In this study, we use the computational textual analysis tool, the Gramulator, to identify and examine the distinctive linguistic features of deceptive and truthful discourse. The theme of the study is abortion rights and the deceptive texts are derived from a Devil’s Advocate approach, conducted to suppress personal beliefs and values. Our study takes the form of a contrastive corpus analysis, and produces systematic differences between truthful and deceptive personal accounts. Results suggest that deceivers employ a distancing strategy that is often associated with deceptive linguistic behavior. Ultimately, these deceivers struggle to adopt a truth perspective. Perhaps of most importance, our results indicate issues of concern with current deception detection theory and methodology. From a theoretical standpoint, our results question whether deceivers are deceiving at all or whether they are merely poorly expressing a rhetorical position, caused by being forced to speculate on a perceived proto-typical position. From a methodological standpoint, our results cause us to question the validity of deception corpora. Consequently, we propose new rigorous standards so as to better understand the subject matter of the deception field. Finally, we question the prevailing approach of abstract data measurement and call for future assessment to consider contextual lexical features. We conclude by suggesting a prudent approach to future research for fear that our eagerness to analyze and theorize may cause us to misidentify deception. After-all, successful deception, which is the kind we seek to detect, is likely to be an elusive and fickle prey.

Novel human-computer interfaces, for instance talking heads, can benefit from language understanding and generation techniques with big impact on user satisfaction. Dialoguebased intelligent tutoring systems require advanced dialogue processing, language understanding and generation components in order to assess students' natural language inputs and provide appropriate feedback. Moreover, language can facilitate human-computer interaction for the handicapped (no typing needed) and elderly leading to an ever increasing user base for computer systems. Some of the many areas emphasized by the ANLP track to include for contributions include multilingual processing, learning environments, multimodal communication, bioNLP, spam filtering, language acquisition (first and second), textual assessment, language varieties, materials development, generic classification, educational applications, information retrieval, speech processing, machine learning, knowledge representations, English for specific purposes, textual assessment indices, coreference resolution, word sense disambiguation, dialogue management and systems, language generation, language models, ontologies, and reasoning. For 2012, there were 15 submissions, out of which 10 were accepted as long papers and 3 as poster presentations.

We discuss the design and implementation of the pro- totype of an avatar-based health system aimed at pro- viding people access to an effective behavior change intervention which can help them to find and cultivate motivation to change unhealthy lifestyles. An empathic Embodied Conversational Agent (ECA) delivers the in- tervention. The health dialog is directed by a compu- tational model of Motivational Interviewing, a novel effective face-to-face patient-centered counseling style which respects an individual’s pace toward behavior change. Although conducted on a small sample size, re- sults of a preliminary user study to asses users’ accep- tance of the avatar counselor indicate that the current early version of the system prototype is well accepted by 75% of users.

This project focuses on artificial social interactions where things get nasty and mean. The purpose is training in social 'facework' -- managing the situation so that participants maintain their social dignity or 'face'. This can be especially delicate in cross-cultural contexts, where assumptions about social protocols and the emotional associations of utterances and gestures may differ. The purpose of this project is two-fold. First, it is intended as a training system, so that users might learn the do's and don'ts of social interactions in different cultures and different situations. The knowledge base draws from existing theories of diplomacy, facework, and (im)politeness theory. The other goal is to provide a platform for observation and experimentation of social interaction in an artificial, virtual setting in order to improve these theories.

Researchers have recognized the importance of classifying personality through discourse for many years. However, this line of research tends to focus almost exclusively on the personality categories known as the Big Five factors. Though this information is certainly valuable, it may also be useful to categorize personality based on the Leary’s Interpersonal Circumplex model which emphasizes a predictive function. In this paper we construct the data set for personality annotation among six dimensions (based on a coding scheme developed from Leary’s Interpersonal Circumplex) for players using a chat interaction in an epistemic game, Land Science. Our results indicate that overall personality annotation is reliable (Average Kappa = 0.65) with the highest reliability for the competitive dimension and the lowest reliability for the leading dimension.

Depression and other mood disorders are common and disabling disorders. We present work towards an objective diagnostic aid supporting clinicians using affective sensing technology with a focus on acoustic and statistical features from spontaneous speech. This work investigates differences in expressing positive and negative emotions in depressed and healthy control subjects as well as whether initial gender classification increases the recognition rate. To this end, spontaneous speech from interviews of 30 subjects of each depressed and controls was analysed, with a focus on questions eliciting positive and negative emotions. Using HMMs with GMMs for classification with 30-fold cross-validation, we found that MFCC, energy and intensity features gave highest recognition rates when female and male subjects were analysed together. When the dataset was first split by gender, log energy and shimmer features, respectively, were found to give the highest recognition rates in females, while it was loudness for males. Overall, correct recognition rates from acoustic features for depressed female subjects were higher than for male subjects. Using statistical features, we found that the response time and average syllable duration were longer in depressed subjects, while the interaction involvement and articulation rate were higher in control subjects.

Affective computing is an emerging field that aspires to narrow the communicative gap between the highly emotional human and the emotionally challenged computer by developing computational systems that recognize and respond to the affective states (such as moods, emotions) of the user. One of the basic principles of affective computing is that automatically recognizing and responding to a user's affective states during interactions with a computer can enhance the quality of the interaction, thereby making the computer interface more usable, enjoyable, and effective. For example, an affect-sensitive learning environment that detects and responds to student frustration is expected to increase motivation, engagement, and learning gains. Although the last decade has been ripe with theory and applications relevant to affective computing, these advances are accompanied by a new set of challenges. By providing a framework to discuss and evaluate novel research, we hope to leverage recent advances to speed up future research in this area.

Analyzing international political behavior based on similar precedent circumstances is one of the basic techniques that policymakers use to monitor and assess current situations. Our goal is to investigate how to analyze geopolitical conflicts as sequences of events and to determine what probabilistic models are suitable to perform these analyses. In this paper, we evaluate the performance of N-grams models on the problem of forecasting political conflicts from sequences of events. For the current phase of the project, we focused on event data collected from the Balkans war in the 1990's. Our experimental results indicate that N-gram models have impressive results when applied to this data set, with accuracies above 90\% for most configurations.

Gene selection has become a vital component in the learning process when using high-dimensional gene expression data. Although extensive research has been done towards evaluating the performance of classifiers trained with the selected features, the stability of feature ranking techniques has received relatively little study. This work evaluates the robustness of eleven threshold-based feature selection techniques, examining the impact of data sampling and class noise on the stability of feature selection. To assess the robustness of feature selection techniques, we use four groups of gene expression datasets, employ eleven threshold-based feature rankers, and generate artificial class noise to better simulate real-world datasets. The results demonstrate that although no ranker consistently outperforms the others, MI and Dev show the best stability on average, while GI and PR show the least stability on average. Results also show that trying to balance datasets through data sampling has on average no positive impact on the stability of feature ranking techniques applied to those datasets. In addition, increased feature subset sizes improve stability, but only does so reliably for noisy datasets.

Recommender systems assist users in the daunting task of sifting through large amounts of data in order to select relevant information or items. Common examples include consumer products and services, such as for songs, books, articles, etc. Unfortunately, such systems may be subject to attack by malicious users who want to manipulate the system’s recommendations to suit their needs: to promote their own (or demote a competitor’s) product/service, or to cause disruption in the recommender system. Attacks can cause the recommender system to become unreliable and untrustworthy, resulting in user dissatisfaction. Developers already face tradeoffs in system efficiency and accuracy, and designing for robustness adds an additional dimension for consideration. In this paper, we show how the underlying implementation choices for item-based and user-based Collaborative Filtering recommender systems can affect the accuracy and robustness of recommender systems. We also show how accuracy and robustness can change over a system’s lifetime by analyzing a set of temporal snapshots from system usage over time. Results provide insight into some of the tradeoffs between robustness and accuracy that operators may need to consider in development and evaluation.