论文标题
意见市场模型：使用积极干预措施来阻止极右派的意见传播
Opinion Market Model: Stemming Far-Right Opinion Spread using Positive Interventions
论文作者
论文摘要
在线极端主义会带来严重的社会后果，包括使仇恨言论，用户激进化和增加社会分裂的正常化。已经探索了各种缓解策略来解决这些后果。这样的策略使用积极的干预措施：受控信号，以增加对意见生态系统的关注以提高某些意见。为了评估积极干预措施的有效性，我们介绍了意见市场模型（OMM），这是一个两层在线意见生态系统模型，既考虑开局互动互动又是积极干预的作用。意见注意市场的规模是使用多元离散时间霍克斯流程以第一层建模的；在第二层中，使用市场份额吸引模型的关注有限，观点会合作并争夺市场份额。我们证明了我们在合成数据集上提出的估计方案的收敛性。接下来，我们对两个学习任务进行测试，该任务适用于两个现实世界数据集，以预测注意市场份额并发现在线项目之间的潜在关系。第一个数据集包括Facebook和Twitter讨论，其中包含有关丛林大火和气候变化的中等和极右翼意见。第二个数据集捕获了流行的VEVO艺术家的YouTube和Twitter注意力量。 OMM的表现优于数据集上的最先进的预测模型，并捕获了潜在的合作竞争关系。我们发现了（1）对丛林大火的极右翼和温和意见之间的自我和跨增强，以及（2）与现实世界互动相关的成对艺术家关系，例如协作和持久的仇恨。最后，我们将OMM用作积极干预措施的测试，并展示了媒体覆盖范围如何调节极端意见的传播。
Online extremism has severe societal consequences, including normalizing hate speech, user radicalization, and increased social divisions. Various mitigation strategies have been explored to address these consequences. One such strategy uses positive interventions: controlled signals that add attention to the opinion ecosystem to boost certain opinions. To evaluate the effectiveness of positive interventions, we introduce the Opinion Market Model (OMM), a two-tier online opinion ecosystem model that considers both inter-opinion interactions and the role of positive interventions. The size of the opinion attention market is modeled in the first tier using the multivariate discrete-time Hawkes process; in the second tier, opinions cooperate and compete for market share, given limited attention using the market share attraction model. We demonstrate the convergence of our proposed estimation scheme on a synthetic dataset. Next, we test OMM on two learning tasks, applying to two real-world datasets to predict attention market shares and uncover latent relationships between online items. The first dataset comprises Facebook and Twitter discussions containing moderate and far-right opinions about bushfires and climate change. The second dataset captures popular VEVO artists' YouTube and Twitter attention volumes. OMM outperforms the state-of-the-art predictive models on both datasets and captures latent cooperation-competition relations. We uncover (1) self- and cross-reinforcement between far-right and moderate opinions on the bushfires and (2) pairwise artist relations that correlate with real-world interactions such as collaborations and long-lasting feuds. Lastly, we use OMM as a testbed for positive interventions and show how media coverage modulates the spread of far-right opinions.
