Two of the difficulties for data analysts of online social networks are (1) the public availability of data and (2) respecting the privacy of the users. One possible solution to both of these problems is to use synthetically generated data. However, this presents a series of challenges related to generating a realistic dataset in terms of topologies, attribute values, communities, data distributions, correlations and so on. In the following work, we present and validate an approach for populating a graph topology with synthetic data which approximates an online social network. The empirical tests confirm that our approach generates a dataset which is both diverse and with a good fit to the target requirements, with a realistic modeling of noise and fitting to communities. A good match is obtained between the generated data and the target profiles and distributions, which is competitive with other state of the art methods. The data generator is also highly configurable, with a sophisticated control parameter set for different “similarity/diversity” levels.
http://link.springer.com/article/10.1007/s13278-016-0352-y
Massively-multiplayer online games (MMOGs) can serve as a unique laboratory for studying large-scale human behaviors. However, one question that often arises is whether the observed behavior is specific to the game world and its winning conditions. This paper studies the nature of conflict and communication across two game worlds that have different game objectives. We compare and contrast the structure of attack networks with trade and communication networks. Similar to real-life, social structures play a significant role in the likelihood of inter-player conflict.
In collaborative tagging systems, it is generally assumed that users assign tags to facilitate retrieval of content at a later time. There is, however, little behavioral evidence that tags actually serve this purpose. Using a large-scale dataset from the social music website Last.fm, we explore how patterns of music tagging and subsequent listening interact to determine if there exist measurable signals of tags functioning as retrieval aids. Specifically, we describe our methods for testing if the assignment of a tag tends to lead to an increase in listening behavior. Results suggest that tagging, on average, leads to only very small increases in listening rates, and overall the data do not support the assumption that tags generally serve as retrieval aids.
This article proposes an empirical test of whether aggregate economic behavior maps from the real to the virtual. Transaction data from a large commercial virtual world — the first such data set provided to outside researchers — is used to calculate metrics for production, consumption and money supply based on real-world definitions. Movements in these metrics over time were examined for consistency with common theories of macroeconomic change. The results indicated that virtual economic behavior follows real-world patterns. Moreover, a natural experiment occurred, in that a new version of the virtual world with the same rules came online during the study. The new world's macroeconomic aggregates quickly grew to be nearly exact replicas of those of the existing worlds, suggesting that `Code is Law': macroeconomic outcomes in a virtual world may be explained largely by design structure.
Social media treats all users the same: trusted friend or total stranger, with little or nothing in between. In reality, rela- tionships fall everywhere along this spectrum, a topic social science has investigated for decades under the theme of tie strength. Our work bridges this gap between theory and practice. In this paper, we present a predictive model that maps social media data to tie strength. The model builds on a dataset of over 2,000 social media ties and performs quite well, distinguishing between strong and weak ties with over 85% accuracy. We complement these quantitative findings with interviews that unpack the relationships we could not predict. The paper concludes by illustrating how modeling tie strength can improve social media design elements, in- cluding privacy controls, message routing, friend introduc- tions and information prioritization.
Personality inference can be used for dynamic personalization of content or system customization. In this study, we examined whether and how personality is expressed in Virtual Worlds (VWs). Survey data from 1,040 World of Warcraft players containing demographic and personality variables was paired with their VW behavioral metrics over a four-month period. Many behavioral cues in VWs were found to be related to personality. For example, Extraverts prefer group activities over solo activities. We also found that these behavioral indicators can be used to infer a player's personality.
The caret package, short for classification and regression training, contains numerous tools for developing predictive models using the rich set of models available in R. The package focuses on simplifying model training and tuning across a wide variety of modeling techniques. It also includes methods for pre-processing training data, calculating variable importance, and model visualizations. An example from computational chemistry is used to illustrate the functionality on a real data set and to benchmark the benefits of parallel processing with several types of models.
For social networks, prediction of new links or edges can be important for many reasons, in particular for understanding future network growth. Recent work has shown that graph vertex similarity measures are good at predicting graph link formation for the near future, but are less effective in predicting further out. This could imply that recent links can be more important than older links in link prediction. To see if this is indeed the case, we apply a new relation strength similarity (RSS) measure on a coauthorship network constructed from a subset of the CiteSeerX
dataset to study the power of recency. We choose RSS because it is one of the few similarity measures designed for weighted networks and easily models FOAF networks. By assigning different weights to the links according to authors coauthoring history, we show that recency is helpful in predicting the formation of new links.
This volume provides an overview of research methods in contemporary
social psychology. Coverage includes conceptual issues in research
design, methods of research, and statistical approaches. Because the
range of research methods available for social psychology have expanded
extensively in the past decade, both traditional and innovative methods
are presented. The goal is to introduce new and established researchers
alike to new methodological developments in the field.
We study online social networks in which relationships can be either positive (indicating relations such as friendship) or negative (indicating relations such as opposition or antagonism). Such a mix of positive and negative links arise in a variety of online settings; we study datasets from Epinions, Slashdot and Wikipedia. We find that the signs of links in the underlying social networks can be predicted with high accuracy, using models that generalize across this diverse range of sites. These models provide insight into some of the fundamental principles that drive the formation of signed links in networks, shedding light on theories of balance and status from social psychology; they also suggest social computing applications by which the attitude of one user toward another can be estimated from evidence provided by their relationships with other members of the surrounding social network.
Given a snapshot of a social network, can we infer which new interactions among its members are likely to occur in the near future? We formalize this question as the link prediction problem, and develop approaches to link prediction based on measures for analyzing the "proximity" of nodes in a network. Experiments on large co-authorship networks suggest that information about future interactions can be extracted from network topology alone, and that fairly subtle measures for detecting node proximity can outperform more direct measures.