论文标题
党派左右，党派预测（不对称）漏洞的漏洞
Right and left, partisanship predicts (asymmetric) vulnerability to misinformation
论文作者
论文摘要
我们通过在Twitter上研究新闻共享行为来分析党派，回声室和在线错误信息的脆弱性之间的关系。尽管我们的结果证实了先前的发现，即在线错误信息共享与右倾的党派关系密切相关，但我们也发现了左倾用户中类似但较弱的趋势。由于用户的党派关系与其在党派回声室中的位置之间的相关性，因此这些类型的影响会感到困惑。为了消除其效果，我们执行回归分析，发现误解的脆弱性受到左派和右倾用户的党派关系的影响最大。
We analyze the relationship between partisanship, echo chambers, and vulnerability to online misinformation by studying news sharing behavior on Twitter. While our results confirm prior findings that online misinformation sharing is strongly correlated with right-leaning partisanship, we also uncover a similar, though weaker trend among left-leaning users. Because of the correlation between a user's partisanship and their position within a partisan echo chamber, these types of influence are confounded. To disentangle their effects, we perform a regression analysis and find that vulnerability to misinformation is most strongly influenced by partisanship for both left- and right-leaning users.
