Backfire Effect揭示在线媒体中的早期争议
Backfire Effect Reveals Early Controversy in Online Media
AI总结：
本文针对在线争议检测，提出“点赞上升梯度”等新特征，发现其与心理“backfire effect”相关，在多语言媒体中表现优异且适用于早期检测。
摘要：
在线媒体的快速发展极大促进了公众的信息消费、知识获取和意见交流。然而，它也导致了在线讨论中更激烈的冲突。因此，争议检测对计算和社会科学变得重要。以往关于检测方法的研究主要集中在更大的数据集和更复杂的计算模型上，但很少检查冲突的潜在机制，特别是其背后的心理动机。在本文中，我们提出证据表明，冲突帖子往往具有高比例的“点赞上升梯度”，即回复比评论获得更多点赞。此外，相邻层级之间的回复数量也存在梯度。我们开发了两个新的梯度特征，并证明了我们的特征在争议检测模型方面的共同增强效果。此外，使用多种评估算法在多个中文和英文媒体上比较了结构、交互和文本特征与新特征。结果表明，梯度特征在争议方面存在显著差异且比其他特征更重要，这是一个普遍情况。更深入地，我们讨论了上升梯度出现的机制，表明该情况与最近受到关注的意识形态冲突中的“backfire effect”有关。由心理机制形成的特征在仅考虑少数热门信息或早期信息的应用场景中也表现出出色的检测性能。我们的发现可为在线冲突行为分析和早期检测提供新视角。
英文摘要：
The rapid development of online media has significantly facilitated the public's information consumption, knowledge acquisition, and opinion exchange. However, it has also led to more violent conflicts in online discussions. Therefore, controversy detection becomes important for computational and social sciences. Previous research on detection methods has primarily focused on larger datasets and more complex computational models but has rarely examined the underlying mechanisms of conflict, particularly the psychological motivations behind them. In this paper, we present evidence that conflicting posts tend to have a high proportion of "ascending gradient of likes", i.e., replies get more likes than comments. Additionally, there is a gradient in the number of replies between the neighboring tiers as well. We develop two new gradient features and demonstrate the common enhancement effect of our features in terms of controversy detection models. Further, multiple evaluation algorithms are used to compare structural, interactive, and textual features with the new features across multiple Chinese and English media. The results show that it is a general case that gradient features are significantly different in terms of controversy and are more important than other features. More thoroughly, we discuss the mechanism by which the ascending gradient emerges, suggesting that the case is related to the "backfire effect" in ideological conflicts that have received recent attention. The features formed by the psychological mechanism also show excellent detection performance in application scenarios where only a few hot information or early information are considered. Our findings can provide a new perspective for online conflict behavior analysis and early detection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
