借助合成与代表性群体的智慧检测假新闻
Fake News Detection via Wisdom of Synthetic & Representative Crowds
AI总结：
本文提出结合在线调查、分层贝叶斯 MrP 与代表性加权的端到端方法，从多元人群视角检测 X 上的疫情假新闻，并估计美国各州及不同群体的分享差异。
摘要：
社交媒体公司一直难以提供一个具有民主正当性的“假新闻”定义。由于普遍的信任缺失和政治两极化，依赖专家判断的做法受到批评。依赖“群体智慧”的方法是一种具有成本效益、透明且包容的替代方案。本文提出了一种新颖的端到端方法，通过“合成与代表性群体的智慧”在 X 上检测假新闻。我们在 Lucid 平台上部署了一项在线调查，以从众包工作者那里收集若干与疫情相关推文的真实性评估。借鉴 MrP 文献，我们训练了一个分层贝叶斯模型，从目标人群中不同人物角色的视角预测每条推文的真实性。随后，我们根据代表性分层框架对预测的真实性评估进行加权，使关于“虚假”推文的决策能够代表所关注的整体政治共同体。基于这些聚合分数，我们分析了一个推文语料库，并执行第二次 MrP，以生成各州分享假新闻人数的估计值。我们发现美国各州在假新闻分享方面存在虽小但具有统计意义的异质性。在个体层面：一、分享假新闻总体上较为罕见，平均分享概率区间为[0.07,0.14]；二、有强有力证据表明民主党人分享的假新闻较少，相对于普通用户，其分享几率降低[57.3%,3.9%]；三、当采用共和党人对假新闻的定义时，后者表现出的假新闻分享倾向下降幅度为[50.8%,2.0%]；四、有一些证据表明女性比男性分享更少假新闻，该效应相当于下降[29.5%,4.9%]。
英文摘要：
Social media companies have struggled to provide a democratically legitimate definition of "Fake News". Reliance on expert judgment has attracted criticism due to a general trust deficit and political polarisation. Approaches reliant on the ``wisdom of the crowds'' are a cost-effective, transparent and inclusive alternative. This paper provides a novel end-to-end methodology to detect fake news on X via "wisdom of the synthetic & representative crowds". We deploy an online survey on the Lucid platform to gather veracity assessments for a number of pandemic-related tweets from crowd-workers. Borrowing from the MrP literature, we train a Hierarchical Bayesian model to predict the veracity of each tweet from the perspective of different personae from the population of interest. We then weight the predicted veracity assessments according to a representative stratification frame, such that decisions about ``fake'' tweets are representative of the overall polity of interest. Based on these aggregated scores, we analyse a corpus of tweets and perform a second MrP to generate state-level estimates of the number of people who share fake news. We find small but statistically meaningful heterogeneity in fake news sharing across US states. At the individual-level: i. sharing fake news is generally rare, with an average sharing probability interval [0.07,0.14]; ii. strong evidence that Democrats share less fake news, accounting for a reduction in the sharing odds of [57.3%,3.9%] relative to the average user; iii. when Republican definitions of fake news are used, it is the latter who show a decrease in the propensity to share fake news worth [50.8%, 2.0%]; iv. some evidence that women share less fake news than men, an effect worth a [29.5%,4.9%] decrease.
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