盘点亚洲社会政治两极分化:政治传播、社交媒体和数字治理

IF 1.5 2区 文学 Q2 COMMUNICATION
T. Lin, Chia-hung Tsai
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引用次数: 5

摘要

在2016年美国总统大选期间,社交媒体算法和回音室效应对社会政治两极分化甚至投票结果可能产生的影响引起了学术界的广泛关注。这种社会政治两极化至少在一定程度上也存在于具有不同政治和文化制度的亚洲国家。研究发现,当人们习惯了多样化的网络话语环境时,接触异质视角可以增强个人的政治参与(Kim, Hsu, & de Zúñiga, 2013)。相比之下,那些处于信息茧中的人被相似的观点或志同道合的价值观所包围,他们倾向于过滤掉不同的观点,坚定自己特定的世界观,并且容易走向极端(Sunstein, 2009)。关于互联网和社交媒体的两种对立观点描述了两极分化的不同属性。一方面,互联网的开放性使个人能够接受不同的想法,这可能会减少两极分化的影响(Garrett, Carnahan, & Lynch, 2013;Hong & Kim, 2016)。另一方面,越来越多的经验证据表明,在选择性地接触分裂的意识形态和社交网站上的信息时,两极分化可能会加剧。社交媒体被认为是意识形态两极分化的一个因素,因为它的算法会导致过滤泡沫,过滤掉不同的观点,同时增强相似的政治信仰和观点(Hong & Kim, 2016)。通过算法个性化,志同道合的内容被提升或过滤,这放大了回声室的影响(Dubois & Blank, 2018)。使用数字跟踪数据的研究支持了社交媒体上回声室的证据(Terren & Borge, 2021)。由于接触不同观点的机会有限,社交媒体重度用户可能缺乏网络异质性,这加剧了极化,因为接触不同观点的机会较少(Lee, Choi, Kim, & Kim, 2014)。互联网和社交媒体算法因加剧社会政治两极分化而受到批评,因为这些黑箱机器机制预先决定了用户对信息的接触。那些接触到同质化政治观点的人认为他们的观点是大多数人所持有的,这加深了与他们不同的政党或社会群体之间的鸿沟,从而导致政治动荡和社会混乱。Yarchi, Baden和Kligler-Vilenchik(2020)的计算传播研究证实了社交媒体对政治两极分化的影响。Yarchi et al.(2020)将政治极化分为互动极化、位置极化和情感极化。政治两极分化是由个人政治态度或党派倾向形成的(Coffey & Joseph, 2012)。媒体框架作为议程设置的第二层,选择和强调事实和解释,以塑造公众的感知,并讲述符合其媒体叙事的故事(Morstatter, Wu, Yavanoglu, Corman, & Liu, 2018)。对冲突的党派框架性在大众媒体中表现出来。对政治新闻的关注会影响观众的行为,加剧两极分化,而两极分化受个人党派倾向和意识形态倾向的强烈影响(Coffey & Joseph, 2012)。通过算法定制,涉及多利益相关者议程设置和框架力量复杂性的社交媒体使用被视为有效的工具
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Taking stock of social-political polarization in Asia: political communication, social media and digital governance
During the 2016 American presidential election, the possible impact of social media algorithm and echo chamber effects on socio-political polarization and perhaps even voting results, attracted much scholarly attention. Such socio-political polarization was present, at least to a degree, also in Asian countries with diverse political and cultural systems. Exposure to heterogeneous perspectives has been found to enhance individuals’ political participation when they get used to diverse online discourse environments (Kim, Hsu, & de Zúñiga, 2013). In contrast, those in information cocoons surrounded by similar viewpoints or like-minded values, tended to filter out dissenting views, firm in their specific world views, and tended to go to extremes easily (Sunstein, 2009). Two opposing arguments about the Internet and social media depict different attributes of polarization. On one hand, the openness of the Internet allows individuals to receive different ideas, which likely decreases the effects of polarization (Garrett, Carnahan, & Lynch, 2013; Hong & Kim, 2016). On the other hand, as increasing empirical evidence shows, polarization can be exacerbated under selective exposure to divisive ideologies and information on social network sites. Social media is considered a contributor to ideology polarization as its algorithm leads to filter bubbles that screen out differing views while enhancing similar political beliefs and perspectives (Hong & Kim, 2016). Through algorithmic personalization, liked-minded contents are promoted or filtered, which magnifies the effects of the echo chamber (Dubois & Blank, 2018). Evidence of echo chambers on social media is supported by studies using digital trace data (Terren & Borge, 2021). Social media heavy users are likely to have scanty network heterogeneity as a result of limited exposure to different perspectives, which reinforces polarization as a result of less exposure to diverse viewpoints (Lee, Choi, Kim, & Kim, 2014). Internet and social media algorithms have been criticized for worsening social-political polarization when these black-box machinery mechanisms predetermine users’ exposure to information. Those exposed to homogeneous political views believe their views are held by the majority, which deepens the gulf between political parties or social groups different from them and thus cause political turmoil and societal chaos. Yarchi, Baden, and Kligler-Vilenchik’s (2020) computational communication research confirms social media effects on political polarization. According to Yarchi et al. (2020), political polarization are categorized into interactional polarization, positional polarization and affective polarization. Political polarization is shaped by individual political attitudes or partisan dispositions (Coffey & Joseph, 2012). Media framing, as the second-level of agenda setting, selects and emphasizes facts and interpretations to shape public perceptions and tell stories to conform to their media narratives (Morstatter, Wu, Yavanoglu, Corman, & Liu, 2018). The partisan framing of conflicts is shown in mass media. Attention to political news influences viewer behaviors and aggravates polarization, which is strongly affected by individual partisan and ideological dispositions (Coffey & Joseph, 2012). Through algorithmic customization, social media use that involves complexity of multi-stakeholders’ agenda-setting and framing forces is regarded as effective tools to
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来源期刊
CiteScore
3.70
自引率
0.00%
发文量
38
期刊介绍: Launched in 1990, Asian Journal of Communication (AJC) is a refereed international publication that provides a venue for high-quality communication scholarship with an Asian focus and perspectives from the region. We aim to highlight research on the systems and processes of communication in the Asia-Pacific region and among Asian communities around the world to a wide international audience. It publishes articles that report empirical studies, develop communication theory, and enhance research methodology. AJC is accepted by and listed in the Social Science Citation Index (SSCI) published by Clarivate Analytics. The journal is housed editorially at the Wee Kim Wee School of Communication and Information at Nanyang Technological University in Singapore, jointly with the Asian Media Information and Communication Centre (AMIC).
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