Emerging Communication Technologies and Public Health Information Dissemination

Health promotion is a critical constituent of the public health system. Its primary objective is the empowerment of individuals and communities in the interest of positively influencing health behaviours and outcomes. One of the main ways in which successful health promotion is achieved is by the dissemination of relevant health information to individuals and communities. As global health costs rise to match the demands of an increasing and ageing population, such delivery of cost-effective public health information is explored. The recent advances in communication technologies have led to the development of social digital platforms (Web 2.0), with unprecedented opportunities for the extensive dissemination of relevant health information. The widespread uptake of social networking sites (SNS) presents a novel platform for public health promotion and management that can verily overcome the issues faced by current public health initiatives while reaching global populations of health consumers. This thesis aims to provide an exploratory analysis of the current landscape of health information communication across SNS, primarily through the platform Twitter. The research will address literature gaps in this cross-disciplinary field of health and communication sciences found for various SNS user-types, analyse and characterise the types of health information being disseminated across such platforms, as well as examine SNS activity during public health events. Public health officials and Web 2.0 platform developers can utilise findings from this thesis to address limitations of online public health-related communication insofar as they can assist with: a) advising plans for better engagement of information disseminated during health events; b) developing future applications and technologies that are appropriate for disadvantaged groups; c) identifying information dissemination strategies for authoritative health bodies and organizations to effectively reach populations

Combating User Misbehavior on Social Media

Social media encourages user participation and facilitates user’s self-expression like never before. While enriching user behavior in a spectrum of means, many social media platforms have become breeding grounds for user misbehavior. In this dissertation we focus on understanding and combating three specific threads of user misbehaviors that widely exist on social media — spamming, manipulation, and distortion. First, we address the challenge of detecting spam links. Rather than rely on traditional blacklist-based or content-based methods, we examine the behavioral factors of both who is posting the link and who is clicking on the link. The core intuition is that these behavioral signals may be more difficult to manipulate than traditional signals. We find that this purely behavioral approach can achieve good performance for robust behavior-based spam link detection. Next, we deal with uncovering manipulated behavior of link sharing. We propose a four-phase approach to model, identify, characterize, and classify organic and organized groups who engage in link sharing. The key motivating insight is that group-level behavioral signals can distinguish manipulated user groups. We find that levels of organized behavior vary by link type and that the proposed approach achieves good performance measured by commonly-used metrics. Finally, we investigate a particular distortion behavior: making bullshit (BS) statements on social media. We explore the factors impacting the perception of BS and what leads users to ultimately perceive and call a post BS. We begin by preparing a crowdsourced collection of real social media posts that have been called BS. We then build a classification model that can determine what posts are more likely to be called BS. Our experiments suggest our classifier has the potential of leveraging linguistic cues for detecting social media posts that are likely to be called BS. We complement these three studies with a cross-cutting investigation of learning user topical profiles, which can shed light into what subjects each user is associated with, which can benefit the understanding of the connection between user and misbehavior. Concretely, we propose a unified model for learning user topical profiles that simultaneously considers multiple footprints and we show how these footprints can be embedded in a generalized optimization framework. Through extensive experiments on millions of real social media posts, we find our proposed models can effectively combat user misbehavior on social media

Red Scare 2.0: User-Generated Ideology in the Age of Jeremy Corbyn and Social Media

This paper asks: How has Jeremy Corbyn been framed in discourses on Twitter in an ideological manner and how have such ideological discourses been challenged? It uses ideology critique as method for the investigation of tweets mentioning Jeremy Corbyn that were collected during the final phase of the Labour Party’s 2015 leadership election. The analysis shows how user-generated ideology portrays Jeremy Corbyn by creating discourse topics focused on general scapegoating, the economy, foreign politics, culture and authoritarianism

Combating User Misbehavior on Social Media

Social media encourages user participation and facilitates user’s self-expression like never before. While enriching user behavior in a spectrum of means, many social media platforms have become breeding grounds for user misbehavior. In this dissertation we focus on understanding and combating three specific threads of user misbehaviors that widely exist on social media — spamming, manipulation, and distortion. First, we address the challenge of detecting spam links. Rather than rely on traditional blacklist-based or content-based methods, we examine the behavioral factors of both who is posting the link and who is clicking on the link. The core intuition is that these behavioral signals may be more difficult to manipulate than traditional signals. We find that this purely behavioral approach can achieve good performance for robust behavior-based spam link detection. Next, we deal with uncovering manipulated behavior of link sharing. We propose a four-phase approach to model, identify, characterize, and classify organic and organized groups who engage in link sharing. The key motivating insight is that group-level behavioral signals can distinguish manipulated user groups. We find that levels of organized behavior vary by link type and that the proposed approach achieves good performance measured by commonly-used metrics. Finally, we investigate a particular distortion behavior: making bullshit (BS) statements on social media. We explore the factors impacting the perception of BS and what leads users to ultimately perceive and call a post BS. We begin by preparing a crowdsourced collection of real social media posts that have been called BS. We then build a classification model that can determine what posts are more likely to be called BS. Our experiments suggest our classifier has the potential of leveraging linguistic cues for detecting social media posts that are likely to be called BS. We complement these three studies with a cross-cutting investigation of learning user topical profiles, which can shed light into what subjects each user is associated with, which can benefit the understanding of the connection between user and misbehavior. Concretely, we propose a unified model for learning user topical profiles that simultaneously considers multiple footprints and we show how these footprints can be embedded in a generalized optimization framework. Through extensive experiments on millions of real social media posts, we find our proposed models can effectively combat user misbehavior on social media

Blogs as Infrastructure for Scholarly Communication.

This project systematically analyzes digital humanities blogs as an infrastructure for scholarly communication. This exploratory research maps the discourses of a scholarly community to understand the infrastructural dynamics of blogs and the Open Web. The text contents of 106,804 individual blog posts from a corpus of 396 blogs were analyzed using a mix of computational and qualitative methods. Analysis uses an experimental methodology (trace ethnography) combined with unsupervised machine learning (topic modeling), to perform an interpretive analysis at scale. Methodological findings show topic modeling can be integrated with qualitative and interpretive analysis. Special attention must be paid to data fitness, or the shape and re-shaping practices involved with preparing data for machine learning algorithms. Quantitative analysis of computationally generated topics indicates that while the community writes about diverse subject matter, individual scholars focus their attention on only a couple of topics. Four categories of informal scholarly communication emerged from the qualitative analysis: quasi-academic, para-academic, meta-academic, and extra-academic. The quasi and para-academic categories represent discourse with scholarly value within the digital humanities community, but do not necessarily have an obvious path into formal publication and preservation. A conceptual model, the (in)visible college, is introduced for situating scholarly communication on blogs and the Open Web. An (in)visible college is a kind of scholarly communication that is informal, yet visible at scale. This combination of factors opens up a new space for the study of scholarly communities and communication. While (in)invisible colleges are programmatically observable, care must be taken with any effort to count and measure knowledge work in these spaces. This is the first systematic, data driven analysis of the digital humanities and lays the groundwork for subsequent social studies of digital humanities.PhDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111592/1/mcburton_1.pd

One Year Later: September 11 and the Internet

Presents findings from a survey that looks at how the terror attacks affected Americans' views about access to online information, Internet use, and the Web after September 11. Contains scholarly studies built around analysis of hundreds of Web sites

The role of Twitter in legitimating the Energy East Pipeline, Canada

This thesis explores the value of social media in contemporary democratic practices; more precisely, on the use of social media in Canadian tar-sands pipeline infrastructure debate through the lens of public sphere theory. The study aims to contribute to improved understanding of Twitter’s shaping the course of the proposed Energy East pipeline, its legitimacy and formation of public debate around it. It is based on a mixed-methods approach employing both qualitative and quantitative research methodology. Data was collected from a topic-specific content stream on Twitter, followed by a series of semi-structured interviews with some of the most influential users within a sample of collected tweets. The study identified the users, the content and socio-political context of tweets that are posted in connection with the pipeline as well as users’ perceptions of Twitter as a tool for online deliberative democratic practices. Findings indicate Twitter is praised for offering an enabling environment for citizen journalism on real-time events, its swiftness of information dissemination, enabling contact with individuals outside of users’ established social circles and the power to influence public opinion. However, the medium is not without limitations which diminish its role as an optimal tool for democratic online public deliberation. My study suggests the main hindrance for this is the absence of constructive debate due to Twitter’s character-limitation of posts and predominantly one-sided communicative processes that take place within this medium. Its role in Energy East debate remains constrained within informative and reactive aspects of its service on current developments on the pipeline polemics and has as such a limited influence on legitimation processes surrounding the project. I therefore conclude that Twitter represents only a fragment of what can be considered the new public sphere and definitely not one-size-fits-all solution to the contemporary legitimation crisis of proposed large-scale industrial projects such as Energy East pipeline.M-IE

Trustworthiness in Social Big Data Incorporating Semantic Analysis, Machine Learning and Distributed Data Processing

This thesis presents several state-of-the-art approaches constructed for the purpose of (i) studying the trustworthiness of users in Online Social Network platforms, (ii) deriving concealed knowledge from their textual content, and (iii) classifying and predicting the domain knowledge of users and their content. The developed approaches are refined through proof-of-concept experiments, several benchmark comparisons, and appropriate and rigorous evaluation metrics to verify and validate their effectiveness and efficiency, and hence, those of the applied frameworks