TY - JOUR AU - Aragón, P. AU - Gómez, V. AU - García, D. AU - Kaltenbrunner, A. PY - 2017 DA - 2017// TI - Generative models of online discussion threads: state of the art and research challenges JO - J Internet Serv Appl VL - 8 UR - https://doi.org/10.1186/s13174-017-0066-z DO - 10.1186/s13174-017-0066-z ID - Aragón2017 ER - TY - STD TI - Aragón P, Gómez V, Kaltenbrunner A (2017b) To thread or not to thread: the impact of conversation threading on online discussion. In: Proceedings of eleventh international AAAI conference on Web and social media, pp 12–21. https://www.aaai.org/ocs/index.php/ICWSM/ICWSM17/paper/viewPaper/15609 UR - https://www.aaai.org/ocs/index.php/ICWSM/ICWSM17/paper/viewPaper/15609 ID - ref2 ER - TY - STD TI - Backstrom L, Kleinberg J, Lee L, Danescu-Niculescu-Mizil C (2013) Characterizing and curating conversation threads. In: Proceedings of 6th ACM international conference on Web search and data mining (WSDM), pp 13–22. https://doi.org/10.1145/2433396.2433401. http://dl.acm.org/citation.cfm?doid=2433396.2433401 UR - http://dl.acm.org/citation.cfm?doid=2433396.2433401 ID - ref3 ER - TY - STD TI - Beck J, Huang R, Lindner D, Guo T, Ce Z, Helbing D, Antulov-Fantulin N (2019) Sensing social media signals for cryptocurrency news. In: Companion proceedings of the 2019 World Wide Web conference, pp 1051–1054 ID - ref4 ER - TY - STD TI - Boyd D, Golder S, Lotan G (2010) Tweet, tweet, retweet: conversational aspects of retweeting on Twitter. In: Proceedings of 43rd Hawaii international conference on system sciences, pp 1–10. https://doi.org/10.1109/HICSS.2010.412. http://ieeexplore.ieee.org/document/5428313/ UR - http://ieeexplore.ieee.org/document/5428313/ ID - ref5 ER - TY - STD TI - Cao Q, Shen H, Cen K, Ouyang W, Cheng X (2017) DeepHawkes: bridging the gap between prediction and understanding of information cascades. In: Proceedings of ACM international conference on information and knowledge management (CIKM) ID - ref6 ER - TY - STD TI - Cheng J, Adamic LA, Dow PA, Kleinberg J, Leskovec J (2014) Can cascades be predicted? In: Proceedings 23rd international conference on World Wide Web, pp 925–936. https://doi.org/10.3390/ijms17101719. arXiv:1403.4608. https://doi.org/10.1145/2566486.2567997 UR - http://arxiv.org/abs/1403.4608 ID - ref7 ER - TY - STD TI - Choi D, Han J, Chung T, Ahn Y-Y, Chun B-G, Kwon TT (2015) Characterizing conversation patterns in reddit. In: Proceedings of ACM conference on online social networks (COSN), pp 233–243. https://doi.org/10.1145/2817946.2817959 ID - ref8 ER - TY - STD TI - DARPA (2018) Computational simulation of online social behavior (SocialSim). https://www.darpa.mil/program/computational-simulation-of-online-social-behavior. Accessed 16 Jan 2021 UR - https://www.darpa.mil/program/computational-simulation-of-online-social-behavior ID - ref9 ER - TY - STD TI - Dow PA, Adamic L, Friggeri A (2013) The anatomy of large Facebook cascades. In: Proceedings of international AAAI conference on Web and social media. https://www.aaai.org/ocs/index.php/ICWSM/ICWSM13/paper/view/6123 UR - https://www.aaai.org/ocs/index.php/ICWSM/ICWSM13/paper/view/6123 ID - ref10 ER - TY - STD TI - Du N, Dai H, Trivedi R, Upadhyay U, Gomez-Rodriguez M, Song L (2016) Recurrent marked temporal point processes: Embedding event history to vector. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 1555–1564 ID - ref11 ER - TY - STD TI - Gao J, Shen H, Liu S, Cheng X (2016) Modeling and predicting retweeting dynamics via a mixture process. In: Proceedings 25th international conference companion on World Wide Web (WWW) ID - ref12 ER - TY - STD TI - Goel S, Watts DJ, Goldstein DG (2012) The structure of online diffusion networks. In: Proceedings of 13th ACM conference on electronic commerce (EC), pp 623–638. https://doi.org/10.1145/2229012.2229058. http://dl.acm.org/citation.cfm?doid=2229012.2229058 UR - http://dl.acm.org/citation.cfm?doid=2229012.2229058 ID - ref13 ER - TY - JOUR AU - Goel, S. AU - Anderson, A. AU - Hofman, J. AU - Watts, D. J. PY - 2016 DA - 2016// TI - The structural virality of online diffusion JO - Manag Sci VL - 62 UR - https://doi.org/10.1287/mnsc.2015.2158 DO - 10.1287/mnsc.2015.2158 ID - Goel2016 ER - TY - STD TI - Gómez V, Kaltenbrunner A, López V (2008) Statistical analysis of the social network and discussion threads in slashdot. In: Proceedings of 17th international conference on World Wide Web (WWW), p 645. https://doi.org/10.1145/1367497.1367585. http://portal.acm.org/citation.cfm?doid=1367497.1367585 UR - http://portal.acm.org/citation.cfm?doid=1367497.1367585 ID - ref15 ER - TY - JOUR AU - Gómez, V. AU - Kappen, H. J. AU - Litvak, N. AU - Kaltenbrunner, A. PY - 2013 DA - 2013// TI - A likelihood-based framework for the analysis of discussion threads JO - World Wide Web VL - 16 UR - https://doi.org/10.1007/s11280-012-0162-8 DO - 10.1007/s11280-012-0162-8 ID - Gómez2013 ER - TY - STD TI - Guo R, Shaabani E, Bhatnagar A, Shakarian P (2015) Toward order-of-magnitude cascade prediction. In: Proceedings of IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM) ID - ref17 ER - TY - JOUR AU - Hodas, N. O. AU - Lerman, K. PY - 2014 DA - 2014// TI - The simple rules of social contagion JO - Sci Rep VL - 4 UR - https://doi.org/10.1038/srep04343 DO - 10.1038/srep04343 ID - Hodas2014 ER - TY - JOUR AU - Hogg, T. AU - Lerman, K. PY - 2012 DA - 2012// TI - Social dynamics of digg JO - EPJ Data Sci VL - 1 UR - https://doi.org/10.1140/epjds5 DO - 10.1140/epjds5 ID - Hogg2012 ER - TY - STD TI - Hui P-M, Weng L, Sahami Shirazi A, Ahn Y-Y, Menczer F (2018) Scalable detection of viral memes from diffusion patterns. In: Lehmann S, Ahn Y-Y (eds) Complex spreading phenomena in social systems: influence and contagion in real-world social networks. Computational social sciences, pp 197–211. Springer, Cham. https://doi.org/10.1007/978-3-319-77332-2_11 ID - ref20 ER - TY - STD TI - Islam MR, Muthiah S, Adhikari B, Prakash BA, Ramakrishnan N (2018) Deepdiffuse: predicting the ‘who’ and ‘when’ in cascades. In: Proceedings of IEEE international conference on data mining (ICDM), pp 1055–1060 ID - ref21 ER - TY - STD TI - Joulin A, Grave E, Bojanowski P, Mikolov T (2017) Bag of tricks for efficient text classification. In: Proceedings of the 15th conference of the European chapter of the association for computational linguistics, vol 2, pp 427–431 ID - ref22 ER - TY - STD TI - Kefato ZT, Sheikh N, Bahri L, Soliman A, Montresor A, Girdzijauskas S (2018) CAS2VEC: network-agnostic cascade prediction in online social networks. In: Proceedings of 5th international conference on social networks analysis, management and security (SNAMS), pp 72–79. https://doi.org/10.1109/SNAMS.2018.8554730. https://ieeexplore.ieee.org/document/8554730/ UR - https://ieeexplore.ieee.org/document/8554730/ ID - ref23 ER - TY - BOOK AU - Klein, J. AU - Moeschberger, M. PY - 2006 DA - 2006// TI - Survival analysis: techniques for censored and truncated data PB - Springer CY - Berlin ID - Klein2006 ER - TY - STD TI - Kobayashi R, Lambiotte R (2016) Tideh: time-dependent Hawkes process for predicting retweet dynamics. In: Proceedings of tenth international AAAI conference on Web and social media (ICWSM) ID - ref25 ER - TY - JOUR AU - Koenker, R. AU - Bassett, G. PY - 1978 DA - 1978// TI - Regression quantiles JO - Econometrica VL - 46 UR - https://doi.org/10.2307/1913643 DO - 10.2307/1913643 ID - Koenker1978 ER - TY - STD TI - Krohn R, Weninger T (2019) Modelling online comment threads from their start. In: Proceedings of 2019 IEEE international conference on Big Data (Big Data), pp 820–829 ID - ref27 ER - TY - STD TI - Kumar R, Mahdian M, McGlohon M (2010) Dynamics of conversations. In: Proceedings of 16th ACM SIGKDD international conference on knowledge discovery and data mining (KDD), p 553. https://doi.org/10.1145/1835804.1835875 ID - ref28 ER - TY - STD TI - Leskovec J, Backstrom L, Kleinberg J (2009) Meme-tracking and the dynamics of the news cycle. In: Proceedings of 15th ACM SIGKDD international conference on knowledge discovery and data mining, pp 497–506 ID - ref29 ER - TY - STD TI - Li C, Ma J, Guo X, Mei Q (2017) DeepCas: an end-to-end predictor of information cascades. In: Proceedings of the 26th international conference on World Wide Web (WWW), pp 577–586. https://doi.org/10.1145/3038912.3052643 ID - ref30 ER - TY - STD TI - Louppe G, Wehenkel L, Sutera A, Geurts P (2013) Understanding variable importances in forests of randomized trees. In: NIPS’13: proceedings of the 26th international conference on neural information processing systems ID - ref31 ER - TY - STD TI - Lumbreras A (2016) Automatic role detection in online forums. PhD thesis, Université de Lyon. https://tel.archives-ouvertes.fr/tel-01439342/ UR - https://tel.archives-ouvertes.fr/tel-01439342/ ID - ref32 ER - TY - JOUR AU - Lumbreras, A. AU - Jouve, B. AU - Velcin, J. AU - Guégan, M. PY - 2017 DA - 2017// TI - Role detection in online forums based on growth models for trees JO - Soc Netw Anal Min VL - 7 UR - https://doi.org/10.1007/s13278-017-0472-z DO - 10.1007/s13278-017-0472-z ID - Lumbreras2017 ER - TY - JOUR AU - Medvedev, A. N. AU - Delvenne, J. -. C. AU - Lambiotte, R. PY - 2019 DA - 2019// TI - Modelling structure and predicting dynamics of discussion threads in online boards JO - J Complex Netw VL - 7 UR - https://doi.org/10.1093/comnet/cny010 DO - 10.1093/comnet/cny010 ID - Medvedev2019 ER - TY - STD TI - Mishra S, Rizoiu M-A, Xie L (2016) Feature driven and point process approaches for popularity prediction. In: Proceedings ACM international conference on information and knowledge management (CIKM) ID - ref35 ER - TY - JOUR AU - Nishi, R. AU - Takaguchi, T. AU - Oka, K. AU - Maehara, T. AU - Toyoda, M. AU - ichi Kawarabayashi, K. AU - Masuda, N. PY - 2016 DA - 2016// TI - Reply trees in Twitter: data analysis and branching process models JO - Soc Netw Anal Min VL - 6 UR - https://doi.org/10.1007/s13278-016-0334-0 DO - 10.1007/s13278-016-0334-0 ID - Nishi2016 ER - TY - STD TI - Pacheco D (2019) twitter_cascades. https://github.com/diogofpacheco/twitter_cascades. Accessed 16 Jan 2021 UR - https://github.com/diogofpacheco/twitter_cascades ID - ref37 ER - TY - JOUR AU - Pasquetto, I. V. AU - Swire-Thompson, B. PY - 2020 DA - 2020// TI - Tackling misinformation: what researchers could do with social media data JO - HKS Misinf Rev UR - https://doi.org/10.37016/mr-2020-49 DO - 10.37016/mr-2020-49 ID - Pasquetto2020 ER - TY - JOUR AU - Pei, S. AU - Muchnik, L. AU - Andrade, J. AU - Zheng, Z. AU - Makse, H. PY - 2014 DA - 2014// TI - Searching for superspreaders of information in real-world social media JO - Sci Rep VL - 4 UR - https://doi.org/10.1038/srep05547 DO - 10.1038/srep05547 ID - Pei2014 ER - TY - STD TI - Pinto H, Almeida J, Gonçalves M (2013) Using early view patterns to predict the popularity of youtube videos. In: Proceedings of ACM international conference on web search and data mining (WSDM) ID - ref40 ER - TY - STD TI - Rizoiu M-A, Lee Y, Mishra S, Xie L (2017) A tutorial on Hawkes processes for events in social media. arXiv:1708.06401, arXiv UR - http://arxiv.org/abs/1708.06401 ID - ref41 ER - TY - STD TI - Rossi L, Magnani M (2012) Conversation practices and network structure in Twitter. In: Proceedings of international AAAI conference on Web and social media (ICWSM). https://www.aaai.org/ocs/index.php/ICWSM/ICWSM12/paper/view/4634 UR - https://www.aaai.org/ocs/index.php/ICWSM/ICWSM12/paper/view/4634 ID - ref42 ER - TY - JOUR AU - Salganik, M. J. AU - Dodds, P. S. AU - Watts, D. J. PY - 2006 DA - 2006// TI - Experimental study of inequality and unpredictability in an artificial cultural market JO - Science VL - 311 UR - https://doi.org/10.1126/science.1121066 DO - 10.1126/science.1121066 ID - Salganik2006 ER - TY - STD TI - Shen H-W, Wang D, Song C, Barabási A-L (2014) Modeling and predicting popularity dynamics via reinforced poisson processes. In: Proceedings of 28th AAAI conference on artificial intelligence ID - ref44 ER - TY - STD TI - Subbian K, Prakash BA, Adamic L (2017) Detecting large reshare cascades in social networks. In: Proceedings of 26th international conference on World Wide Web, pp 597–605. https://doi.org/10.1145/3038912.3052718 ID - ref45 ER - TY - STD TI - Wang C, Ye M, Huberman BA (2012) From user comments to on-line conversations. In: Proceedings of 18th ACM SIGKDD international conference on knowledge discovery and data mining (KDD), pp 244–252. https://doi.org/10.2139/ssrn.2012183. http://dl.acm.org/citation.cfm?doid=2339530.2339573 UR - http://dl.acm.org/citation.cfm?doid=2339530.2339573 ID - ref46 ER - TY - STD TI - Wang J, Zheng VW, Liu Z, Chang KC-C (2017) Topological recurrent neural network for diffusion prediction. In: IEEE international conference on data mining (ICDM), pp 475–484 ID - ref47 ER - TY - JOUR AU - Watson, H. W. AU - Galton, F. PY - 1875 DA - 1875// TI - On the probability of the extinction of families JO - J Anthropol Inst G B Irel VL - 4 UR - https://doi.org/10.2307/2841222 DO - 10.2307/2841222 ID - Watson1875 ER - TY - JOUR AU - Weng, L. AU - Menczer, F. AU - Ahn, Y. -. Y. PY - 2013 DA - 2013// TI - Virality prediction and community structure in social networks JO - Sci Rep VL - 3 UR - https://doi.org/10.1038/srep02522 DO - 10.1038/srep02522 ID - Weng2013 ER - TY - STD TI - Weng L, Menczer F, Ahn Y-Y (2014) Predicting successful memes using network and community structure. In: Proceedings of eighth international AAAI conference on weblogs and social media (ICWSM). http://www.aaai.org/ocs/index.php/ICWSM/ICWSM14/paper/view/8081 UR - http://www.aaai.org/ocs/index.php/ICWSM/ICWSM14/paper/view/8081 ID - ref50 ER - TY - JOUR AU - Weninger, T. PY - 2014 DA - 2014// TI - An exploration of submissions and discussions in social news: mining collective intelligence of Reddit JO - Soc Netw Anal Min VL - 4 UR - https://doi.org/10.1007/s13278-014-0173-9 DO - 10.1007/s13278-014-0173-9 ID - Weninger2014 ER - TY - STD TI - Zhao Q, Erdogdu MA, He HY, Rajaraman A, Leskovec J (2015) SEISMIC: a self-exciting point process model for predicting tweet popularity. In: Proceedings of 21th ACM SIGKDD international conference on knowledge discovery and data mining, pp1513–1522. https://doi.org/10.1145/2783258.2783401. arXiv:1506.02594 UR - http://arxiv.org/abs/1506.02594 ID - ref52 ER -