Towards Automatic Evaluation of Health-Related CQA Data

The paper reports on evaluation of Russian community question answering (CQA) data in health domain. About 1,500 question-answer pairs were manually evaluated by medical professionals, in addition automatic evaluation based on reference disease-medicine pairs was performed. Although the results of the manual and automatic evaluation do not fully match, we find the method still promising and propose several improvements. Automatic processing can be used to dynamically monitor the quality of the CQA content and to compare different data sources. Moreover, the approach can be useful for symptomatic surveillance and health education campaigns.This work is partially supported by the Russian Foundation for Basic Research, project #14-07-00589 “Data Analysis and User Modelling in Narrow-Domain Social Media”. We also thank assessors who volunteered for the evaluation and Mail.Ru for granting us access to the data

Dirichlet belief networks for topic structure learning

Recently, considerable research effort has been devoted to developing deep architectures for topic models to learn topic structures. Although several deep models have been proposed to learn better topic proportions of documents, how to leverage the benefits of deep structures for learning word distributions of topics has not yet been rigorously studied. Here we propose a new multi-layer generative process on word distributions of topics, where each layer consists of a set of topics and each topic is drawn from a mixture of the topics of the layer above. As the topics in all layers can be directly interpreted by words, the proposed model is able to discover interpretable topic hierarchies. As a self-contained module, our model can be flexibly adapted to different kinds of topic models to improve their modelling accuracy and interpretability. Extensive experiments on text corpora demonstrate the advantages of the proposed model.Comment: accepted in NIPS 201

Using Twitter to learn about the autism community

Considering the raising socio-economic burden of autism spectrum disorder (ASD), timely and evidence-driven public policy decision making and communication of the latest guidelines pertaining to the treatment and management of the disorder is crucial. Yet evidence suggests that policy makers and medical practitioners do not always have a good understanding of the practices and relevant beliefs of ASD-afflicted individuals' carers who often follow questionable recommendations and adopt advice poorly supported by scientific data. The key goal of the present work is to explore the idea that Twitter, as a highly popular platform for information exchange, could be used as a data-mining source to learn about the population affected by ASD -- their behaviour, concerns, needs etc. To this end, using a large data set of over 11 million harvested tweets as the basis for our investigation, we describe a series of experiments which examine a range of linguistic and semantic aspects of messages posted by individuals interested in ASD. Our findings, the first of their nature in the published scientific literature, strongly motivate additional research on this topic and present a methodological basis for further work.Comment: Social Network Analysis and Mining, 201

The Social Side of the Internet

Presents survey findings on Americans' level of participation in voluntary groups by type of group, demographics, and Internet and social media use, as well as views on the role of the Internet in group connections, activities, and accomplishments

Vaccine hesitancy and anti-vaccination attitudes during the start of COVID-19 vaccination program: A content analysis on twitter data

Twitter is a useful source for detecting anti-vaccine content due to the increasing prevalence of these arguments on social media. We aimed to identify the prominent themes about vaccine hesitancy and refusal on social media posts in Turkish during the COVID-19 pandemic. In this qualitative study, we collected public tweets (n = 551,245) that contained a vaccine-related keyword and had been published between 9 December 2020 and 8 January 2021 through the Twitter API. A random sample of tweets (n = 1041) was selected and analyzed by four researchers with the content analysis method. We found that 90.5% of the tweets were about vaccines, 22.6% (n = 213) of the tweets mentioned at least one COVID-19 vaccine by name, and the most frequently mentioned COVID-19 vaccine was CoronaVac (51.2%). We found that 22.0% (n = 207) of the tweets included at least one anti-vaccination theme. Poor scientific processes (21.7%), conspiracy theories (16.4%), and suspicions towards manufacturers (15.5%) were the most frequently mentioned themes. The most co-occurring themes were "poor scientific process" with "suspicion towards manufacturers" (n = 9), and "suspicion towards health authorities" (n = 5). This study may be helpful for health managers, assisting them to identify the major concerns of the population and organize preventive measures through the significant role of social media in early spread of information about vaccine hesitancy and anti-vaccination attitudes

The Dark Side of Morality: Group Polarization and Moral Epistemology

This article argues that philosophers and laypeople commonly conceptualize moral truths or justified moral beliefs as discoverable through intuition, argument, or some other purely cognitive or affective process. It then contends that three empirically well-supported theories all predict that this ‘Discovery Model’ of morality plays a substantial role in causing social polarization. The same three theories are then used to argue that an alternative ‘Negotiation Model’ of morality—according to which moral truths are not discovered but instead created by actively negotiating compromises—promises to reduce polarization by fostering a progressive willingness to ‘work across the aisle’ to settle moral issues cooperatively. This article then examines potential methods for normatively evaluating polarization, arguing there are prima facie reasons to favor the Negotiation Model over the Discovery Model based on their hypothesized effects on polarization. Finally, I outline avenues for further empirical and philosophical research

Spartan Daily October 23, 2012

Volume 139, Issue 29https://scholarworks.sjsu.edu/spartandaily/1345/thumbnail.jp

Topic Modelling of Everyday Sexism Project Entries

The Everyday Sexism Project documents everyday examples of sexism reported by volunteer contributors from all around the world. It collected 100,000 entries in 13+ languages within the first 3 years of its existence. The content of reports in various languages submitted to Everyday Sexism is a valuable source of crowdsourced information with great potential for feminist and gender studies. In this paper, we take a computational approach to analyze the content of reports. We use topic-modelling techniques to extract emerging topics and concepts from the reports, and to map the semantic relations between those topics. The resulting picture closely resembles and adds to that arrived at through qualitative analysis, showing that this form of topic modeling could be useful for sifting through datasets that had not previously been subject to any analysis. More precisely, we come up with a map of topics for two different resolutions of our topic model and discuss the connection between the identified topics. In the low resolution picture, for instance, we found Public space/Street, Online, Work related/Office, Transport, School, Media harassment, and Domestic abuse. Among these, the strongest connection is between Public space/Street harassment and Domestic abuse and sexism in personal relationships.The strength of the relationships between topics illustrates the fluid and ubiquitous nature of sexism, with no single experience being unrelated to another.Comment: preprint, under revie

Optimism as a Mediating Factor in the Relationship between Anxiety and News Media Exposure in College Students

Recently, media research has focused on young people to determine what effect violent media images may have on aggressive behavior, but little research has investigated the kind of psychological distress similar images may cause. What emotional impact does increased exposure to negative and even violent news coverage have on young adults? In this study, the relationship between such news media and anxiety levels is examined, as well as the possible mediating role that an optimistic life orientation may play in that relationship. It is hypothesized that the degree to which these individuals follow news media will positively correlate with their state anxiety levels, but when accounting for an optimistic worldview, this effect will be minimized or eliminated. A survey was administered to a sample of 278 undergraduate students attending Bryant University that measured their anxiety levels, life orientation in terms of optimism, and news media viewing habits. The results showed no significant correlation between news media viewing and state anxiety, and therefore also could not support any mediating role of optimism either. Limitations and mitigating factors regarding this study as well as possible avenues for future research are discussed