魏建良, 朱庆华. 基于信息级联的网络意见传播及扭曲效应国外研究进展[J]. 情报学报, 2019, 38(10): 1117-1128.
Wei Jianliang, Zhu Qinghua. An Overseas Review of the Spread of Opinions on Social Networks and Information Distortion Based on Information Cascade. 情报学报, 2019, 38(10): 1117-1128.
1 SunsteinC R, VermeuleA. Conspiracy theories: Causes and cures[J]. Journal of Political Philosophy, 2009, 17(2): 202-227. 2 NickersonR S. Confirmation bias: A ubiquitous phenomenon in many guises[J]. Review of General Psychology, 1998, 2(2): 175-220. 3 GarrettR K. Echo Chambers online? Politically motivated selective exposure among Internet news users[J]. Journal of Computer-Mediated Communication, 2009, 14(2): 265-285. 4 SawyerR K. Social emergence: Societies as complex systems[M]. Cambridge: Cambridge University Press, 2005: 1-38. 5 EasleyD, NetworksKleinberg J., crowds, and markets: Reasoning about a highly connected world[M]. Cambridge: Cambridge University Press, 2010: 2-506. 6 HowellL. Digital wildfires in a hyper-connected world[R]. WEF Report,2013, 3: 15-94. 7 BikhchandaniS, HirshleiferD, WelchI. A theory of fads, fashion, custom, and cultural change as informational cascades[J]. Journal of Political Economy, 1992, 100(5): 992-1026. 8 AndersonL R, HoltC A. Information cascades in the laboratory[J]. The American Economic Review, 1997, 87(5): 847-862. 9 ZongB, WuY H, SinghA K, et al. Inferring the underlying structure of information cascades[C]// Proceedings of the 12th IEEE International Conference on Data Mining. New York: IEEE, 2012: 1218-1223. 10 LeskovecJ, SinghA, KleinbergJ. Patterns of influence in a recommendation network[C]// Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining. Heidelberg: Springer, 2006: 380-389. 11 LeskovecJ, McGlohonM, FaloutsosC, et al. Patterns of cascading behavior in large blog graphs[C]// Proceedings of the 7th SIAM International Conference on Data Mining, Minneapolis, Minnesota, USA, 2007: 551-556. 12 Ba?osR A, Borge-HolthoeferJ, MorenoY. The role of hidden influentials in the diffusion of online information cascades[J]. EPJ Data Science, 2013, 2: 6. 13 ZiegelmeyerA, KoesslerF, BrachtJ, et al. Fragility of information cascades: An experimental study using elicited beliefs[J]. Experimental Economics, 2010, 13(2): 121-145. 14 GalubaW, AbererK, ChakrabortyD, et al. Outtweeting the Twitterers - Predicting information cascades in Microblogs[C]// Proceedings of the 3rd Conference on Online Social Networks. Berkeley: USENIX Association, 2010: 3. 15 RattanaritnontG, ToyodaM, KitsuregawaM. A study on characteristics of topic specific information cascade in Twitter[C]// Proceedings of the IEEE International Conference on Data Engineering. IEEE, 2011: 65-70. 16 GuoR C, ShaabaniE, BhatnagarA, et al. Toward order-of-magnitude cascade prediction[C]// Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. New York: ACM Press, 2015: 1610-1613. 17 GoelS, WattsD J, GoldsteinD G. The structure of online diffusion networks[C]// Proceedings of the 13th ACM Conference on Electronic Commerce. New York: ACM Press, 2012: 623-638. 18 WangD K, WuY, ZhangY. Two models for inferring network structure from cascades[C]// Proceedings of the International Conference on Internet Computing, Las Vegas, Nevada, USA, 2011: 59-63. 19 SadikovE, MedinaM, LeskovecJ, et al. Correcting for missing data in information cascades[C]// Proceedings of the Fourth ACM International Conference on Web Search and Data Mining. New York: ACM Press, 2011: 55-64. 20 Luczak-RoeschM, TinatiR, van KleekM, et al. From coincidence to purposeful flow? Properties of transcendental information cascades[C]// Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. New York: ACM Press, 2015: 633-638. 21 SarkarS, GuoR C, ShakarianP. Understanding and forecasting lifecycle events in information cascades[J]. Social Network Analysis and Mining, 2017, 7: 55. 22 FushimiT, KawazoeT, SaitoK, et al. What does an information diffusion model tell about social network structure?[C]// Proceedings of the Pacific Rim Knowledge Acquisition Workshop. Heidelberg: Springer, 2009: 122-136. 23 PeiS, MuchnikL, Andrade JrJ S, et al. Searching for superspreaders of information in real-world social media[J]. Scientific Reports, 2015, 4: 5547. 24 WegrzyckiK, SankowskiP, PacukA, et al. Why do cascade sizes follow a power-law?[C]// Proceedings of the 26th International Conference on World Wide Web. New York: ACM Press, 2017: 569-576. 25 JaliliM, PercM. Information cascades in complex networks[J]. Journal of Complex Networks, 2017, 5(5): 665-693. 26 VaastE, SafadiH, LapointeL, et al. Social media affordances for connective action: An examination of microblogging use during the gulf of Mexico oil spill[J]. MIS Quarterly, 2017, 41(4): 1179-1205. 27 HackettA, MelnikS, GleesonJ P. Cascades on a class of clustered random networks[J]. Physical Review E, 2011, 83(5): 056107. 28 ChoobdarS, RibeiroP, ParthasarathyS, et al. Dynamic inference of social roles in information cascades[J]. Data Mining and Knowledge Discovery, 2015, 29(5): 1152-1177. 29 GoelS, AndersonA, HofmanJ, et al. The structural virality of online diffusion[J]. Management Science, 2015, 62(1): 180-196. 30 SreenivasanS, ChanK S, SwamiA, et al. Information cascades in feed-based networks of users with limited attention[J]. IEEE Transactions on Network Science and Engineering, 2017, 4(2): 120-128. 31 WattsD J. A simple model of global cascades on random networks[J]. Proceedings of the National Academy of Sciences of the United States of America, 2002, 99(9): 5766-5771. 32 GleesonJ P, CahalaneD J. Seed size strongly affects cascades on random networks[J]. Physical Review E, 2007, 75(5): 056103. 33 BarbieriN, BonchiF, MancoG. Cascade-based community detection[C]// Proceedings of the Sixth ACM International Conference on Web Search and Data Mining. New York: ACM Press, 2013: 33-42. 34 AkroufS, MeriemL, YahiaB, et al. Social network analysis and information propagation: A case study using Flickr and YouTube networks[J]. International Journal of Future Computer and Communication, 2013, 2(3): 246-252. 35 DasA, GollapudiS, KicimanE. Effect of persuasion on information diffusion in social networks[Z]. MSR-TR-2014-69, 2014: 1-8. 36 YuM, GuptaV, KolarM. An influence-receptivity model for topic based information cascades[C]// Proceedings of the IEEE International Conference on Data Mining. New York: IEEE, 2017: 18-21. 37 GómezV, KappenH J, KaltenbrunnerA. Modeling the structure and evolution of discussion cascades[C]// Proceedings of the 22nd ACM Conference on Hypertext and Hypermedia. New York: ACM Press, 2011: 181-190. 38 SaitoK, KimuraM, OharaK, et al. Learning asynchronous-time information diffusion models and its application to behavioral data analysis over social networks[J]. Journal of Computer Engineering and Informatics, 2013, 1(2): 30-57. 39 FrydmanC, KrajbichI. Using response times to infer others’ beliefs: An application to information cascades[R]. SSRN, 2017: 3-5. 40 CuiB. From Information cascade to knowledge transfer: Predictive analyses on social networks[D]. Rochester: Rochester Institute of Technology, 2016: 1-10. 41 XuS B, SmithD A. Contrastive training for models of information cascades[C]// Proceedings of Thirty-Second AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2018: 483-490. 42 MyersS A, ZhuC G, LeskovecJ. 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 Press, 2012: 33-41. 43 NguyenD T, ZhangH Y, DasS, et al. Least cost influence in multiplex social networks: Model representation and analysis[C]// Proceedings of the IEEE International Conference on Data Mining. New York: IEEE, 2013: 567-576. 44 LiuC, ZhanX X, ZhangZ K, et al. How events determine spreading patterns: Information transmission via internal and external influences on social networks[J]. New Journal of Physics, 2015, 17(11): 113045. 45 LiZ F, YanF H, JiangY C. Cross-layers cascade in multiplex networks[J]. Autonomous Agents and Multi-Agent Systems, 2015, 29(6): 1186-1215. 46 BorodinA, FilmusY, OrenJ. Threshold models for competitive influence in social networks[C]// Proceedings of the International Workshop on Internet and Network Economics. Heidelberg: Springer, 2010: 539-550. 47 BeutelA, PrakashB A, RosenfeldR, et al. Interacting viruses in networks: Can both survive?[C]// Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 2012: 426-434. 48 MyersS A, LeskovecJ. Clash of the contagions: Cooperation and competition in information diffusion[C]// Proceedings of the IEEE 12th International Conference on Data Mining. New York: IEEE, 2012: 539-548. 49 KrishnanS, CadenaJ, RamakrishnanN. The dynamics of competing cascades in social media: Applications to agenda setting[C]// Proceedings of the 7th ACM WSDM Conference. New York: ACM Press, 2014. 50 LimY, OzdaglarA, TeytelboymA. A simple model of cascades in networks[OL]. http://www.t8el.com/wp-content/uploads/2015/08/SimpleCascades.pdf. 51 LimY, OzdaglarA, TeytelboymA. Competitive rumor spread in social networks[J]. ACM SIGMETRICS Performance Evaluation Review, 2017, 44(3): 7-14. 52 FisherJ C D, WoodersJ. Interacting information cascades: On the movement of conventions between groups[J]. Economic Theory, 2017, 63(1): 211-231. 53 CooperM J, DimitrovO, RauP R. A rose.com by any other name[J]. The Journal of Finance, 2001, 56(6): 2371-2388. 54 TafflerR J, TuckettD. A psychoanalytic interpretation of dot.com stock valuations[R]. SSRN, 2005: 1-27. 55 MatsubaraY, SakuraiY, PrakashB A, et al. Rise and fall patterns of information diffusion: model and implications[C]// Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 2012: 6-14. 56 KobayashiT. Trend-driven information cascades on random networks[J]. Physical Review E, 2015, 92(6): 062823. 57 RotabiR, KamathK, KleinbergJ, et al. Cascades: A view from Audience[C]// Proceedings of the 26th International Conference on World Wide Web. New York: ACM Press, 2017: 587-596. 58 RosasF, HsiaoJ H, ChenK C. A technological perspective on information cascades via social learning[J]. IEEE Access, 2017, 5: 22605-22633. 59 KempeD, KleinbergJ, éTardos. Maximizing the spread of influence through a social network[C]// Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 2003: 137-146. 60 WattsD J, DoddsP S. Influentials, networks, and public opinion formation[J]. Journal of Consumer Research, 2007, 34(4): 441-458. 61 KitsakM, GallosL K, HavlinS, et al. Identification of influential spreaders in complex networks[J]. Nature Physics, 2010, 6(11): 888-893. 62 SunM, ZhangX M, ZhuF. Escaping the herd: Experimental evidence on the need to be different in social networks[OL]. https://research.chicagobooth.edu/~/media/48615372D8234E31ABD294-FDD2B2A0EE.pdf. 63 Borge-HolthoeferJ, MorenoY. Absence of influential spreaders in rumor dynamics[J]. Physical Review E, 2012, 85(2): 026116. 64 ZhaoJ, LuiJ C S, TowsleyD, et al. Whom to follow: Efficient followee selection for cascading outbreak detection on online social networks[J]. Computer Networks, 2014, 75: 544-559. 65 HallR T, WhiteJ S, FieldsJ. Social relevance: Toward understanding the impact of the individual in an information cascade[J]. Proceeding of SPIE, 2016, 9826: id 98260C. 66 de KerchoveC, KringsG, LambiotteR, et al. Role of second trials in cascades of information over networks[J]. Physical Review E, 2009, 79(1): 016114. 67 ver SteegG, GhoshR, LermanK. What stops social epidemics?[C]// Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media. Palo Alto: AAAI Press, 2011: 377-384. 68 EftekharM, GanjaliY, KoudasN. Information cascade at group scale[C]// Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 2013: 401-409. 69 KomatsuT, SatoH, NamatameA. Maximizing cascade of innovation on networks[J]. SICE Journal of Control, Measurement, and System Integration, 2013, 6(2): 66-75. 70 WangY B. Maximizing the speed of influence in social networks[D]. San Jose: San Jose State University, 2015: 2-9. 71 LiuH, IoannidisS, BhagatS, et al. Adding structure: Social network inference with graph priors[C]// Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York,: ACM Press, 2016. 72 Kapros-AnastasiadisG. Cascades on temporal networks[D]. Oxford: University of Oxford, 2012: 10-20. 73 HurdT R, GleesonJ P. On Watts’ cascade model with random link weights[J]. Journal of Complex Networks, 2013, 1(1): 25-43. 74 WengL L, RatkiewiczJ, PerraN, et al. The role of information diffusion in the evolution of social networks[C]// Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York,: ACM Press, 2013: 356-364. 75 MyersS A, LeskovecJ. The bursty dynamics of the twitter information network[C]// Proceedings of the 23rd International Conference on World Wide Web. New York: ACM Press, 2014: 913-924. 76 AntoniadesD, DovrolisC. Co-evolutionary dynamics in social networks: A case study of Twitter[J]. Computational Social Networks, 2015, 2(1): 14. 77 FarajtabarM, WangY, Gomez-RodriguezM, et al. Coevolve: A joint point process model for information diffusion and network co-evolution[C]// Proceedings of the International Conference on Neural Information Processing Systems. Cambridge: MIT Press, 2015: 1954-1962. 78 WangY, ShenH W, LiuS, et al. Learning user-specific latent influence and susceptibility from information cascades[C]// Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2015: 477-483. 79 WangS, YanZ, HuX, et al. Burst time prediction in cascades[C]// Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2015: 325-331. 80 ChengJ, AdamicL, DowP A, et al. Can cascades be predicted?[C]// Proceedings of the 23rd International Conference on World Wide Web. New York: ACM Press, 2014: 925-936. 81 ZhengH Y, WuJ. Fast information cascade prediction through spatiotemporal decompositions[C]// Proceedings of the 11th IEEE International Conference on Mobile Ad hoc and Sensor Systems. New York: IEEE, 2014: 154-162. 82 WuT, ChenL T, XianX P, et al. Full-scale cascade dynamics prediction with a local-first approach[OL]. https://arxiv.org/pdf/1512.08455.pdf. 83 SubbianK, PrakashB A, AdamicL. Detecting large reshare cascades in social networks[C]// Proceedings of the 26th International Conference on World Wide Web. New York: ACM Press, 2017: 597-605. 84 GuoR C, ShakarianP. A comparison of methods for cascade prediction[C]// Proceedings of the International Conference on Advances in Social Network Analysis and Mining. New York: IEEE, 2016: 591-598. 85 LagnierC, DenoyerL, GaussierE, et al. Predicting information diffusion in social networks using content and user’s profiles[C]// Proceedings of the European Conference on Information Retrieval. Heidelberg: Springer, 2013: 74-85. 86 CuiP, JinS F, YuL Y, et al. Cascading outbreak prediction in networks: A data-driven approach[C]// Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 2013: 901-909. 87 HeX, RekatsinasT, FouldsJ, et al. Hawkestopic: A joint model for network inference and topic modeling from text-based cascades[C]// Proceedings of the International Conference on Machine Learning, Lille, French, 2015: 871-880. 88 ZhaoQ Y, ErdogduM A, HeH Y, et al. Seismic: A self-exciting point process model for predicting tweet popularity[C]// Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 2015: 1513-1522. 89 KrishnanS, ButlerP, TandonR, et al. Seeing the forest for the trees: New approaches to forecasting cascades[C]// Proceedings of the 8th ACM Conference on Web Science. New York: ACM Press, 2016: 249-258. 90 LiC, MaJ Q, GuoX X, et al. DeepCas: An end-to-end predictor of information cascades[C]// Proceedings of the 26th International Conference on World Wide Web. New York: ACM Press, 2017: 577-586. 91 FriggeriA, AdamicL A, EcklesD, et al. Rumor cascades[C]// Proceedings of the International Conference on Weblogs and Social Media, Ann, Arbor, 2014. 92 FairchildR, AlsharmanM, HinvestN, et al. cascadesInformation, herding and emotional investors in an IPO[C]// Proceedings of the 11th International Conference on Computational and Financial Econometrics, London, UK, 2017.