Search arXivSearch

arXiv · 2401.17840

Propagation Dynamics of Rumor vs. Non-rumor across Multiple Social Media Platforms Driven by User Characteristics

Abstract

Studying information propagation dynamics in social media can elucidate user behaviors and patterns. However, previous research often focuses on single platforms and fails to differentiate between the nuanced roles of source users and other participants in cascades. To address these limitations, we analyze propagation cascades on Twitter and Weibo combined with a crawled dataset of nearly one million users with authentic attributes. Our preliminary findings from multiple platforms robustly indicate that rumors tend to spread more deeply, while non-rumors distribute more broadly. Interestingly, we discover that the spread of rumors is slower, persists longer, and, in most cases, involves fewer participants than that of non-rumors. And an undiscovered highlight is that reputable active users, termed `onlookers', inadvertently or unwittingly spread rumors due to their extensive online interactions and the allure of sensational fake news. Conversely, celebrities exhibit caution, mindful of releasing unverified information. Additionally, we identify cascade features aligning with exponential patterns, highlight the Credibility Erosion Effect (CEE) phenomenon in the propagation process, and discover the different contents and policies between the two platforms. Our findings enhance current understanding and provide a valuable statistical analysis for future research.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Dongpeng Hou, Shu Yin, Chao Gao, Xianghua Li, Zhen Wang. 2024-01-31. Propagation Dynamics of Rumor vs. Non-rumor across Multiple Social Media Platforms Driven by User Characteristics. https://arxiv.org/abs/2401.17840

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

The Benefit of Collective Intelligence in Community-Based Content Moderation is Limited by Overt Political Signalling

Social media platforms face increasing scrutiny over the rapid spread of misinformation. In response, many have adopted community-based content moderation systems, including Community Notes (formerly Birdwatch) on X (formerly Twitter), Community Notes on Meta, and Footnotes on TikTok. However, research shows that the current design of these systems can allow political biases to influence both the development of notes and the rating processes, reducing their overall effectiveness. We hypothesise that enabling users to collaborate on writing notes, rather than relying solely on individually authored notes, can enhance the overall quality of their notes. To test this idea, we conducted an online experiment in which participants jointly authored notes on politically misleading posts. We find that collaboration improves the helpfulness of notes, although the average effect depends on the interactional context. In particular, the benefits of collaboration decline when participants are made aware of one another's political affiliations. We also find that politically diverse teams improve note quality when evaluating Republican posts, while team composition does not meaningfully affect note quality for Democrat posts. These findings underscore the complexity of community-based content moderation and highlight the importance of understanding group dynamics and political diversity when designing more effective moderation systems.

cs.SI

The Same Ledger, Different Verdicts: How Measurement Specification Determines On-Chain Concentration

Whether a public blockchain is "decentralized" is routinely settled by citing a concentration statistic. On two ERC-20 ledgers, Chainlink (LINK) and Uniswap (UNI) over a 90-day window, we show that verdict depends on measurement specification rather than the ledger itself. Four discretionary choices (holder population, address type, temporal aggregation, and entity resolution) move the balance HHI for UNI from 109 to 2,336 (a factor of 21), with every specification defensible. Over the same range, the Gini coefficient moves by less than 0.003 and does not change under entity resolution, demonstrating that Gini and HHI answer different questions and cannot substitute for one another. We further document an implementation choice - summing versus overwriting repeated transfers - that discards roughly 85% of volume and overturns a finding on wealth and structural position. Substantively, both ledgers are extraordinarily unequal in ownership (balance Gini = 0.990 and 0.998) yet unconcentrated in routing (weekly flow HHI = 421 and 386), with the two dimensions close to statistically independent across addresses. A parameterized criterion for hidden brokers identifies 30 and 18 zero-balance intermediaries, 12 shared across ledgers; a matched control confirms that degree thresholding, rather than learned embeddings, drives the discovery. Finally, on the governance ledger, proposal-eligible addresses and routing intermediaries are almost disjoint, so routing contestability is held at the pleasure of a rule layer with a Nakamoto coefficient of two. We conclude that on-chain concentration should be reported as a specified range rather than a point estimate.

cs.SI

Optimal and heuristic strategies for evaluating the influence of coordinated behavior in information cascades and retweet networks

Coordinated Inauthentic Behavior (CIB) has become a major concern in online social platforms, yet its actual impact on information diffusion remains poorly understood. Existing research has primarily focused on detecting coordinated activity, while comparatively little attention has been devoted to quantifying its influence once detected. In this work, we introduce two complementary frameworks for the post-hoc evaluation of coordinated accounts. First, we formulate the problem on information cascades as a constrained influence maximization problem over directed trees and develop a polynomial-time dynamic programming algorithm that computes the optimal placement of coordinated nodes, providing an upper bound on their achievable influence. Second, motivated by the limited availability of diffusion cascades in real-world platforms, we propose a network-based framework that estimates influence directly from retweet networks using the independent cascade model and compares the observed placement of coordinated accounts against established heuristic baselines. We evaluate both approaches on Twitter/X data from the 2019 UK General Election and on a collection of verified state-backed information operation campaigns spanning multiple countries. While coordinated accounts exhibit limited influence in the UK cascades, the network-based analysis reveals substantial differences across campaigns, with several operations achieving influence comparable to or exceeding that of structurally central seed sets. Finally, by reconstructing cascades from the retweet networks, we show that the two frameworks produce consistent results, suggesting that the observed effects reflect intrinsic structural properties of coordinated activity rather than artifacts of the underlying methodology.

cs.SI