Exposure to opposing views on social media can increase political polarization.
Exposure to opposing views on social media can increase political polarization.
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DOI：
10.1073/pnas.1804840115
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发表时间：
2018-09-11
影响因子：
11.1
通讯作者：
Volfovsky A
中科院分区：
文献类型：
--
作者：
Bail CA;Argyle LP;Brown TW;Bumpus JP;Chen H;Hunzaker MBF;Lee J;Mann M;Merhout F;Volfovsky A
社交媒体网站经常被指责为加剧了政治两极分化，因为它创造了“回声室”，阻止人们接触与他们先前存在的信仰相矛盾的信息。我们进行了一项实地实验，为一大群民主党人和共和党人提供经济补偿，以跟踪那些转发民选官员和持相反政治观点的意见领袖的信息的机器人。共和党参与者在关注自由派Twitter机器人后表达了更为保守的观点，而民主党人在关注保守派Twitter机器人后的态度变得更加自由，尽管这种影响在统计学上并不显著。尽管存在一些限制，但这项研究对新兴的计算社会科学领域和正在进行的减少在线政治极化的努力具有重要意义。人们越来越担心，社交媒体网站通过创建“回声室”，使人们与对当前事件的相反观点隔绝，从而助长了政治两极分化。我们调查了大量民主党人和共和党人，他们每周至少访问Twitter三次，讨论一系列社会政策问题。一周后，我们将受访者随机分配到一种治疗条件下，在这种条件下，他们被提供经济激励，以跟踪一个Twitter机器人1个月，使他们接触到来自那些具有相反政治意识形态的人的信息（例如，民选官员、意见领袖、媒体组织和非营利组织）。在月底对受访者进行重新调查，以衡量这种治疗的效果，并在整个研究期间定期监测治疗依从性。我们发现，跟随自由派Twitter机器人的共和党人在治疗后变得更加保守。民主党人在追随保守的Twitter机器人后，自由主义态度略有增加，尽管这些影响在统计上并不显著。尽管我们的研究存在重要局限性，但这些发现对有关政治两极分化和新兴计算社会科学领域的跨学科文献具有重大影响。
Social media sites are often blamed for exacerbating political polarization by creating “echo chambers” that prevent people from being exposed to information that contradicts their preexisting beliefs. We conducted a field experiment that offered a large group of Democrats and Republicans financial compensation to follow bots that retweeted messages by elected officials and opinion leaders with opposing political views. Republican participants expressed substantially more conservative views after following a liberal Twitter bot, whereas Democrats’ attitudes became slightly more liberal after following a conservative Twitter bot—although this effect was not statistically significant. Despite several limitations, this study has important implications for the emerging field of computational social science and ongoing efforts to reduce political polarization online. There is mounting concern that social media sites contribute to political polarization by creating “echo chambers” that insulate people from opposing views about current events. We surveyed a large sample of Democrats and Republicans who visit Twitter at least three times each week about a range of social policy issues. One week later, we randomly assigned respondents to a treatment condition in which they were offered financial incentives to follow a Twitter bot for 1 month that exposed them to messages from those with opposing political ideologies (e.g., elected officials, opinion leaders, media organizations, and nonprofit groups). Respondents were resurveyed at the end of the month to measure the effect of this treatment, and at regular intervals throughout the study period to monitor treatment compliance. We find that Republicans who followed a liberal Twitter bot became substantially more conservative posttreatment. Democrats exhibited slight increases in liberal attitudes after following a conservative Twitter bot, although these effects are not statistically significant. Notwithstanding important limitations of our study, these findings have significant implications for the interdisciplinary literature on political polarization and the emerging field of computational social science.
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