Led it on Reddit: An exploratory study examining opinion leadership on Reddit

Kilgo, Danielle K.; Yoo, Joseph; Sinta, Vinicio; Geise, Stephanie; Suran, Melissa; Johnson, Thomas J.

doi:10.5210/fm.v21i9.6429

Cited by 15 publications

(15 citation statements)

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“…Our results regarding Karma scores also differ from conclusions drawn by Kilgo et al (2016), who suggested that Karma scores may be used to identify opinion leaders (i.e. highly connected individuals).…”

Section: Discussioncontrasting

confidence: 99%

Learning in the Wild: Understanding Networked Ties in Reddit

Valle

Gruzd

Kumar

et al. 2020

Research in Networked Learning

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Section: Discussioncontrasting

confidence: 99%

Learning in the Wild: Understanding Networked Ties in Reddit

Valle

Gruzd

Kumar

et al. 2020

Research in Networked Learning

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“…There are no functions for users to share or like others' content, and besides the date of registration and the number of published posts, there are no metrics that, for instance, signal user's "karma," like on Reddit (cf. Kilgo et al, 2016). There are also vast possibilities for user anonymity; forum regulations even state that it is forbidden to disclose other users' identities (Flashback, 2020).…”

Section: Case Studymentioning

confidence: 99%

Influence Without Metrics: Analyzing the Impact of Far-Right Users in an Online Discussion Forum

Åkerlund

2021

Social Media + Society

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The study presented in this article explores the processes through which influence takes shape in eclectic online forums with few vanity metrics. Using a dataset of 7.5 million posts in the large Swedish online discussion forum Flashback, it explores who becomes influential, their strategies for appealing to the community, and others’ support of them. While it has been known that Flashback hosts far-right users and content, the current study shows that these sentiments are not fringe or obscure, but instead seemingly widely supported and influential in the forum. It illustrates that the influential users—those who are supported and acknowledged by others as important—exclusively and continuously expressed far-right ideas and displayed an embeddedness within the far-right, as well as in the forum’s culture. The study finds that despite few visible markers, many users learned to recognize influential users and their far-right content as worthy of support. In the absence of built-in functions, some users engaged in manual “liking” and “sharing” of influential users’ content via their replies, acknowledging it as a way to legitimize them. At the same time, the analysis showcased how a lack of vanity metrics countered potential echo chamber effects in the forum as disliked users—advocating progressive gender and immigration ideas—were unintentionally amplified by those who attempted to silence them. The article also discusses the role of Flashback as a platform in the proliferation of hate.

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“…Sophisticated work has been conducted by computer scientists on automatic the process of crediblization or vetting of content. Research on Amazon reviews (e.g., Liang, 2016;Walther, Liang, Ganster, Wohn, & Emington, 2012), on Slashdot (e.g., Gosain, 2003;Stevenson, 2016) and Reddit (e.g., Kilgo et al, 2016;Lim, Carman, & Wong, 2017;Park, Conway, & Chen, 2018), as well as on social media likes, shares, and comments (e.g., Alhabash, Almutairi, Lou, & Kim, 2018;Liang & Kee, 2018), have opened the road to a very productive line of research. The core research agenda item is discovering methods to identify and mitigate or eliminate false or malevolent content or evaluations.…”

Section: Trust Credibility and Transparencymentioning

confidence: 99%

Computational communication research

Matei

Kee

2019

WIREs Data Min & Knowl

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The article advances a new way to think about computational research, in general, and computational communication research, in particular. Contrary to current definitions of “computational science,” which emphasizes its inductive nature, we define computational research as an incomplete inductive process, blending both theoretical and data‐driven methods of discovery. Communication theory needs to be driven by a clear concept of human needs and abilities, recovering and extending known theoretical insights from mass and interpersonal communication research. The definition we propose for computational communication research has a practical implication. Relying on theory, the definition demands to identify specific processes and domains within the field of computational communication research. The processes include communication production, behavior, and effects. The domains include collaboration, trust, and data storytelling and journalism, while the methods include content and network analyses. The article starts with a broad definition of the “computational” approach, using the Johari window. We continue with a typology of computational communication research, which blends reviews of foundational texts with summaries of leading research. In the conclusions, we discuss the strengths and identify new opportunities in the field of computational communication research. This article is categorized under: Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction Commercial, Legal, and Ethical Issues > Social Considerations Fundamental Concepts of Data and Knowledge > Big Data Mining

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Led it on Reddit: An exploratory study examining opinion leadership on Reddit

Cited by 15 publications

References 0 publications

Learning in the Wild: Understanding Networked Ties in Reddit

Learning in the Wild: Understanding Networked Ties in Reddit

Influence Without Metrics: Analyzing the Impact of Far-Right Users in an Online Discussion Forum

Computational communication research

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