Loyalty in Online Communities

Loyalty is an essential component of multi-community engagement. When users have the choice to engage with a variety of different communities, they often become loyal to just one, focusing on that community at the expense of others. However, it is unclear how loyalty is manifested in user behavior, or whether loyalty is encouraged by certain community characteristics. In this paper we operationalize loyalty as a user-community relation: users loyal to a community consistently prefer it over all others; loyal communities retain their loyal users over time. By exploring this relation using a large dataset of discussion communities from Reddit, we reveal that loyalty is manifested in remarkably consistent behaviors across a wide spectrum of communities. Loyal users employ language that signals collective identity and engage with more esoteric, less popular content, indicating they may play a curational role in surfacing new material. Loyal communities have denser user-user interaction networks and lower rates of triadic closure, suggesting that community-level loyalty is associated with more cohesive interactions and less fragmentation into subgroups. We exploit these general patterns to predict future rates of loyalty. Our results show that a user's propensity to become loyal is apparent from their first interactions with a community, suggesting that some users are intrinsically loyal from the very beginning.Comment: Extended version of a paper appearing in the Proceedings of ICWSM 2017 (with the same title); please cite the official ICWSM versio

From Small to Big: Smartwatch Use in Mitigating COVID-19 – Understanding User Experience from Social Media Content Analysis

Smartwatches offer both functions and convenience that can have great potentials for technological interventions. Despite widespread discussion of technological interventions for COVID-19, smartwatch use has received little attention in the literature. This research aims to fill the literature gap by providing a broad understanding of smartwatch use for COVID-19 mitigation. We investigate smartwatch use through content analysis of the data collected from two social media platforms. The method allows us to draw on user experience beyond technological features and functions. In addition to functions, we also identified the concerns of using smartwatches for mitigating COVID-19. Furthermore, we uncovered both similarities and differences between the different social media platforms in terms of functions and concerns of smartwatch use. Our findings have implications for various stakeholders of the smartwatch technology and for mitigating the impact of the pandemic

Gender Effects in Online Low-Threshold Evaluations: Evidence from a Large-Scale Online Discussion-based Community

Online communities thrive on the basis of interactions between like-minded individuals, and usually involve some form of feedback or evaluations by peers. In these contexts, there is systematic evidence of gender-based biases in evaluations. How can such biases be attenuated? We study the efficacy of one approach—anonymization of gender information on the community. We use data from a large-scale digital discussion platform, Political Science Rumors, to examine the presence of gender bias. When users on the community post a discussion message, they are randomly assigned a pseudonym in the form of a given (or first name), such as “Daniel” or “Haylee,” and each post subsequently garners positive and negative votes from readers. We analyze the up votes, down votes, and net votes garnered by 1.4 million posts where names are randomly assigned to posters. We find that posts from randomly assigned “female” names receive 2.5% lower evaluation scores, all else equal. Further, when “female” users post emotive content with a negative tone, the posts receive disproportionately more negative evaluations

Reading In-Between the Lines: An Analysis of Dissenter

Efforts by content creators and social networks to enforce legal and policy-based norms, e.g. blocking hate speech and users, has driven the rise of unrestricted communication platforms. One such recent effort is Dissenter, a browser and web application that provides a conversational overlay for any web page. These conversations hide in plain sight - users of Dissenter can see and participate in this conversation, whereas visitors using other browsers are oblivious to their existence. Further, the website and content owners have no power over the conversation as it resides in an overlay outside their control. In this work, we obtain a history of Dissenter comments, users, and the websites being discussed, from the initial release of Dissenter in Feb. 2019 through Apr. 2020 (14 months). Our corpus consists of approximately 1.68M comments made by 101k users commenting on 588k distinct URLs. We first analyze macro characteristics of the network, including the user-base, comment distribution, and growth. We then use toxicity dictionaries, Perspective API, and a Natural Language Processing model to understand the nature of the comments and measure the propensity of particular websites and content to elicit hateful and offensive Dissenter comments. Using curated rankings of media bias, we examine the conditional probability of hateful comments given left and right-leaning content. Finally, we study Dissenter as a social network, and identify a core group of users with high comment toxicity.Comment: Accepted at IMC 202

Themes and Participants’ Role in Online Health Discussion: Evidence From Reddit

Health-related topics are discussed widely on different social networking sites. These discussions and their related aspects can reveal significant insights and patterns that are worth studying and understanding. In this dissertation, we explore the patterns of mandatory and voluntary vaccine online discussions including the topics discussed, the words correlated with each of them, and the sentiment expressed. Moreover, we explore the role opinion leaders play in the health discussion and their impact on participation in a particular discussion. Opinion leaders are determined, and their impact on discussion participation is differentiated based on their different characteristics such as their connections and locations in the social network, their content, and their sentiment. We apply social network analysis, topic modeling, sentiment analysis, machine learning, econometric analysis, and other techniques to analyze the collected data from Reddit. The results of our analyses show that sentiment is an important factor in health discussion, and it varies between different types of discussions. In addition, we identified the main topics discussed for each vaccine. Furthermore, the results of our study found that global opinion leaders have more influence compared to local opinion leaders in elevating the health discussion. Our study has important theoretical and practical implications