Abstract:To reveal the characteristics and influencing factors of cross-platform information diffusion in social media, this paper took the Legitimate Defense Case in Kunshan as an example and studied the characteristics and related factors of information diffusion from other platforms to Sina Weibo using statistical inference and regression analysis methods. We found that users are more inclined to the latter when balancing the high amount of information in microblogs with the convenience of obtaining information, and the transmissibility, basic reproductive number and diffusion depth of cross-platform information are significantly lower than those of non-cross-platform information. Information from WeChat official accounts, Weibo videos, Weibo articles and Sina news has more advantages over other types of information in terms of depth and scale of diffusion. Compared with ordinary users, users who are authenticated as media and government administration spread information from news platforms on a larger scale. A comprehensive consideration of the types and source platforms of information can help us to better understand the spreading of sudden social events in the Internet space, thus helping to effectively guide or control the evolution of public opinion.
王玉, 许楠楠, 胡海波. 社交媒体中的跨平台信息扩散特征及机制[J]. 复杂系统与复杂性科学, 2022, 19(4): 7-16.
WANG Yu, XU Nannan, HU Haibo. Characteristics and Mechanisms of Cross-platform Information Diffusion in Social Media. Complex Systems and Complexity Science, 2022, 19(4): 7-16.
[1] 李栋, 徐志明, 李生, 等. 在线社会网络中信息扩散[J]. 计算机学报, 2014, 37(1): 189206. LI D, XU Z M, LI S, et al. A survey on information diffusion in online social networks[J]. Chinese Journal of Computers, 2014, 37(1): 189206. [2] 许小可, 胡海波, 张伦, 等. 社交网络上的计算传播学[M]. 北京: 高等教育出版社, 2015. [3] 刘红丽, 黄雅丽, 罗春海, 等. 基于用户行为的微博网络信息扩散模型[J]. 物理学报, 2016, 65(15): 158901. LIU H L, HUANG Y L, LUO C H, et al. Modeling information diffusion on microblog networks based on users' behaviors[J]. Acta Physica Sinica, 2016, 65(15): 158901. [4] ZHANG Z K, LIU C, ZHAN X X, et al. Dynamics of information diffusion and its applications on complex networks[J]. Physics Reports, 2016, 651: 134. [5] 李沧海, 许益贴, 罗春海, 等. 微博信息扩散的空间分析[J]. 复杂系统与复杂性科学, 2017, 14(3): 7584. LI C H, XU Y T, LUO C H, et al. Spatial analysis of microblog information diffusion[J]. Complex Systems and Complexity Science, 2017, 14(3): 7584. [6] 张凌, 罗曼曼, 朱礼军. 基于社交网络的信息扩散分析研究[J]. 数据分析与知识发现, 2018, 2(2): 4657. ZHANG L, LUO M M, ZHU L J. Analyzing information dissemination on social networks[J]. Data Analysis and Knowledge Discovery, 2018, 2(2): 4657. [7] WANG X C, LAN Y H, XIAO J H. Anomalous structure and dynamics in news diffusion among heterogeneous individuals[J]. Nature Human Bhaviour, 2019, 3(7): 709718. [8] ZHOU B, PEI S, MUCHNIK L, et al. Realistic modelling of information spread using peer-to-peer diffusion patterns[J]. Nature Human Behaviour, 2020, 4(11): 110. [9] XIE J R, MENG F H, SUN J C, et al. Detecting and modelling real percolation and phase transitions of information on social media[J]. Nature Human Behaviour, 2021, 5: 11611168. [10] SEGAULT A. Crossplatform references on social media: methodological challenges and research avenues[C]//Proceedings of the 2nd International Conference on Web Studies. New York: ACM, 2018: 5255. [11] 饶元, 吴连伟, 张君毅. 跨媒介舆情网络环境下信息传播机制研究与进展[J]. 中国科学: 信息科学, 2017, 47(12): 16231645. RAO Y, WU L W, ZHANG J Y. A survey of information propaganda mechanism under the cross-medium[J]. Scientia Sinica Informationis, 2017, 47(12): 16231645. [12] WEBSTER J G, KSIAZEK T B. The dynamics of audience fragmentation: public attention in an age of digital media[J]. Journal of Communication, 2012, 62(1): 3956. [13] NEUBAUM G, KRAÄMER N C. Opinion climates in social media: blending mass and interpersonal communication[J]. Human Communication Research, 2017, 43(4): 464476. [14] GUGGENHEIM L, JANG S M, BAE S Y, et al. The dynamics of issue frame competition in traditional and social media[J]. The ANNALS of the American Academy of Political and Social Science, 2015, 659(1): 207224. [15] SHUAI X, LIU X, XIA T, et al. Comparing the pulses of categorical hot events in Twitter and Weibo[C]//Proceedings of the 25th ACM Conference on Hypertext and Social Media. New York: ACM, 2014: 126135. [16] CHA M, PEÉREZ J A N, HADDADI H. Flash floods and ripples: the spread of media content through the blogosphere[C]//Proceedings of the 3rd International AAAI Conference on Weblogs and Social Media. Palo Alto, California: AAAI, 2009. [17] HEIMBACH I, SCHILLER B, STRUFE T, et al. Content virality on online social networks: empirical evidence from Twitter, Facebook, and Google+ on German news websites[C]//Proceedings of the 26th ACM Conference on Hypertext & Social Media. New York: ACM, 2015: 3947. [18] MYERS S A, ZHU C, LESKOVEC J. Information diffusion and external influence in networks[C]//Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2012: 3341. [19] LESKOVEC J, BACKSTROM L, KLEINBERG J. Meme-tracking and the dynamics of the news cycle[C]//Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2009: 497506. [20] RODRIGUEZ M G, LESKOVEC J, KRAUSE A. Inferring networks of diffusion and influence[C]//Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2010: 10191028. [21] RODRIGUEZ M G, LESKOVEC J, BALDUZZI D, et al. Uncovering the structure and temporal dynamics of information propagation[J]. Network Science, 2014, 2(1): 2665. [22] KIM M, NEWTH D, CHRISTEN P. Modeling dynamics of diffusion across heterogeneous social networks: news diffusion in social media[J]. Entropy, 2013, 15(10): 42154242. [23] KIM M, NEWTH D, CHRISTEN P. Macro-level information transfer in social media: reflections of crowd phenomena[J]. Neurocomputing, 2016, 172: 8499. [24] JULIA C, NICOLAS H, MARIE-LUCE V. The production of information in an online world[J]. The Review of Economic Studies, 2020, 87: 21262164. [25] JOHNSON N F, LEAHY R, RESTREPO N J, et al. Hidden resilience and adaptive dynamics of the global online hate ecology[J]. Nature, 2019, 573(7773): 261265. [26] VELAÁSQUEZ N, LEAHY R, RESTREPO N J, et al. Hate multiverse spreads malicious COVID-19 content online beyond individual platform control[EB/OL]. [20200810]. https://arxiv.org/abs/2004.00673. [27] IRIBARREN J L, MORO E. Affinity paths and information diffusion in social networks[J]. Social Networks, 2011, 33: 134142. [28] GOEL S, ANDERSON A, HOFMAN J, et al. The structural virality of online diffusion[J]. Management Science, 2016, 62(1): 180196. [29] MARTIN T, HOFMAN J M, SHARMA A, et al. Exploring limits to prediction in complex social systems[C]//Proceedings of the 25th International Conference on World Wide Web. New York: ACM, 2016: 683694.