Elephant in the Room: Dissecting and Reflecting on the Evolution of Online Social Network Research

2024-11-20

International Conference on Web and Social Media

AI Summary

Online Social Network (OSN) research has long suffered from scarce data access and platform bias—particularly overreliance on Twitter—resulting in methodological stagnation and weak empirical foundations. Method: We systematically analyze 13,842 OSN papers published between 2006 and 2023, constructing Minerva-OSN—the first open-source, structured metadata dataset for the field—and conduct the first end-to-end academic graph analysis, integrating topic modeling, interactive visualization, and empirical consensus from 50 domain experts. Contribution/Results: Our analysis identifies three critical bottlenecks: low data accessibility, platform monoculture, and methodological obsolescence. We propose a cross-sectoral improvement framework with 12 actionable recommendations, explicitly advocating platform–researcher collaboration. All datasets and analytical tools are publicly released, establishing foundational infrastructure and governance pathways to catalyze a paradigm shift in OSN research.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Application Domains: Humanities & Computational Social Science
• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Billions of individuals engage with Online Social Networks (OSN) daily. The owners of OSN try to meet the demands of their end-users while complying with business necessities. Such necessities may, however, lead to the adoption of restrictive data access policies that hinder research activities from "external"' scientists---who may, in turn, resort to other means (e.g., rely on static datasets) for their studies. Given the abundance of literature on OSN, we - as academics - should take a step back and reflect on what we have done so far, after having written thousands of papers on OSN. This is the first paper that provides a holistic outlook to the entire body of research that focused on OSN - since the seminal work by Acquisti and Gross (2006). First, we search through over 1 million peer-reviewed publications, and derive 13,842 papers that focus on OSN: we organize the metadata of these works in the Minerva-OSN dataset, the first of its kind - which we publicly release. Next, by analyzing Minerva-OSN, we provide factual evidence elucidating trends and aspects that deserve to be brought to light - such as the predominant focus on Twitter or the difficulty in obtaining OSN data. Finally, as a constructive step to guide future research, we carry out an expert survey (n=50) with established scientists in this field, and coalesce suggestions to improve the status quo - such as an increased involvement of OSN owners. Our findings should inspire a reflection to "rescue" research on OSN. Doing so would improve the overall OSN ecosystem, benefiting both their owners and end-users - and, hence, our society.

Problem

• Analyzing evolution and trends in Online Social Network research
• Addressing restrictive data access policies hindering external research
• Proposing solutions to improve future OSN research ecosystem

Innovation

• Created Minerva-OSN dataset from 13,842 papers
• Analyzed trends via Minerva-OSN metadata
• Conducted expert survey with 50 scientists