A Computational Linguistic Approach towards Understanding Wikipedia\u27s Article for Deletion (AfD) Discussions

With the thriving of online deliberation, Wikipedia\u27s Article for Deletion (AfD) discussion has drawn a number of researchers\u27 attention in the past decade. In this thesis we aim to solve two main problems: 1) how to help new users effectively participate in the discussion; and 2) how to make it efficient for administrators to make decision based on the discussion. To solve the first problem, we obtain a knowledge repository for new users by recognizing imperatives. We propose a method to detect imperatives based on syntactic analysis of the texts. And the result shows a good precision and reasonable recall. To solve the second problem, we propose a decision making support system that provides administrators with an reorganized overview of a discussion. We first divide the arguments in the discussion into several groups based on similarity; then further divide each group into subgroups based on sentiment (positive, neutral and negative). In order to classify sentiment polarity, we propose a recursive algorithm based on the dependency structure of the text. Comparing with the state of the art sentiment analysis tool by Stanford, our algorithm shows a promising result of 3-categories classification without requiring a large training dataset

An NLP Analysis of Health Advice Giving in the Medical Research Literature

Health advice – clinical and policy recommendations – plays a vital role in guiding medical practices and public health policies. Whether or not authors should give health advice in medical research publications is a controversial issue. The proponents of actionable research advocate for the more efficient and effective transmission of science evidence into practice. The opponents are concerned about the quality of health advice in individual research papers, especially that in observational studies. Arguments both for and against giving advice in individual studies indicate a strong need for identifying and accessing health advice, for either practical use or quality evaluation purposes. However, current information services do not support the direct retrieval of health advice. Compared to other natural language processing (NLP) applications, health advice has not been computationally modeled as a language construct either. A new information service for directly accessing health advice should be able to reduce information barriers and to provide external assessment in science communication. This dissertation work built an annotated corpus of scientific claims that distinguishes health advice according to its occurrence and strength. The study developed NLP-based prediction models to identify health advice in the PubMed literature. Using the annotated corpus and prediction models, the study answered research questions regarding the practice of advice giving in medical research literature. To test and demonstrate the potential use of the prediction model, it was used to retrieve health advice regarding the use of hydroxychloroquine (HCQ) as a treatment for COVID-19 from LitCovid, a large COVID-19 research literature database curated by the National Institutes of Health. An evaluation of sentences extracted from both abstracts and discussions showed that BERT-based pre-trained language models performed well at detecting health advice. The health advice prediction model may be combined with existing health information service systems to provide more convenient navigation of a large volume of health literature. Findings from the study also show researchers are careful not to give advice solely in abstracts. They also tend to give weaker and non-specific advice in abstracts than in discussions. In addition, the study found that health advice has appeared consistently in the abstracts of observational studies over the past 25 years. In the sample, 41.2% of the studies offered health advice in their conclusions, which is lower than earlier estimations based on analyses of much smaller samples processed manually. In the abstracts of observational studies, journals with a lower impact are more likely to give health advice than those with a higher impact, suggesting the significance of the role of journals as gatekeepers of science communication. For the communities of natural language processing, information science, and public health, this work advances knowledge of the automated recognition of health advice in scientific literature. The corpus and code developed for the study have been made publicly available to facilitate future efforts in health advice retrieval and analysis. Furthermore, this study discusses the ways in which researchers give health advice in medical research articles, knowledge of which could be an essential step towards curbing potential exaggeration in the current global science communication. It also contributes to ongoing discussions of the integrity of scientific output. This study calls for caution in advice-giving in medical research literature, especially in abstracts alone. It also calls for open access to medical research publications, so that health researchers and practitioners can fully review the advice in scientific outputs and its implications. More evaluative strategies that can increase the overall quality of health advice in research articles are needed by journal editors and reviewers, given their gatekeeping role in science communication

Social media and the public sphere

Social media has become a key term in Media and Communication Studies and public discourse for characterising platforms such as Facebook, Twitter, YouTube, Wikipedia, LinkedIn, Wordpress, Blogspot, Weibo, Pinterest, Foursquare and Tumblr. This paper discusses the role of the concept of the public sphere for understanding social media critically. It argues against an idealistic interpretation of Habermas and for a cultural-materialist understanding of the public sphere concept that is grounded in political economy. It sets out that Habermas’ original notion should best be understood as a method of immanent critique that critically scrutinises limits of the media and culture grounded in power relations and political economy. The paper introduces a theoretical model of public service media that it uses as foundation for identifying three antagonisms of the contemporary social media sphere in the realms of the economy, the state and civil society. It concludes that these limits can only be overcome if the colonisation of the social media lifeworld is countered politically so that social media and the Internet become public service and commons-based media. Acknowledgement: This paper is the extended version of Christian Fuchs’ inaugural lecture for his professorship of social media at the University of Westminster that he took up on February 1st, 2013. He gave the lecture on February 19th, 2014, at the University of Westminster

“You’re trolling because…” – A Corpus-based Study of Perceived Trolling and Motive Attribution in the Comment Threads of Three British Political Blogs

This paper investigates the linguistically marked motives that participants attribute to those they call trolls in 991 comment threads of three British political blogs. The study is concerned with how these motives affect the discursive construction of trolling and trolls. Another goal of the paper is to examine whether the mainly emotional motives ascribed to trolls in the academic literature correspond with those that the participants attribute to the alleged trolls in the analysed threads. The paper identifies five broad motives ascribed to trolls: emotional/mental health-related/social reasons, financial gain, political beliefs, being employed by a political body, and unspecified political affiliation. It also points out that depending on these motives, trolling and trolls are constructed in various ways. Finally, the study argues that participants attribute motives to trolls not only to explain their behaviour but also to insult them

Mapping (Dis-)Information Flow about the MH17 Plane Crash

Digital media enables not only fast sharing of information, but also disinformation. One prominent case of an event leading to circulation of disinformation on social media is the MH17 plane crash. Studies analysing the spread of information about this event on Twitter have focused on small, manually annotated datasets, or used proxys for data annotation. In this work, we examine to what extent text classifiers can be used to label data for subsequent content analysis, in particular we focus on predicting pro-Russian and pro-Ukrainian Twitter content related to the MH17 plane crash. Even though we find that a neural classifier improves over a hashtag based baseline, labeling pro-Russian and pro-Ukrainian content with high precision remains a challenging problem. We provide an error analysis underlining the difficulty of the task and identify factors that might help improve classification in future work. Finally, we show how the classifier can facilitate the annotation task for human annotators

Governing PatientsLikeMe: information production and research through an open, distributed, and data-based social media network

Many organizations develop social media networks with the aim of engaging a wide range of social groups in the production of information that fuels their processes. This effort appears to crucially depend on complex data structures that allow the organization to connect and collect data from a myriad of local contexts and actors. One such organization, PatientsLikeMe, is developing a platform with the aim of connecting patients with one another while collecting self-reported medical data, which it uses for scientific and commercial medical research. Here the question of how technology and the underlying data structures shape the kind of information and medical evidence that can be produced through social media-based arrangements comes powerfully to the fore. In this observational case study, I introduce the concepts of information cultivation and social denomination to explicate how the development of such a data collection architecture requires a continuous exercise of balancing between the conflicting demands of patient engagement, necessary for collecting data in scale, and data semantic context, necessary for effective capture of health phenomena in informative and specific data. The study extends the understanding of the context-embeddedness of information phenomena and discusses some of the social consequences of social media models for knowledge making