Erica Briscoe

An Integrated Bayesian Approach to Shape Representation and Perceptual Organization

We present a unified Bayesian approach to shape representation and related problems in perceptual organization, including part decomposition, shape similarity, figure/ground estimation, and 3D shape. The approach is based on the idea of estimating the skeletal structure most likely to have generated the observed shape via a process of stochastic “growth.” We survey the approach briefly and show how it can be extended in a principled way to solve a wide array of related problems.

Cues to Deception in Social Media Communications

With the increasing reliance on social media as a dominant communication medium for current news and personal communications, communicators are capable of executing deception with relative ease. While past-related research has investigated written deception in traditional forms of computer mediated communication (e.g. email), we are interested determining if those same indicators hold in social media-like communication and if new, social-media specific linguistic cues to deception exist. Our contribution is two-fold: 1) we present results on human subjects experimentation to confirm existing and new linguistic cues to deception; 2) we present results on classifying deception from training machine learning classifiers using our best features to achieve an average 90% accuracy in cross fold validation.

Discriminative Models for Predicting Deception Strategies

Although a large body of work has previously investigated various cues predicting deceptive communications, especially as demonstrated through written and spoken language (e.g., [30]), little has been done to explore predicting kinds of de- ception. We present novel work to evaluate the use of textual cues to discriminate between deception strategies (such as exaggeration or falsification), concentrating on intention- ally untruthful statements meant to persuade in a social media context. We conduct human subjects experimenta- tion wherein subjects were engaged in a conversational task and then asked to label the kind(s) of deception they employed for each deceptive statement made. We then develop discriminative models to understand the difficulty between choosing between one and several strategies. We evaluate the models using precision and recall for strategy prediction among 4 deception strategies based on the most relevant psycholinguistic, structural, and data-driven cues. Our single strategy model results demonstrate as much as a 58% increase over baseline (random chance) accuracy and we also find that it is more difficult to predict certain kinds of de- ception than others.

Real-time streaming intelligence: Integrating graph and NLP analytics

With the growth of social media, embedded sensors, and “smart” devices, those responsible for managing resources during emergencies, such as weather-related disasters, are transitioning from an era of data scarcity to data deluge. During a crisis situation, emergency managers must aggregate various data to assess the situation on the ground, evaluate response plans, give advice to state and local agencies, and inform the public. We make the case that social graph analysis and natural language modeling in real time are paramount to distilling useful intelligence from the large volumes of data available to crisis response personnel. Using ground truth information from social media data surrounding the 2012 Hurricane Sandy in New York City, we test and evaluate our real-time analytics platform to identify immediate and critical information that increases situational awareness during disastrous events.

Determining credibility from social network structure

The increasing proliferation of social media results in users that are forced to ascertain the truthfulness of information that they encounter from unknown sources using a variety of indicators (e.g. explicit ratings, profile information, etc.). Through human-subject experimentation with an online social network-style platform, our study focuses on the determination of credibility in ego-centric networks based on subjects observing social network properties such as degree centrality and geodesic distance. Using manipulated social network graphs, we find that corroboration and degree centrality are most utilized by subjects as indicators of credibility. We discuss the implications of the use of social network graph structural properties and use principal components analysis to visualize the reduced dimensional space.

Evaluating Counter-IED Strategies

Improvised explosive devices (IEDs) are one of the largest threats facing coalition forces in current military conflicts. The United States and other nations are greatly invested in mitigating these deadly devices. Past results have shown that completely technological counter-IED (cIED) efforts will be insufficient and, therefore, attention is focusing on augmenting the technological methods with neutralizing factors that contribute to human involvement in the IED perpetration process.To do so successfully requires an understanding of the behavioral aspects and influences of human involvement.This has led to an interest in socio-technical and systems-based models of terrorist activity. By integrating behavioral aspects of adversarial activities with computational methods, a greater understanding of these activities can be attained; simultaneously, potentially effective intervention points can be ascertained.This is often accomplished by modeling individuals, organizations, and societies via the creation of micro-, meso-, and macro-scale models to analyze and experiment with the impact of potential influences on population behavior. In addition to providing insight, model flexibility and model dynamics are required to assessmultiple interpretations of situations as they play out over time. Static models often cannot achieve this since disparate motivations and ideological factors evolve as a function of time. This article focuses on modeling the IED perpetration process, where knowledge was provided by subject matter experts (SMEs) from the United States and the United Kingdom, to ascertain behavioral aspects of cIED efforts. Defense & Security AnalysisVol. 27, No. 2, pp. 135–147, June 2011

Technology Futures From Passive Crowdsourcing

Efforts to predict emerging, new, or disruptive technologies use various analyses and data sources to derive indicators and subsequent forecasts about technological innovations, including quantitative (such as bibliometric analysis) and qualitative methods (such as expert elicitation). We describe a novel approach for harnessing a collective (crowdsourced) predictive ability available through publicly made technology-related statements by automatically determining significant convergences on technology forecasts. We evaluate our approach using a corpus of science-related articles and demonstrate that passive crowdsourcing may be a powerful source of technology-related predictive intelligence.

Passive Crowd Sourcing for Technology Prediction

Technology prediction methods use various types of information to make systematic forecasts about technological innovations. Forecasting approaches vary, including quantitative (such as patent analysis) and qualitative methods (such as expert elicitation using the Delphi method). We discuss a new method and system for predicting technology futures by harnessing the predictive information made available by society in open sources. Our approach automatically discovers future-looking temporal phrases associated with technology topics and presents predictions deemed significant using the \(G^2\) statistic. Here, we evaluate the phrase discovery component using a dataset of 782 technology forecast statements. We hope to demonstrate that ’passive crowd-sourcing’ may be a meaningful source of technology-related predictive intelligence.

Social Network Derived Credibility

The increasing use of social media results in users that must ascertain the truthfulness of information that they encounter from unknown sources using a variety of indicators (e.g., explicit ratings, profile information, etc.). Through human-subject experimentation with an online social network-style platform, we focus on the determination of credibility in ego-centric networks, where participants are able to observe salient social network properties, such as degree centrality and geodesic distance. Using manipulated social network graphs, we find that corroboration and degree centrality are most utilized by subjects as indicators of credibility. utilized by subjects as indicators of credibility. We discuss the implications of the use of social network structural properties, use principal components analysis to visualize the reduced dimensional feature space, and analyze how credibility changes per property according to the “Big 5” theory of personality.

A Semantic Frame-Based Similarity Metric for Characterizing Technological Capabilities

In this work we are motivated by the problem of representing technological capabilities that are present in text. We propose to use frames to capture the semantics around technologies and describe a new method, called FrameSim, that serves as a means of determining the similarity between these capabilities. We intentionally focus on a corpus built from informal media (e.g., news articles), which provides greater variability and an increased amount of suppositions about technologies’ uses, deriving value from ‘passive crowdsourcing’. Our evaluation shows that this semantic frame-based similarity metric preserves technology topic coherence, and we discuss how this method shows promise for improving conceptual search in scientific and technical writing.