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Heat diffusion approach for scientific impact analysis in social media

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Abstract

The advent of social media has enabled us to explore the impact of academic entities beyond the conventional bibliometric community. The traditional bibliometric indicators such as citation count, h-index and SNIP metrics aim to represent the propagation of knowledge in the academic world rather than the impact of the research on the wider world. In this study, we are interested in measuring the impact of the academic world on social media. We first create the 2-layered graph where the first layer is the bipartite graph between academic and social media entities and a second layer is the graph between social media entities. Then, we employed the heat diffusion-based metrics to measure the influence of academic entities in social media. Thus, we evaluate the academic entities by (1) predicting links between academic entities and social media and (2) suggesting memes for the academic entities. Our analysis on predicting links between scientist and social media entities showed the AUC-ROC score of 0.73 and the AUC-PR score of 0.30. Similarly, predicting links between scientific publications and social media entities showed the AUC-ROC score of 0.80 and the AUC-PR score of 0.19. Our approach can also suggest textual memes for scientific publications.

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Notes

  1. https://www.netflix.com.

  2. https://www.programmableweb.com/api/spinn3r.

  3. https://gate.ac.uk/.

  4. https://dev.elsevier.com/sc_apis.html.

  5. http://www.apastyle.org/learn/faqs/what-is-doi.aspx.

  6. http://answers.library.curtin.edu.au/faq/121100.

  7. http://snap.stanford.edu/data/memetracker9.html.

  8. http://www.memetracker.org/.

  9. https://news.mongabay.com/2018/01/global-warming-pollution-supersize-the-oceans-oxygen-depleted-dead-zones/.

References

  • Alperin JP, Gomez CJ, Haustein S (2018) Identifying diffusion patterns of research articles on twitter: a case study of online engagement with open access articles. Public Underst Sci. https://doi.org/10.1177/0963662518761733

    Article  Google Scholar 

  • Barnes C (2015) The use of altmetrics as a tool for measuring research impact. Aust Acad Res Libr 46(2):121–134

    Article  Google Scholar 

  • Cai D, He X, Han J, Huang TS (2011) Graph regularized nonnegative matrix factorization for data representation. IEEE Trans Pattern Anal Mach Intell 33(8):1548–1560

    Article  Google Scholar 

  • Castellano C, Fortunato S, Loreto V (2009) Statistical physics of social dynamics. Rev Mod Phys 81(2):591

    Article  Google Scholar 

  • Clauset A, Moore C, Newman ME (2008) Hierarchical structure and the prediction of missing links in networks. Nature 453(7191):98–101

    Article  Google Scholar 

  • Costas R, de Rijcke S, Marres N (2017) Beyond the dependencies of altmetrics: conceptualizing heterogeneous couplings between social media and science. altmetrics17 The dependencies of altmetrics

  • Cunningham H (2002a) Gate: a framework and graphical development environment for robust NLP tools and applications. In: Proc. 40th annual meeting of the association for computational linguistics (ACL 2002), pp 168–175

  • Cunningham H (2002b) Gate, a general architecture for text engineering. Comput Humanit 36(2):223–254

    Article  Google Scholar 

  • Díaz C, Mauricio C (2013) Defining and characterizing the concept of internet meme. CES Psicol 6(2):82–104

    Google Scholar 

  • Eysenbach G (2011) Can tweets predict citations? Metrics of social impact based on twitter and correlation with traditional metrics of scientific impact. J Med Internet Res 13(4):e123

    Article  Google Scholar 

  • Finkel JR, Grenager T, Manning C (2005) Incorporating non-local information into information extraction systems by Gibbs sampling. In: Proceedings of the 43rd annual meeting on association for computational linguistics. Association for Computational Linguistics, pp 363–370

  • Garcia-Gasulla D, Ayguadé E, Labarta J, Cortés U (2016) Limitations and alternatives for the evaluation of large-scale link prediction. arXiv preprint arXiv:161100547

  • Haustein S, Bowman TD, Costas R (2015) Interpreting” altmetrics”: viewing acts on social media through the lens of citation and social theories. arXiv preprint arXiv:150205701

  • Huo L, Ma C (2017) Dynamical analysis of rumor spreading model with impulse vaccination and time delay. Phys A Stat Mech Appl 471:653–665

    Article  MathSciNet  Google Scholar 

  • Katz L (1953) A new status index derived from sociometric analysis. Psychometrika 18(1):39–43

    Article  Google Scholar 

  • Kim M, Newth D, Christen P (2014) Trends of news diffusion in social media based on crowd phenomena. In: Proceedings of the 23rd international conference on world wide web. ACM, pp 753–758

  • Kuhn T, Perc M, Helbing D (2014) Inheritance patterns in citation networks reveal scientific memes. Phys Rev X 4(4):041,036

    Google Scholar 

  • Lafferty J, Lebanon G (2005) Diffusion kernels on statistical manifolds. J Mach Learn Res 6(Jan):129–163

    MathSciNet  MATH  Google Scholar 

  • Leskovec J, Backstrom L, Kleinberg J (2009) Meme-tracking and the dynamics of the news cycle. In: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 497–506

  • Ma H, Yang H, King I, Lyu MR (2008a) Learning latent semantic relations from clickthrough data for query suggestion. In: Proceedings of the 17th ACM conference on information and knowledge management. ACM, pp 709–718

  • Ma H, Yang H, Lyu MR, King I (2008b) Mining social networks using heat diffusion processes for marketing candidates selection. In: Proceedings of the 17th ACM conference on information and knowledge management. ACM, pp 233–242

  • Newman ME (2002) Spread of epidemic disease on networks. Phys Rev E 66(1):016,128

    Article  MathSciNet  Google Scholar 

  • Page L, Brin S, Motwani R, Winograd T (1999) The pagerank citation ranking: bringing order to the web. Tech. rep., Stanford InfoLab

  • Priem J, Groth P, Taraborelli D (2012a) The altmetrics collection. PloS one 7(11):e48753

    Article  Google Scholar 

  • Priem J, Piwowar HA, Hemminger BM (2012b) Altmetrics in the wild: using social media to explore scholarly impact. arXiv preprint arXiv:12034745

  • Radicchi F, Fortunato S, Markines B, Vespignani A (2009) Diffusion of scientific credits and the ranking of scientists. Phys Rev E 80(5):056,103

    Article  Google Scholar 

  • Rowlands I, Nicholas D, Russell B, Canty N, Watkinson A (2011) Social media use in the research workflow. Learn Publ 24(3):183–195

    Article  Google Scholar 

  • Sahami M, Heilman TD (2006) A web-based kernel function for measuring the similarity of short text snippets. In: Proceedings of the 15th international conference on world wide web. ACM, pp 377–386

  • Shifman L (2013) Memes in a digital world: reconciling with a conceptual troublemaker. J Comput Mediat Commun 18(3):362–377

    Article  Google Scholar 

  • Spitzberg BH (2014) Toward a model of meme diffusion (m3d). Commun Theory 24(3):311–339

    Article  Google Scholar 

  • Sugimoto CR, Work S, Larivière V, Haustein S (2017) Scholarly use of social media and altmetrics: a review of the literature. J Assoc Inf Sci Technol 68(9):2037–2062

    Article  Google Scholar 

  • Thelwall M (2008) Bibliometrics to webometrics. J Inf Sci 34:605–621

    Article  Google Scholar 

  • Thelwall M, Haustein S, Larivière V, Sugimoto CR (2013) Do altmetrics work? Twitter and ten other social web services. PloS one 8(5):e64,841

    Article  Google Scholar 

  • Timilsina M, Davis B, Taylor M, Hayes C (2017a) Predicting citations from mainstream news, weblogs and discussion forums. In: Proceedings of the international conference on web intelligence. ACM, pp 237–244

  • Timilsina M, Khawaja W, Davis B, Taylor M, Hayes C (2017b) Social impact assessment of scientist from mainstream news and weblogs. Soc Netw Anal Min 7(1):48

    Article  Google Scholar 

  • Timilsina M, Yang H, Rebholz-Schuhmann D (2018) A 2-layered graph based diffusion approach for altmetric analysis. In: 2018 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM). IEEE, pp 463–466

  • Wang L, Hu K, Tang Y (2014) Robustness of link-prediction algorithm based on similarity and application to biological networks. Curr Bioinform 9(3):246–252

    Article  Google Scholar 

  • Warren HR, Raison N, Dasgupta P (2017) The rise of altmetrics. Jama 317(2):131–132

    Article  Google Scholar 

  • Yang H, King I, Lyu MR (2007) Diffusionrank: a possible penicillin for web spamming. In: Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval. ACM, pp 431–438

  • Zahedi Z, Costas R, Wouters P (2014) How well developed are altmetrics? A cross-disciplinary analysis of the presence of alternative metrics in scientific publications. Scientometrics 101(2):1491–1513

    Article  Google Scholar 

  • Zhang S, Wang W, Ford J, Makedon F (2006) Learning from incomplete ratings using non-negative matrix factorization. In: Proceedings of the 2006 SIAM international conference on data mining. SIAM, pp 549–553

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Authors and Affiliations

  1. Insight Centre for Data Analytics, National University of Ireland Galway, Galway, Ireland

    Mohan Timilsina & Mathieu d’Aquin

  2. School of Mathematics, Statistics and Applied Mathematics, National University of Ireland Galway, Galway, Ireland

    Haixuan Yang

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  1. Mohan Timilsina
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  2. Mathieu d’Aquin
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  3. Haixuan Yang
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Correspondence to Mohan Timilsina.

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Timilsina, M., d’Aquin, M. & Yang, H. Heat diffusion approach for scientific impact analysis in social media. Soc. Netw. Anal. Min. 9, 16 (2019). https://doi.org/10.1007/s13278-019-0560-3

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