Figure 1: Overview of DramatVis Personae (DVP). (a) Rich text editor. (b) Characters and Demographics panel for listing characters identifed by the system, merging aliases, and assigning social identities to characters. (c) Timeline representation of the story (Peter Pan by J. M. Barrie (1911)) showing every mentions of characters as well as the total number of mentions of characters. (d) Word zone [38] showing sample adjectives used for the selected characters (Peter Pan and Wendy).

No abstract

Many creative writing tasks (e.g., fiction writing) require authors to write complex narrative components (e.g., characterization, events,

Background: Lymphatic filariasis, commonly known as elephantiasis, is a neglected tropical disease. Evaluation of mass drug administration (MDA) is done internally by the health authorities and externally by independent agencies. This paper reports the findings of evaluation of MDA conducted in Malda district of West Bengal state in May-June 2015. Objectives: To assess the Coverage & Compliance rates of MDA against lymphatic filariasis and to study the factors influencing non-coverage and non-compliance in Malda district. Materials & Methods: A Community based cross-sectional study was conducted in three selected rural blocks and one municipality. Family was the unit of sampling in the current MDA coverage survey. 30 families in each of four clusters were taken as samples. Cluster sampling technique was adopted. The data was collected in a pre-designed semi-structured proforma from 120 households. Results: 564 eligible population 120 families were studied and 50.53% of them were males. Predominant respondents were male (92%) with average age 40.7 years. The Diethylcarbamazine citrate plus Albendazole coverage rate was 95% and the compliance rate was 71.6%. The major reason for non–compliance was due to fear of side effects amounting to 58%. Only 4 persons reported adverse effects after drug consumption. Conclusion: Though distribution was high, many people were not consuming drugs. Consumption was not properly supervised and there was misconception prevailed among workers about time of consumption. A high level of motivation and commitment from the drug distributors with adequate training is required for ensuring a high coverage and compliance rates. Supervision should be strengthened to improve consumption and misconception should be eliminated through training.

Intersectional bias is a bias caused by an overlap of multiple social factors like gender, sexuality, race, disability, religion, etc. A recent study has shown that word embedding models can be laden with biases against intersectional groups like African American females, etc. The first step towards tackling such intersectional biases is to identify them. However, discovering biases against different intersectional groups remains a challenging task. In this work, we present WordBias, an interactive visual tool designed to explore biases against intersectional groups encoded in static word embeddings. Given a pretrained static word embedding, WordBias computes the association of each word along different groups based on race, age, etc. and then visualizes them using a novel interactive interface. Using a case study, we demonstrate how WordBias can help uncover biases against intersectional groups like Black Muslim Males, Poor Females, etc. encoded in word embedding. In addition, we also evaluate our tool using qualitative feedback from expert interviews. The source code for this tool can be publicly accessed for reproducibility at github.com/bhavyaghai/WordBias. CCS Concepts: • Human-centered computing → Visual analytics; • Computing methodologies → Natural language processing.