Data science methods for the analysis of controversial social dedia discussions

Social media communities like Reddit and Twitter allow users to express their views on topics of their interest, and to engage with other users who may share or oppose these views. This can lead to productive discussions towards a consensus, or to contended debates, where disagreements frequently arise. Prior work on such settings has primarily focused on identifying notable instances of antisocial behavior such as hate-speech and “trolling”, which represent possible threats to the health of a community. These, however, are exceptionally severe phenomena, and do not encompass controversies stemming from user debates, differences of opinions, and off-topic content, all of which can naturally come up in a discussion without going so far as to compromise its development. This dissertation proposes a framework for the systematic analysis of social media discussions that take place in the presence of controversial themes, disagreements, and mixed opinions from participating users. For this, we develop a feature-based model to describe key elements of a discussion, such as its salient topics, the level of activity from users, the sentiments it expresses, and the user feedback it receives. Initially, we build our feature model to characterize adversarial discussions surrounding political campaigns on Twitter, with a focus on the factual and sentimental nature of their topics and the role played by different users involved. We then extend our approach to Reddit discussions, leveraging community feedback signals to define a new notion of controversy and to highlight conversational archetypes that arise from frequent and interesting interaction patterns. We use our feature model to build logistic regression classifiers that can predict future instances of controversy in Reddit communities centered on politics, world news, sports, and personal relationships. Finally, our model also provides the basis for a comparison of different communities in the health domain, where topics and activity vary considerably despite their shared overall focus. In each of these cases, our framework provides insight into how user behavior can shape a community’s individual definition of controversy and its overall identity.Social-Media Communities wie Reddit und Twitter ermöglichen es Nutzern, ihre Ansichten zu eigenen Themen zu äußern und mit anderen Nutzern in Kontakt zu treten, die diese Ansichten teilen oder ablehnen. Dies kann zu produktiven Diskussionen mit einer Konsensbildung führen oder zu strittigen Auseinandersetzungen über auftretende Meinungsverschiedenheiten. Frühere Arbeiten zu diesem Komplex konzentrierten sich in erster Linie darauf, besondere Fälle von asozialem Verhalten wie Hassrede und "Trolling" zu identifizieren, da diese eine Gefahr für die Gesprächskultur und den Wert einer Community darstellen. Die sind jedoch außergewöhnlich schwerwiegende Phänomene, die keinesfalls bei jeder Kontroverse auftreten die sich aus einfachen Diskussionen, Meinungsverschiedenheiten und themenfremden Inhalten ergeben. All diese Reibungspunkte können auch ganz natürlich in einer Diskussion auftauchen, ohne dass diese gleich den ganzen Gesprächsverlauf gefährden. Diese Dissertation stellt ein Framework für die systematische Analyse von Social-Media Diskussionen vor, die vornehmlich von kontroversen Themen, strittigen Standpunkten und Meinungsverschiedenheiten der teilnehmenden Nutzer geprägt sind. Dazu entwickeln wir ein Feature-Modell, um Schlüsselelemente einer Diskussion zu beschreiben. Dazu zählen der Aktivitätsgrad der Benutzer, die Wichtigkeit der einzelnen Aspekte, die Stimmung, die sie ausdrückt, und das Benutzerfeedback. Zunächst bauen wir unser Feature-Modell so auf, um bei Diskussionen gegensätzlicher politischer Kampagnen auf Twitter die oben genannten Schlüsselelemente zu bestimmen. Der Schwerpunkt liegt dabei auf den sachlichen und emotionalen Aspekten der Themen im Bezug auf die Rollen verschiedener Nutzer. Anschließend erweitern wir unseren Ansatz auf Reddit-Diskussionen und nutzen das Community-Feedback, um einen neuen Begriff der Kontroverse zu definieren und Konversationsarchetypen hervorzuheben, die sich aus Interaktionsmustern ergeben. Wir nutzen unser Feature-Modell, um ein Logistischer Regression Verfahren zu entwickeln, das zukünftige Kontroversen in Reddit-Communities in den Themenbereichen Politik, Weltnachrichten, Sport und persönliche Beziehungen vorhersagen kann. Schlussendlich bietet unser Modell auch die Grundlage für eine Vergleichbarkeit verschiedener Communities im Gesundheitsbereich, auch wenn dort die Themen und die Nutzeraktivität, trotz des gemeinsamen Gesamtfokus, erheblich variieren. In jedem der genannten Themenbereiche gibt unser Framework Erkenntnisgewinne, wie das Verhalten der Nutzer die spezifisch Definition von Kontroversen der Community prägt

Engagement in a newly launched online support community for complex regional pain syndrome: membership growth, header analysis and introductory messages

Several studies have investigated forum engagement, with a small but growing body of research focusing on the number of people using a forum (membership growth), how they use it (header analysis) and how they introduce themselves (introductory messages). Most studies use established forums and little is known about newly launched forums. This study examines engagement in a newly launched forum for complex regional pain syndrome. Results showed that membership growth occurred in bursts that were closely linked to promotional strategies. Header analysis showed the forum was used throughout the day, evening and night, with a focus on providing support as well as receiving it. Introductory messages took the form of disease stories with six themes: personal details, precipitating event, symptoms, treatment, living with CRPS, and reasons for joining. Implications and areas for future research are discussed

Engagement Patterns of Peer-to-Peer Interactions on Mental Health Platforms

Mental illness is a global health problem, but access to mental healthcare resources remain poor worldwide. Online peer-to-peer support platforms attempt to alleviate this fundamental gap by enabling those who struggle with mental illness to provide and receive social support from their peers. However, successful social support requires users to engage with each other and failures may have serious consequences for users in need. Our understanding of engagement patterns on mental health platforms is limited but critical to inform the role, limitations, and design of these platforms. Here, we present a large-scale analysis of engagement patterns of 35 million posts on two popular online mental health platforms, TalkLife and Reddit. Leveraging communication models in human-computer interaction and communication theory, we operationalize a set of four engagement indicators based on attention and interaction. We then propose a generative model to jointly model these indicators of engagement, the output of which is synthesized into a novel set of eleven distinct, interpretable patterns. We demonstrate that this framework of engagement patterns enables informative evaluations and analysis of online support platforms. Specifically, we find that mutual back-and-forth interactions are associated with significantly higher user retention rates on TalkLife. Such back-and-forth interactions, in turn, are associated with early response times and the sentiment of posts.Comment: Accepted to ICWSM 202

Social recognition provision patterns in professional Q&A forums in Healthcare and Construction

© 2015 Elsevier Ltd. All rights reserved. For some decades, professional Q&A forums have been used as a mainstream way of sharing practices between novices and experts. Several forums have had time to develop their own communities and habits, which made them a suitable place to explore patterned epistemic practices. In this paper we look at the social recognition, help seeking and informal learning patterns in communities of practice; our aim is to use the corresponding outputs to scaffold technology supported informal learning. We analyzed professional discussion forums in two countries (UK and Germany) in two different sectors (Healthcare and Construction). We identified a set of interrelated patterns that are used for socially verifying and maturing rules and guidelines, solving problems, introducing new practices and triggering learning. Some particular social recognition and learning trends common in Healthcare and Construction sector Q&A forums are highlighted. We discuss epistemic practice pattern networks for developing scaffolds to enhance the quality of informal learning in workplace environments in an integrated way. We suggest and validate empirically a model of social recognition provision in Q&A forums

UNDERSTANDING INFORMATION USE IN ONLINE CONSUMER-HEALTH SUPPORT GROUPS: A LOOK INTO INTERACTIVE HEALTH COMMUNICATIONS

UNDERSTANDING INFORMATION USE IN ONLINE CONSUMER HEALTH SUPPORT GROUPS: A LOOK INTO INTERACTIVE HEALTH COMMUNICATIONS The exponential growth of the Internet in the past two decades has been accompanied by an increased interest by Internet users in communicating among each other electronically about all sorts of topics, including health-related issues. This increased interest in peer-to-peer communication for health topics raised lots of questions about the potential harmful effects of these communications on those participants who might take some health-related action without consulting with a doctor first. This potential problem has motivated the researcher to investigate how people with certain health conditions use health information that they obtain from online support groups. Even though the understanding of how information is sought, retrieved, and ultimately used is a very important topic within information behavior research, information use is an area that has seen less study. For this reason, the researcher decided to investigate information use within online consumer health support groups using a content analytical approach. The study had two specific objectives: (a) to describe what some of the cognitive, affective, and behavioral actions that consumers indicate they had taken based on information shared within some of the online support groups to which they belong; and (b) to determine if the uses given to information follow any pattern among different chronic conditions being studied with relation to the type of questions asked, the type of reply messages, and the health-related content of the messages. Methodologically, the study used computer-mediated discourse analysis to guide collection of trace data that came from archives of selected online discussion boards related to the three chronic conditions chosen for the study. For data to be part of the study, the presence of interactions with indications of usefulness was necessary. Then, through content analysis, the data was coded using several classification schemas found in the literature, some of them in their original form, others adapted to fit this research purpose. These schemas looked into the types of questions asked, the functions of the reply messages, the type of medical content of the posted messages, and the type of use given to the information. Once all the data was processed, the researcher looked for patterns among the different variables and across the different gender-based chronic conditions. Results of the analysis show that the message characteristics of content type, function of reply messages, and question types, have a significant relationship with the types of conditions. Message characteristics also show a significant relationship with the cognitive, affective, and behavioral information uses. Discussions of the results as well as some alternatives for future research are presented. Enter Abstract here late

Investigating and Supporting Sensemaking within Online Health Communities

This dissertation focuses on understanding and supporting individual and collective sensemaking within online health communities (OHCs). This major goal was achieved in three aims. In Aim 1, this dissertation contributes a rich descriptive account of collective sensemaking in OHCs forums by describing how it occurs and develops, what triggers it, what elements constitute collective construction of meaning, and what conversational moves positively contribute to this process. Further, it describes how collective sensemaking in OHCs is impacted by the interplay between informational and socio-emotional needs of OHCs members. Moreover, it examines how design of different social computing platforms influences OHCs members’ ability to meet their informational and socio-emotional needs and engage in collective sensemaking. In Aim 2, this dissertation explores the design space of tools for supporting individual sensemaking through optimized information access. Through the design and evaluation of a prototype DisVis it examines the impact of such tools on OHCs members’ ability to understand information within discussion threads. In the final Aim 3, this dissertation proposes a novel approach for meeting the three main needs identified in Aims 1 and 2: promoting individual sensemaking, while at the same time encouraging collective sensemaking, and facilitating development of social awareness and ties among community members. The design and evaluation of the novel solution for visualizing discussion threads that synergistically addresses these three needs—dSense—provides insights for future research and design of interactive solutions for supporting individual and collective sensemaking within OHCs

User Dynamics in Mental Health Forums – A Sentiment Analysis Perspective

Individuals around the world in need of mental healthcare do not find adequate treatment because of lacking resources. Since the necessary support can often not be provided directly, many turn to the Internet for assistance, whereby mental health forums have evolved into an important medium for millions of users to share experiences. Information Systems research lacks empirical evidence to analyze how health forums influence users’ moods. This paper addresses the research gap by conducting sentiment analysis on a large dataset of user posts from three leading English-language forums. The goal of this study is to shed light on the mood effects of mental health forum participation, as well as to better understand user roles. The results of our exploratory study show that sentiment scores develop either positively or negatively depending on the condition. We additionally investigate and report on user forum roles

Technologies for electronically Assisting Nursing Communication

New information and computing technologies promise new virtual learnin g and communication opportunities within the real communities of health care professionals. The Assisted Electronic Communication project has been prototyping , administering and evaluating an integrated digital discourse, webcasting and digital newsletter system, for health care professionals within one such community - an acute UK National Health Service Hospital. The first two of these systems are discussed in this paper. The principal group of health care staff participating in this study were nurses, who were able to access and contribute to threaded, asynchronous discussions and themed information in the context of critical work documents, view and interact with live webcasts by key hospital personnel, and view and submit stories to an online newsletter. The system has been evaluated very positively, and is seen by staff as a way of critically engaging with new material that is getting closer to an idealized vision of learning in the workplace

Impact of Heterogeneous Prior Contribution on Reciprocity in Online Sellers’ Community

This study explores how different types of resources a seller contributes in the online community will trigger others’ reciprocity, reflected by the responses the seller’s threads receive. Drawing on social exchange theory and using machine learning techniques, we identify two important types of resources transferred in an online sellers’ community: informational resource and instrumental resource. Our findings reveal that a seller’s provision of informational resource is positively associated with the responses the seller’s threads receive, while a seller’s provision of instrumental resource is negatively associated with the responses the seller’s threads receive. Moreover, the effect is moderated by the types of resources sought by a certain thread. Specifically, both effects are strengthened for threads that seek informational resource and undermined for threads that seek instrumental resource. The study contributes to the understanding of online reciprocity by uncovering the differing impact of different contribution and its boundary condition