Analyzing the Language of Food on Social Media

We investigate the predictive power behind the language of food on social media. We collect a corpus of over three million food-related posts from Twitter and demonstrate that many latent population characteristics can be directly predicted from this data: overweight rate, diabetes rate, political leaning, and home geographical location of authors. For all tasks, our language-based models significantly outperform the majority-class baselines. Performance is further improved with more complex natural language processing, such as topic modeling. We analyze which textual features have most predictive power for these datasets, providing insight into the connections between the language of food, geographic locale, and community characteristics. Lastly, we design and implement an online system for real-time query and visualization of the dataset. Visualization tools, such as geo-referenced heatmaps, semantics-preserving wordclouds and temporal histograms, allow us to discover more complex, global patterns mirrored in the language of food.Comment: An extended abstract of this paper will appear in IEEE Big Data 201

Signal Fusion and Semantic Similarity Evaluation for Social Media Based Adverse Drug Event Detection

Recent advancements in pharmacovigilance tasks have shown the usage of social media as a resource to obtain real-time signals for drug surveillance. Researchers demonstrated a good potential for the detection of Adverse Drug Events (ADEs) using social media much earlier than the traditional reporting systems maintained by official regulatory authorities like the United States Food and Drug Administration (FDA). Existing automated drug surveillance systems have used various types of social media channels and search query logs for monitoring ADE signals.;In this thesis, we address two key performance issues related to automated drug surveillance systems. The first is to improve the ADE signal detection by analyzing signals from multiple social media channels, and the second is usage of semantic similarity to evaluate ADE narratives detected by drug surveillance systems. Most current approaches for detecting ADEs from social media rely on a single channel: forums or microblogs or query logs. In this study we propose a new methodology to fuse signals from different social media channels. We use graphical causal models to discover potentially hidden connections between data channels, and then use such associations to generate signals for ADEs. Further, prior work have not emphasized much on the language of healthcare consumers, which is often casual and informal in expressing health issues on social media. There is a high potential to miss the semantic similarity between ADE terms extracted from social media and terms from formal official narratives when the two sets of terms do not share exact text. Thus, we exhibit the usage of semantic similarity to enhance accuracy of detected ADEs, and evaluated similarity measurement algorithms developed over biomedical vocabularies in ADE surveillance domain. We experimented on a dataset of drugs which had FDA black box warnings with a retrospective analysis spanning years 2008 to 2015. The results show a better detection rate and an improved performance in terms of precision, recall and timeliness using our proposed methods

Picturing another culture: Developing language proficiency, empathy, and visual literacy through art

Integrating art (paintings, sculptures, photography, and other types of images) into second language (L2) instruction, can have a positive effect on language acquisition and developing intercultural understanding. As the instructor provides visual scaffolding, learners at all proficiency levels have the opportunity to engage more deeply with L2 course materials. Not only can students learn to interpret imagery and create their own effective combinations of visuals and texts, they also develop some familiarity with seminal artwork from the target culture. This article outlines how spiraling art through a language curriculum can aid vocabulary retention, illustrate poetic language, and raise awareness of diversity and inclusion. As a result, learners investigate and interact with the products, practices, and perspectives of the L2 culture while simultaneously developing visual analysis skills - the latter essential in an age in which both authentic and digitally manipulated imagery dominate the media and social discourse.Accepted manuscrip

Children\u27s Books as Cultural Products: A Qualitative Study of Cultural Representation in Hmong and Non-Hmong American Books

This study examined the type of cultural practices and values depicted within Hmong American children’s books in comparison to non-Hmong American children’s books from the United States. The purpose was to explore if prior Hmong traditional practices and values reflective of American individualism would extend to Hmong children’s books. Thirty best-seller children’s books were coded using two checklists, one focused on Hmong traditional practices and the other on American values. Results showed that Hmong traditional practices underscored by Hmong adolescents in prior research somewhat extended to Hmong children’s books. Moreover, in some respects Hmong children’s books displayed similar numbers of American values as did American children’s books. This study expanded the ethnic-racial socialization literature to an understudied population, the Hmong. In addition, the study provides parents and public educators insights into the cultural practices and values presented within Hmong children’s books