Abstract

While fact-checking has received much attention as a tool to fight misinformation online, fact-checking efforts have yielded limited success in combating political misinformation due to partisans’ biased information processing. The efficacy of fact-checking often decreases, if not backfires, when the fact-checking messages contradict individual audiences’ political stance. To explore ways to minimize such politically biased processing of fact-checking messages, an online experiment (N = 645) examined how different source labels of fact-checking messages (human experts vs. AI vs. crowdsourcing vs. human experts-AI hybrid) influence partisans’ processing of fact-checking messages. Results showed that AI and crowdsourcing source labels significantly reduced motivated reasoning in evaluating the credibility of fact-checking messages whereas the partisan bias remained evident for the human experts and human experts-AI hybrid source labels.

摘要

尽管事实核查作为打击网上虚假信息的工具而受到广泛关注，但由于党派的信息处理存在偏见，事实核查工作在打击政治虚假信息方面取得的成果有限。当事实核查信息与个别受众的政治立场相矛盾时，事实核查的效率即使不会适得其反，也往往会降低。为了探索如何最大限度地减少事实核查消息的这种政治偏见处理，进行了一项在线实验（N = 645）研究了事实核查消息的不同来源标签（人类专家、人工智能、众包、人类专家-人工智能混合体）如何影响党派对事实核查消息的处理。结果表明，人工智能和众包源标签显着降低了评估事实核查消息可信度时的动机推理，而人类专家和人类专家-人工智能混合源标签的党派偏见仍然很明显。