We describe the dimensions along which Facebook users tend to express themselves via status updates using the semi-automated text analysis approach, the Meaning Extraction Method (MEM). First, we examined dimensions of self-expression in all status updates from a sample of four million Facebook users from four English-speaking countries (the United States, Canada, the United Kingdom, and Australia) in order to examine how these countries vary in their self-expressions. All four countries showed a basic three-component structure, indicating that the medium is a stronger influence than country characteristics or demographics on how people use Facebook status updates. In each country, people vary in terms of the extent to which they use Informal Speech, share Positive Events, and discuss School in their Facebook status updates. Together, these factors tell us how users differ in their self-expression, and thus illustrate meaningful use cases for the product: Talking about what’s going on tends to be positive, and people vary in terms of the extent to which their status updates are short, slangy emotional expressions and topics regarding school. The specific words that define these factors showed subtle differences across countries: The use of profanity indicates fewer school words (but only in Australia), whereas the UK shows greater use of slang terms (rather than profanity) when speaking informally. The MEM also identified English-language dialects as a meaningful dimension along which the countries varied. In sum, beyond simply indicating topicality of posts, this study provides insight into how status updates are used for self-expression. We discuss several theoretical frameworks that could produce these results, and more broadly discuss the generation of theoretical frameworks from wholly empirical data (such as naturalistic Internet speech) using the MEM.

Microblogging websites such as Twitter are increasingly being used by businesses/campaigners for timely dissemination of information to their followers. The diffusion of a tweet depends on several factors: the activity of the follower nodes, the responsiveness of follower nodes to tweets from the source node, the out-degree of the follower nodes, the content of recent related tweets seen by the follower node, etc. Using such factors, in this paper, we propose a framework to measure the effectiveness of an information campaign over Twitter. We consider a positive as well as a negative metric to measure the impact of a tweet: while retweets are used to measure the positive impact, the lack of a timely response from an active follower node is taken as a potential negative impact. We investigate the scheduling of tweets to increase the net positive impact while keeping the net negative impact below a desired level. We propose and study several scheduling algorithms by casting the problem in a Markov Decision Process (MDP) framework. In order to compare our algorithms, we estimate the model parameters from tweet data collected using the Twitter API from an arbitrarily selected node and its 6837 followers over several months. For this dataset, we find that if successive tweets in the campaign are novel, then substantial gains over user activity based scheduling can be obtained by scheduling tweets in time slots where the ratio of the expected positive and negative metrics is high. We call this the MaxRatio policy and we show that it is optimal under certain conditions. In cases where we are not certain about the response of users to successive related tweets, we identify another algorithm (which we call MaxReach) as a robust alternative.

Gold farming and real money trade refer to a set of illicit practices in massively multiplayer online games (MMOGs) whereby players accumulate virtual resources to sell for “real world” money. Prior work has examined trade relationships formed by gold farmers but not the trust relationships which exist between members of these organizations. We adopt a hypergraph approach to model the multi-modal relationships of gold farmers granting other players permission to use and modify objects they own. We argue these permissions reflect underlying trust relationships which can be analyzed using network analysis methods. We compare farmers’ trust networks to the trust networks of both unidentified farmers and typical players. Our results demonstrate that gold farmers’ networks are different from trust networks of normal players whereby farmers trust highly-central non-farmer players but not each other. These findings have implications for augmenting detection methods and re-evaluating theories of clandestine behavior.

Real-time search is a well known approach to solving search problems under tight time constraints. Recently, it has been shown that LSS-LRTA∗ , a well-known real-time search algorithm, can be improved when search is actively guided away of depressions. In this paper we investigate whether or not RTAA∗ can be improved in the same manner. We propose aRTAA∗ and daRTAA∗ , two algorithms based on RTAA∗ that avoid heuristic depressions. Both algorithms outperform RTAA∗ on standard path-finding tasks, obtaining better-quality solutions when the same time deadline is imposed on the duration of the planning episode. We prove, in addition, that both algorithms have good theoretical properties.

Despite the prevalence of the Computational Theory of Mind and the Connectionist Model, the establishing of the key principles of the Cognitive Science are still controversy and inconclusive. This paper proposes the concept of PATTERN RECOGNITION as NECESSARY AND SUFFICIENT PRINCIPLE for a general cognitive science modeling, in a very ambitious scientific proposal. A formal physical definition of the pattern recognition concept is also proposed to solve many key conceptual gaps on the field.

We address the cost-sensitive feature acquisition problem, where misclassifying an instance is costly but the expected misclassification cost can be reduced by acquiring the values of the missing features. Because acquiring the features is costly as well, the objective is to acquire the right set of features so that the sum of the feature acquisition cost and misclassification cost is minimized. We describe the Value of Information Lattice (VOILA), an optimal and efficient feature subset acquisition framework. Unlike the common practice, which is to acquire features greedily, VOILA can reason with subsets of features. VOILA efficiently searches the space of possible feature subsets by discovering and exploiting conditional independence properties between the features and it reuses probabilistic inference computations to further speed up the process. Through empirical evaluation on five medical datasets, we show that the greedy strategy is often reluctant to acquire features, as it cannot forecast the benefit of acquiring multiple features in combination.

Real-time heuristic search is a standard approach to pathfind- ing when agents are required to make decisions in a bounded, very short period of time. An assumption usually made in the development and evaluation of real-time algorithms is that the environment is unknown. Nevertheless, in many interesting applications such as pathfinding for automnomous characters in video games, the environment is known in advance. Recent real-time search algorithms such as D LRTA* and kNN LRTA* exploit knowledge about the environment while pathfinding under real-time constraints. Key to those algorithms is the computation of subgoals in a preprocessing step. Subgoals are subsequently used in the online planning phase to obtain high-quality solutions. Preprocessing in those algorithms, however, requires significant computation. In this paper we propose a novel preprocessing algorithm that generates subgoals using a series of backward search episodes carried out from potential goals. The result of a single backward search episode is a tree of subgoals that we then use while planning online. We show the advantages of our approach over state-of-the-art algorithms by carrying out experiments on standard real-time search benchmarks.

A collection of landmarks is complete if the cost of a minimum-cost hitting set equals h + and there is a minimum-cost hitting set that is an optimal relaxed plan. We present an algorithm for generating a complete collection of landmarks and we show that this algorithm can be extended into effective polytime heuristics for optimal and satisficing planning. The new admissible heuristics are compared with current state-of-the-art heuristics for optimal planning on benchmark problems from the IPC.

State-of-the-art abstraction heuristics are those constructed by the merge-and-shrink approach in which an abstraction consists of a labeled transition system, and the composition of abstractions correspond to the synchronized product of transition systems. Merge-and-shrink heuristics build a composite abstraction from atomic abstractions that are directly associated with the variables of the planning problem. In this paper, we show that the framework of labeled transition systems is more general, and propose a new type of abstraction called the counting abstraction. Counting abstractions can be transparently combined with other type of abstractions to get more informative heuristics. We show how to effectively construct the counting abstractions and presents preliminary experiments over benchmark problems.

We present a software tool that is able to automatically translate an NP problem into a STRIPS problem such that the former problem has a solution iff the latter has one, a solution for the latter can be transformed into a solution for the former, and all this can be done efficiently. Moreover, the tool is built such that it only produces problems that belong to a fragment of STRIPS that is solvable in non-deterministic polynomial time, a fact that guarantees that the whole approach is not an overkill. This tool has interesting applications. For example, with the advancement of planning technology, it can be used as an off-the-shelf method to solve general NP problems with the help of planners and to automatically generate benchmark problems of known complexity in a systematic and controlled manner. Another interesting contribution is related to the area of Knowledge Engineering in which one of the goals is to devise automatic methods for using the available planning technology to solve real-life problems.