AI 中文总结
研究社交媒体上网络欺凌规范传播，利用Instagram等数据发现前一条CB评论增加下条评论为CB几率，CB→CB对文本更相似，非攻击性回复随前条CB严重程度变负面，这些发现可复制，单一特征能更好预测，为实时审核提供信号。
AI 中文摘要
虽然网络欺凌（CB）的某些方面，如影响因素和流行程度，已得到广泛研究，但对于欺凌行为如何从一条评论转移到另一条评论，关注较少。而这一理解对设计更好的反欺凌功能具有重要意义。本文研究了社交媒体会话中这种攻击转移性质的多个方面。利用来自430个Instagram会话的32754对连续评论数据，发现前一条CB评论会大幅增加下一条评论为CB的几率，主要由跨用户传播导致，且通过会话固定效应控制得到证实。还发现CB→CB对在文本上比NoCB→CB对更相似，且在排除共享词汇、会话毒性和会话长度等混淆因素的匹配跨会话基线情况下依然成立。此外，随着前一条CB严重程度增加，非攻击性回复变得更负面。这些关键发现在三个独立数据集（Reddit、Wikipedia Detox和SOCC）中得到复制，溢出率与平台可见性设计相关。最后表明，一个单一的二元特征（前一条评论是否为CB）比会话级基线和微调后的HateBERT分类器能更好地进行预测，可作为针对传播链而非个别违规者的实时审核信号。
英文摘要
While certain aspects of cyberbullying (CB) such as its factors and prevalence have been studied extensively, relatively little attention has been given to specifically how the aggression transfers from comment to comment. This understanding could have important implications for designing better anti-bullying features. In this paper, we study multiple aspects of the nature of this aggression transference in social media sessions. Using data from 32,754 consecutive comment pairs from 430 Instagram sessions, we find that a preceding CB comment substantially raises the odds of the next comment being CB, an effect confirmed by session fixed-effects controls and driven primarily by cross-user spread. We also find that $\text{CB} \to \text{CB}$ pairs are more textually similar than $\text{NoCB} \to \text{CB}$ pairs across five complementary methods, and that this pattern holds under a matched cross-session baseline that rules out shared vocabulary, session toxicity, and session length as confounds. Moreover, non-aggressive replies grow more negative as preceding CB severity increases, a graded pattern consistent with automatic emotional influence below the threshold of overt aggression. These key findings replicate across three independent datasets (Reddit, Wikipedia Detox, and SOCC), with spillover rates that track platform visibility design. Finally, we show that a single binary feature (whether the prior comment was CB) improves prediction over session-level baselines and over a fine-tuned HateBERT classifier, serving as a real-time moderation signal that targets the spreading chain rather than individual offenders.
Comments15 pages, 1 figure, 6 tables, Accepted at The 18th International Conference on Advances in Social Networks Analysis and Mining
