The issue of content closure on information platforms has received increasing attention in academia. However,the debates on whether content closure exists,the formation causes,and its operation mechanisms remain inconclusive. Based on the Toutiao platform,this paper addresses the question of whether and how content closure occurs on information platforms by introducing the concept of content closure conduction,and focusing on the effect and mechanism of this phenomenon.
Through big data fetching and processing,more than 18 million data points from the Toutiao platform were clustered and content Gini coefficient was calculated. All content was divided into 46 tiers according to its heat level,in order to analyze the conduction efficiency between different levels of heat in a more fine-grained manner. This paper empirically examines the core hypotheses moving from effect test to mechanism analysis. In the effect test,the granger causality analysis method was applied to determine the conduction between each heat level. Besides,in the mechanism analysis,the binary logistic regression analysis was employed to verify the influence of the heat gap between the tiers on the effect of conduction effect. Subsequently,the QAP regression analysis method was used to identify 25 content categories that might be related to the conduction effect of content closure.
The results indicate that the phenomenon of content closure conduction exists between contents with different heat levels,and the more similar the heat levels,the more likely they are to produce closure conduction between contents. In other words,the content closure conduction follows the“heat proximity”law. In terms of content features,only a small number of content categories,which encompass sharing life and entertainment themes,are likely to have an impact on the dissemination effect.
This paper reveals the formation and evolution of content closure on the Toutiao platform through the perspective of the conduction effect. Besides,the results confirm the influence of the content heat gap and topic features on the closure conduction effect,enhancing the understanding of mechanisms,characteristics and mechanism of content closure on the information platforms,and offering practical references for addressing issues,such as content homogenization,information narrowing,and public opinion regulation in cyberspace.
MARTINJ D, HASSANF, ANGHELCEVG, et al. From echo chambers to “idea chambers”: concurrent online interactions with similar and dissimilar others[J].International Communication Gazette,2022, 84(3):252-275.
POWERSM, BENSONR. Is the internet homogenizing or diversifying the news? External pluralism in the US, Danish, and French press[J]. The International Journal of Press/Politics,2014, 19(2):246-265.
[6]
HELLMANH. Diversity-an end in itself?Developing a multi-measure methodology of television programme variety studies[J]. European Journal of Communication, 2001, 16(2):181-208.
[7]
WANGN, GUOZ, SHENF. Message, perception, and the Beijing Olympics: impact of differential media exposure on perceived opinion diversity[J]. Communication Research, 2011, 38(3):422-445.
HUANGH P. Frame-rich, frame-poor: an investigation of the contingent effects of media frame diversity and individual differences on audience frame diversity[J]. International Journal of Public Opinion Research, 2010, 22(1): 47-73.
[13]
GUOL, VARGOC.Fake news and emerging online media ecosystem: an integrated intermedia agenda-setting analysis of the 2016 U.S. presidential election[J]. Communication Research, 2020, 47(2): 178-200.
[14]
SINGERP, FLÖCKF, MEINHARTC, et al. Evolution of reddit: from the front page of the Internet to a self-referential community? [J]. Association for Computing Machinery, 2014, 7: 517-522.
[15]
WEBSTERJ G, KSIAZEKT B. The dynamics of audience fragmentation: public attention in an age of digital media[J]. Journal of Communication, 2012, 62(1): 39-56.
[16]
WEBSTERJ G. The duality of media: a structurational theory of public attention[J]. Communication Theory, 2011, 21(1): 43-66.
JENNINGSW, BEVANS, JOHNP. The agenda of British government: the speech from the throne, 1911—2008[J]. Political Studies, 2011, 59(1): 74-98.
[20]
KÜMPELA S. The Matthew effect in social media news use: assessing inequalities in news exposure and news engagement on social network sites (SNS)[J]. Journalism, 2020, 21(8): 1083-1098.
ASATANIK, YAMANOH, SAKAKIT, et al. Dense and influential core promotion of daily viral information spread in political echo chambers[J]. Scientific Reports, 2021, 11(1): 1-10.
[23]
BUCHERT. Want to be on the top? Algorithmic power and the threat of invisibility on Facebook[J]. New Media &Society, 2012, 14(7): 1164-1180.
[24]
VĪKE-FREIBERGAV, DÄUBLER-GMELINH, HAMMERSLEYB, et al. A free and pluralistic media to sustain European democracy[C]. High Level Group on Media Freedom and Media Pluralism, 2013.
[25]
NECHUSHTAIE, LEWISS C. What kind of news gatekeepers do we want machines to be? Filter bubbles, fragmentation, and the normative dimensions of algorithmic recommendations[J]. Computers in Human Behavior, 2019, 90: 298-307.
KLEPPERM D, SLEEBOSE, BUNTG,et al. Similarity in friendship networks: selection or influence? The effect of constraining contexts and non-visible attributes[J]. Social Networks, 2010, 32(1): 82-90.
SUY. Networked agenda flow between elite US newspapers and Twitter: a case study of the 2020 Black Lives Matter movement[J]. Journalism, 2022: 14648849221092521.
[33]
ENGELMANNI, SIMONM L, SABRINAH K.Effects of news factors on users’ news attention and selective exposure on a news aggregator website[J]. Journalism Studies, 2021, 22(6): 780-798.
TATARA, AMORIMM D, FDIDAS, et al. A survey on predicting the popularity of web content[J].Journal of Internet Services and Applications, 2014, 5(8): 1-20.
PASCALJ, ANDREASJ, HARALDS. Small worlds with a difference: new gatekeepers and the filtering of political information on Twitter[C]. Proceedings of the 3rd International Web Science Conference, 2011.