Mitigating selective exposure in social media forums

This dissertation focuses on designing social media interfaces to help people explore diverse social opinions and mitigate selective exposure - a tendency that people actively seek attitude-consistent information and avoid attitude-inconsistent information. Diverse information consumption has potential benefits, including but not limited to helping individuals form accurate viewpoints, facilitating better decision-making processes, cultivating people's tolerance and mutual understanding with others, which is essential for a thriving democratic society. Both actively seeking congenial information (i.e., selective exposure) and passively being in a congenial information environment (i.e., de facto selective exposure) can impair people's exposure to diverse social opinions. Meanwhile, people can play a significant role in shaping others' information environments by sharing information on social media. Thus, we break our general research problem down to two sub-problems: 1) designing interfaces to mitigate selective exposure for individual information consumption, which focuses on the effect of interface design on people's active information-seeking behavior; 2) understanding humans' role as the information filter for others, which is the first step towards a better interface design to tackle potential problems caused by information sharing among people. We first proposed organizing and showing categorized social opinions based on emotional reactions to mitigate selective exposure for individual information consumption. Our evaluation indicated that such a design could motivate people to explore diverse social opinions. Next, we designed and implemented a system that can provide novel visual hints with new recommendation mechanisms to improve people's awareness of diverse opinions and mitigate selective exposure. Finally, we studied the effect of the stance label and the credibility label on people's information selection and perception on a two-column news feed. We found that the stance label can exacerbate selective exposure and make people agree more on fake news. And the credibility label has a limited effect on mitigating selective exposure and combating fake news. Our work expanded the design toolbox of mitigating selective exposure and gave interface/system designers more choices when using these tools. To better understand people's role as the information filter, we conducted a simulated online experiment to figure out how the attitude distribution of the recipient group affects people's information-sharing behavior in the anonymous scenario. We observed that the attitude distribution of the recipient group has an impact on people’s sharing behavior even though various factors (e.g., topics, people's attitudes, etc.) may be related to such effect. People tend to cater to the majority's attitudes by selectively sharing more information consistent with the majority's attitudes in some specific context, which creates the filter bubble for others. This result indicated the necessity to study interface design to motivate people to share more balanced information to help break the filter bubble for those recipients

Networked, Collaborative, and Activist News Communities Online: A Case Study of Reddit and Daily Kos

Participatory democracy depends on formations of community and social relations, places and spaces for critical discourse, and the organizational and technical capacity for collective action. This study seeks to better understand how these processes are at work in the virtual realm, and more broadly examine the changing nature of political information and discourse in the online context. Toward this end, I examine two sites that embody different yet highly successful models of user participation, collective content production, and increasingly, political action: the political blogging community of Daily Kos and the social news site Reddit. This study is based on three broad theoretical frameworks of community, discourse, and action. I use work by Michele Willson (2006) to explore how community exists in the virtual realm. Drawing upon the scholarship of Jurgen Habermas (1991) and more recent adaptations and extensions of Habermasian public sphere theory from Aaron Barlow (2006), I ask can online communities set the foundation for a public spheres, and if so, how do they function as virtual public spheres? Building upon understandings of online community and virtual public sphere(s), I utilize work by Manuel Castells (1997) and Jeffrey Juris (2005) to understand how community and discourse can enable collective action. These lines of analysis provide the structure through which I examine Daily Kos and Reddit. Using ethnographic methods, I place the voices and perspectives of users within this theoretical structure to produce a comprehensive look at the function of collaborative online political information communities

Unsupervised Content-Based Characterization and Anomaly Detection of Online Community Dynamics

The structure and behavior of human networks have been investigated and quantitatively modeled by modern social scientists for decades, however the scope of these efforts is often constrained by the labor-intensive curation processes that are required to collect, organize, and analyze network data. The surge in online social media in recent years provides a new source of dynamic, semi-structured data of digital human networks, many of which embody attributes of real-world networks. In this paper we leverage the Reddit social media platform to study social communities whose dynamics indicate they may have experienced a disturbance event. We describe an unsupervised approach to analyzing natural language content for quantifying community similarity, monitoring temporal changes, and detecting anomalies indicative of disturbance events. We demonstrate how this method is able to detect anomalies in a spectrum of Reddit communities and discuss its applicability to unsupervised event detection for a broader class of social media use cases

From Apathy to Algoactivism: Worker Resistance to Algorithmic Control in Food Delivery Platforms

Platforms in the gig economy rely on algorithmic control to manage their workforce, but recent scientific evidence has shown that workers have begun to resist this control. Due to lacking focus and limited empirical data, the phenomenon of worker resistance to algorithmic control is still insufficiently understood. Based on a topic modeling approach with over 2 million text documents extracted from Reddit forums of different food-delivery platforms, we identify 14 resistance actions showing how food-delivery workers resist algorithmic control. Our study contributes to current research by expanding the understanding of resistance to algorithmic control in the gig economy, showing what resistant actions workers take, and discussing the concepts of individual opacity and collective knowledge as possible escalators and de-escalators of this resistance

Argumentation dialogues in web-based GDSS: an approach using machine learning techniques

Tese de doutoramento em InformaticsA tomada de decisão está presente no dia a dia de qualquer pessoa, mesmo que muitas vezes ela não tenha consciência disso. As decisões podem estar relacionadas com problemas quotidianos, ou podem estar relacionadas com questões mais complexas, como é o caso das questões organizacionais. Normalmente, no contexto organizacional, as decisões são tomadas em grupo. Os Sistemas de Apoio à Decisão em Grupo têm sido estudados ao longo das últimas décadas com o objetivo de melhorar o apoio prestado aos decisores nas mais diversas situações e/ou problemas a resolver. Existem duas abordagens principais à implementação de Sistemas de Apoio à Decisão em Grupo: a abordagem clássica, baseada na agregação matemática das preferências dos diferentes elementos do grupo e as abordagens baseadas na negociação automática (e.g. Teoria dos Jogos, Argumentação, entre outras). Os atuais Sistemas de Apoio à Decisão em Grupo baseados em argumentação podem gerar uma enorme quantidade de dados. O objetivo deste trabalho de investigação é estudar e desenvolver modelos utilizando técnicas de aprendizagem automática para extrair conhecimento dos diálogos argumentativos realizados pelos decisores, mais concretamente, pretende-se criar modelos para analisar, classificar e processar esses dados, potencializando a geração de novo conhecimento que será utilizado tanto por agentes inteligentes, como por decisiores reais. Promovendo desta forma a obtenção de consenso entre os membros do grupo. Com base no estudo da literatura e nos desafios em aberto neste domínio, formulou-se a seguinte hipótese de investigação - É possível usar técnicas de aprendizagem automática para apoiar diálogos argumentativos em Sistemas de Apoio à Decisão em Grupo baseados na web. No âmbito dos trabalhos desenvolvidos, foram aplicados algoritmos de classificação supervisionados a um conjunto de dados contendo argumentos extraídos de debates online, criando um classificador de frases argumentativas que pode classificar automaticamente (A favor/Contra) frases argumentativas trocadas no contexto da tomada de decisão. Foi desenvolvido um modelo de clustering dinâmico para organizar as conversas com base nos argumentos utilizados. Além disso, foi proposto um Sistema de Apoio à Decisão em Grupo baseado na web que possibilita apoiar grupos de decisores independentemente de sua localização geográfica. O sistema permite a criação de problemas multicritério e a configuração das preferências, intenções e interesses de cada decisor. Este sistema de apoio à decisão baseado na web inclui os dashboards de relatórios inteligentes que são gerados através dos resultados dos trabalhos alcançados pelos modelos anteriores já referidos. A concretização de cada um dos objetivos permitiu validar as questões de investigação identificadas e assim responder positivamente à hipótese definida.Decision-making is present in anyone’s daily life, even if they are often unaware of it. Decisions can be related to everyday problems, or they can be related to more complex issues, such as organizational issues. Normally, in the organizational context, decisions are made in groups. Group Decision Support Systems have been studied over the past decades with the aim of improving the support provided to decision-makers in the most diverse situations and/or problems to be solved. There are two main approaches to implementing Group Decision Support Systems: the classical approach, based on the mathematical aggregation of the preferences of the different elements of the group, and the approaches based on automatic negotiation (e.g. Game Theory, Argumentation, among others). Current argumentation-based Group Decision Support Systems can generate an enormous amount of data. The objective of this research work is to study and develop models using automatic learning techniques to extract knowledge from argumentative dialogues carried out by decision-makers, more specifically, it is intended to create models to analyze, classify and process these data, enhancing the generation of new knowledge that will be used both by intelligent agents and by real decision-makers. Promoting in this way the achievement of consensus among the members of the group. Based on the literature study and the open challenges in this domain, the following research hypothesis was formulated - It is possible to use machine learning techniques to support argumentative dialogues in web-based Group Decision Support Systems. As part of the work developed, supervised classification algorithms were applied to a data set containing arguments extracted from online debates, creating an argumentative sentence classifier that can automatically classify (For/Against) argumentative sentences exchanged in the context of decision-making. A dynamic clustering model was developed to organize conversations based on the arguments used. In addition, a web-based Group Decision Support System was proposed that makes it possible to support groups of decision-makers regardless of their geographic location. The system allows the creation of multicriteria problems and the configuration of preferences, intentions, and interests of each decision-maker. This web-based decision support system includes dashboards of intelligent reports that are generated through the results of the work achieved by the previous models already mentioned. The achievement of each objective allowed validation of the identified research questions and thus responded positively to the defined hypothesis.I also thank to Fundação para a Ciência e a Tecnologia, for the Ph.D. grant funding with the reference: SFRH/BD/137150/2018

Harnessing Collaborative Technologies: Helping Funders Work Together Better

This report was produced through a joint research project of the Monitor Institute and the Foundation Center. The research included an extensive literature review on collaboration in philanthropy, detailed analysis of trends from a recent Foundation Center survey of the largest U.S. foundations, interviews with 37 leading philanthropy professionals and technology experts, and a review of over 170 online tools.The report is a story about how new tools are changing the way funders collaborate. It includes three primary sections: an introduction to emerging technologies and the changing context for philanthropic collaboration; an overview of collaborative needs and tools; and recommendations for improving the collaborative technology landscapeA "Key Findings" executive summary serves as a companion piece to this full report

The rhetorical constitution of online community: Identification and constitutive rhetoric in the community of reddit

The concepts of online identity and online community within the context of social media have been major research interests in the field of communication in recent years. Questions of interest include how the Internet and social media contribute to the construction of identity both online and offline, and what factors encourage participation in and contribution to online communities. This thesis will address these questions related to online identity and community from a rhetorical perspective to examine the role rhetoric plays in these processes and build on the application of rhetorical approaches to online contexts. Specifically, this project proposes a rhetorical analysis of the online community of Reddit, which encourages its users to submit and vote on content that is valued by the overall community. The analysis will focus on the use of identification and constitutive rhetoric in both the communication Reddit provides about itself and the everyday communication of its members. Overall, this thesis argues that identification and constitutive rhetoric create a strong collective identity within the community that contributes to the loyalty and commitment of its members, but also constrains its members\u27 behavior within the community in ways that are consistent with this identity, which ultimately may create challenges to the community\u27s continued success. However, this thesis also finds evidence of dissent from some of Reddit\u27s established guidelines, which creates tension between those who adhere to Reddit\u27s unified, constituted identity and those who choose to ignore or deviate from it