Suicide is a leading cause of death in the United States. One of the major challenges to suicide prevention is that those who may be most at risk cannot be relied upon to report their conditions to clinicians. This paper takes an initial step toward the automatic detection of suicidal risk factors through social media activity, with no reliance on self-reporting. We consider the performance of annotators with various degrees of expertise in suicide prevention at annotating microblog data for the purpose of training text-based models for detecting suicide risk behaviors. Consistent with crowdsourcing literature, we found that novice-novice annotator pairs underperform expert annotators and outperform automatic lexical analysis tools, such as Linguistic Inquiry and Word Count.

Domestic abuse affects people of every race, class, age, and nation. There is significant research on the prevalence and effects of domestic abuse; however, such research typically involves population-based surveys that have high financial costs. This work provides a qualitative analysis of domestic abuse using data collected from the social and news-aggregation website reddit.com. We develop classifiers to detect submissions discussing domestic abuse, achieving accuracies of up to 92%, a substantial error reduction over its baseline. Analysis of the top features used in detecting abuse discourse provides insight into the dynamics of abusive relationships.

This paper presents a study completed in Quito, Ecuador's capital, in 2002. It investigates the attitudinal perceptions toward English in advertising in this context, as well as the actual distribution of English in magazine ads and commercial names of business establishments. The findings are the result of four data collection procedures: first, a questionnaire administered to advertising experts; second, an analysis of business names in ten shopping centers; third, an analysis of advertisements in Ecuadorian magazines; and fourth, an interview survey with the same group of advertising experts. The results are analyzed both quantitatively and qualitatively, with the aim to provide an attitudinal sociolinguistic profile of English in Ecuador from a descriptive, comparative and critical perspective. Adopting the socioeconomic framework presented by Bourdieu (1991), English is found to represent commercial capital. Moreover, English is shown to be highly stratified according to socioeconomic strata, and to function as a segmentizer and a gatekeeper on the Ecuadorian market. Thus, if English is to succeed in functioning as empowerment (cf. Friedrich, 2001) among the disadvantaged in Ecuador in the future, affirmative action is needed, especially within the educational sector.

We discuss Image Sense Discrimination (ISD), and apply a method based on spectral clustering, using multimodal features from the image and text of the embedding web page. We evaluate our method on a new data set of annotated web images, retrieved with ambiguous query terms. Experiments investigate different levels of sense granularity, as well as the impact of text and image features, and global versus local text features.

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In addition to information, text contains attitudinal, and more specifically, emotional content. This paper explores the text-based emotion prediction problem empirically, using supervised machine learning with the SNoW learning architecture. The goal is to classify the emotional affinity of sentences in the narrative domain of children's fairy tales, for subsequent usage in appropriate expressive rendering of text-to-speech synthesis. Initial experiments on a preliminary data set of 22 fairy tales show encouraging results over a naïve baseline and BOW approach for classification of emotional versus non-emotional contents, with some dependency on parameter tuning. We also discuss results for a tripartite model which covers emotional valence, as well as feature set alternations. In addition, we present plans for a more cognitively sound sequential model, taking into consideration a larger set of basic emotions.

Understanding and characterizing perceptual expertise is a major bottleneck in developing intelligent systems. In knowledge-rich domains such as dermatology, perceptual expertise influences the diagnostic inferences made based on the visual input. This study uses eye movement data from 12 dermatology experts and 12 undergraduate novices while they inspected 34 dermatological images. This work investigates the differences in global and local temporal fixation patterns between the two groups using recurrence quantification analysis (RQA). The RQA measures reveal significant differences in both global and local temporal patterns between the two groups. Results show that experts tended to refixate previously inspected areas less often than did novices, and their refixations were more widely separated in time. Experts were also less likely to follow extended scan paths repeatedly than were novices. These results suggest the potential value of RQA measures in characterizing perceptual expertise. We also discuss potential use of the RQA method in understanding the interactions between experts' visual and linguistic behavior.

Humans rely on multiple sensory modalities when examining and reasoning over images. In this paper, we describe a new multimodal dataset that consists of gaze measurements and spoken descriptions collected in parallel during an image inspection task. The task was performed by multiple participants on 100 general-domain images showing everyday objects and activities. We demonstrate the usefulness of the dataset by applying an existing visual-linguistic data fusion framework in order to label important image regions with appropriate linguistic labels.