The escalating obesity rate in the USA has made obesity prevention a top public health priority. Recent interventions have tapped into the social media (SM) landscape. To leverage SM in obesity prevention, we must understand user-generated discourse surrounding the topic. This study was conducted to describe SM interactions about weight through a mixed methods analysis. Data were collected across 60 days through SM monitoring services, yielding 2.2 million posts. Data were cleaned and coded through Natural Language Processing (NLP) techniques, yielding popular themes and the most retweeted content. Qualitative analyses of selected posts add insight into the nature of the public dialogue and motivations for participation. Twitter represented the most common channel. Twitter and Facebook were dominated by derogatory and misogynist sentiment, pointing to weight stigmatization, whereas blogs and forums contained more nuanced comments. Other themes included humor, education, and positive sentiment countering weight-based stereotypes. This study documented weight-related attitudes and perceptions. This knowledge will inform public health/obesity prevention practice.

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Detecting deception in natural language is a problem amenable to economic analysis. Economics typically assumes that individuals are self-interested, which leads them to perform actions in accord with their own goals. The field of experimental economics emerged to construct environments wherein human subjects make decisions so as to test economic hypotheses. Experimental economists recently have developed virtual worlds to better situate experiment subjects in more realistic environments. Virtual word experiments represent an exciting new area for deception research as they offer insight into individuals both acting out and communicating in accord with their intentions. This paper describes the use of virtual world experiments for economic research incorporating the detection of deceptive individuals.

The Speech Transcription Analysis Tool (STAT) is an open source tool for aligning and comparing two phonetically transcribed texts of human speech. The output analysis is a parameterized set of phonological differences. These differences are based upon a selectable set of binary phonetic features such as [voice], [continuant], [high], etc. STAT was initially designed to provide sets of phonological speech patterns in the comparisons of various English accents found in the Speech Accent Archive http://accent.gmu.edu, but its scope and utility expand to matters of language assessment, phonetic training, forensic linguistics, and speech recognition.