Xiaochen Li

Agent-Based Social Simulation and Modeling in Social Computing

Agent-based social simulation (ABSS) as a main computational approach to social simulation has attracted increasing attention in the field of social computing. With the development of computer and information technologies, many new ABSS approaches have been proposed with wide application.. In this paper, we aim at reviewing research and applications of agent-based social simulation and modeling in recent years from a social computing perspective. We identify the underlying social theories for ABSS, its simulation and modeling techniques, and computational frameworks from both individual agent and multi-agent system perspective. We finally address some future research issues in agent-based social simulation and modeling.

Automatic construction of domain theory for attack planning

Terrorism organizations are devising increasingly sophisticated plans to conduct attacks. The ability of emulating or constructing attack plans by potential terrorists can help us understand the intents and motivation behind terrorism activities. A feasible computational method to construct plans is planning technique in AI. Traditionally, AI planning methods rely on a predefined domain theory which is compiled by domain experts manually. To facilitate domain theory construction and plan generation, we propose a method to construct domain theory automatically from free text data. The effectiveness of our proposed approach is evaluated empirically through experimental studies using real world terrorist plans .

Probabilistic Plan Inference for Group Behavior Prediction

A probabilistic plan inference approach explicitly takes an observed agents preferences into consideration and computes expected plan utilities to disambiguate competing hypotheses.

Agent-based modeling of netizen groups in chinese internet events

Internet events are public events with the participation of netizens to express their opinions or comments. As an emerging phenomenon, Internet events often draw nationwide attention and eventually influence offline events. Netizen groups who participate in the Internet events play a central role in such events. In this paper, we focus on the study of netizen groups and propose an agent-based model to capture their dynamics and evolvement in Internet events. Our experiment is based on two case studies of Chinese Internet events. We test the proposed model by running simulations and comparing experimental results with real social media data to show the effectiveness of our model.

Performance evaluation of classification methods in cultural modeling

Cultural modeling is an emergent and promising research area in social computing. It aims to develop behavioral models of groups and analyze the impact of culture factors on group behavior using computational methods. Classification methods play a critical role in cultural modeling domain. As various cultural-related datasets possess different properties, for group behavior prediction, it is important to gain a computational understanding of the performance of various classification methods. In this paper, we investigate the performance of seven representative classification algorithms using a benchmark cultural modeling dataset and analyze the experimental results.

Handling Class Imbalance Problem in Cultural Modeling

Cultural modeling is an emergent and promising research area in social computing. It aims at developing behavioral models of groups and analyzing the impact of culture factors on group behavior using computational methods. Machine learning methods in particular classification, play a central role in such applications. In cultural modeling, it is expected that classifiers yield good performance. However, the performance of standard classifiers is often severely hindered in practice due to the imbalanced distribution of class in cultural data. In this paper, we identify class imbalance problem in cultural modeling domain. To handle the problem, we propose a user involved solution employing the receiver operating characteristic (ROC) analysis for classification algorithms with sampling approaches. Finally, we conduct experiment to verify the effectiveness of the proposed solution.

Forecasting complex group behavior via multiple plan recognition

Group behavior forecasting is an emergent research and application field in social computing. Most of the existing group behavior forecasting methods have heavily relied on structured data which is usually hard to obtain. To ease the heavy reliance on structured data, in this paper, we propose a computational approach based on the recognition of multiple plans/intentions underlying group behavior.We further conduct human experiment to empirically evaluate the effectiveness of our proposed approach.

From Causal Scenarios to Social Causality: An Attributional Approach

Inspired by the psychological attribution theory, this article presents a computational approach to construct causal scenarios and facilitate social causality studies based on online textual data.

A bottom-up method for constructing topic hierarchies

In security-related applications, it is of great need to construct topic hierarchies from the data automatically. It can help us better understand the contents and structure of information and benefit many applications in security informatics. The existing topic hierarchy construction methods either need to specify the structure manually, or are not robust enough for sparse social media data such as microblog. In this paper, we propose an approach to automatically construct topic hierarchies from microblog data in a bottom up manner. We first detect the fine grained topics and then build the topic structure based on a multi-branch hierarchical construction method. We conduct a preliminary empirical study based on Weibo data. The experimental results show that the hierarchical topic structure generated by our method can provide meaningful results.

An ACP-Based Approach to Intelligence and Security Informatics

The field of Intelligence and security informatics (ISI) is resulted from the integration and development of advanced information technologies, systems, algorithms, and databases for international, national, and homeland security-related applications, through an integrated technological, organizational, and policy-based approach. Traditionally, ISI research and applications have focused on information sharing and data mining, social network analysis, infrastructure protection, and emergency responses for security informatics. Recent years, with the continuous advance of related technologies and the increasing sophistication of national and international security, new directions in ISI research and applications have emerged that address the research challenges with advanced technologies, especially the advancements in social computing. This is the focus of discussion in the current chapter.