Abstract
Good news should answer the following questions: ‘Who?’, ‘Where?’, ‘When?’, ‘What?’, and possibly ‘Why?’. We present an approach which extracts interesting events from thousands of daily news. We construct a time-varying, three-layer network where the nodes are entities of interest in the news. The temporal aspect of the network answers the ‘When?’ question. The layers are: (1) the co-occurrence of entities which answers the ‘Who?’ or ‘Where?’, (2) the summary layer which answers the ‘What?’, and (3) the sentiment layer which labels the links as ‘good’ or ‘bad’ news. We demonstrate the news network evolution over a period of four years in an interactive web portal.
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References
Albert, R., Barabási, A.L.: Statistical mechanics of complex networks. Rev. Mod. Phys. 74(1), 47 (2002)
Bastian, M., Heymann, S., Jacomy, M.: Gephi: an open source software for exploring and manipulating networks (2009)
Caldarelli, G.: Scale-Free Networks: Complex Webs in Nature and Technology. Oxford University Press, Oxford (2007)
Cattuto, C., Schmitz, C., Baldassarri, A., Servedio, V.D., Loreto, V., Hotho, A., Grahl, M., Stumme, G.: Network properties of folksonomies. AI Commun. 20(4), 245–262 (2007)
Cohen, A.M., Hersh, W.R., Dubay, C., Spackman, K.: Using co-occurrence network structure to extract synonymous gene and protein names from medline abstracts. BMC bioinformatics 6(1), 103 (2005)
De Domenico, M., Porter, M.A., Arenas, A.: MuxViz: a tool for multilayer analysis and visualization of networks. J. Complex Netw. (2014)
Edmonds, P.: Choosing the word most typical in context using a lexical co-occurrence network. In: Proceedings of 35th Annual meeting of ACL, pp. 507–509. Association for Computational Linguistics (1997)
Fagerland, M.W.: t-tests, non-parametric tests, and large studies a paradox of statistical practice? BMC Med. Res. Methodol. 12(78) (2012)
Feldman, R., Sanger, J.: Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge University Press, New York (2006)
Freilich, S., Kreimer, A., Meilijson, I., Gophna, U., Sharan, R., Ruppin, E.: The large-scale organization of the bacterial network of ecological co-occurrence interactions. Nucleic Acids Res. 38(12), 3857–3868 (2010)
Jackson, M.O.: Social and Economic Networks. Princeton University Press, Princeton (2010)
Kralj Novak, P., Grčar, M., Sluban, B., Mozetič, I.: Analysis of financial news with newsStream. Technical report IJS-DP-11965, (2015). arXiv:1508.00027
Piškorec, M., Sluban, B., Šmuc, T.: MultiNets: web-based multilayer network visualization. In: Proceedings of European Conference on ML and KDD. LNCS, vol. 9286, pp. 298–302. Springer (2015)
Popović, M., Štefančić, H., Sluban, B.: Kralj Novak, P., Grčar, M., Puliga, M., Mozetič, I., Zlatić, V.: Extraction of temporal networks from term co-occurrences in online textual sources. PLoS ONE 9(12), e99515 (2014)
Ranco, G., Aleksovski, A., Caldarelli, G., Grčar, M., Mozetič, I.: The effects of Twitter sentiment on stock price returns. PLoS ONE 10(9), e138441 (2015)
Rossi, L., Magnani, M.: Towards effective visual analytics on multiplex and multilayer networks. Chaos, Solitons Fractals 72, 68–76 (2015)
Salton, G.: Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer. Addison-Wesley Longman Publishing Co., Inc, Boston (1989)
Sluban, B., Smailović, J., Battiston, S., Mozetič, I.: Sentiment leaning of influential communities in social networks. Comput. Soc. Netw. 2(9), 1–21 (2015)
Sluban, B., Smailović, J., Mozetič, I.: Understanding financial news with multi-layer network analysis. In: Proceedings of European Conference on Complex Systems, ECCS-14. Springer (2015)
Smailović, J., Kranjc, J., Grčar, M., Žnidaršič, M., Mozetič, I.: Monitoring the Twitter sentiment during the Bulgarian elections. In: Proceedings of IEEE International Conference on Data Science and Advanced Analytics. IEEE (2015)
Su, H.N., Lee, P.C.: Mapping knowledge structure by keyword co-occurrence: a first look at journal papers in technology foresight. Scientometrics 85(1), 65–79 (2010)
Tetlock, P.C., Saar-Tsechansky, M., Macskassy, S.: More than words: quantifying language to measure firms’ fundamentals. J. Finan. 63(3), 1437–1467 (2008)
Welch, B.L.: The generalization of "Student’s" problem when several different population variances are involved. Biometrika 34(1–2), 28–35 (1947)
Zollo, F.: Kralj Novak, P., Del Vicario, M., Bessi, A., Mozetič, I., Scala, A., Caldarelli, G., Quattrociocchi, W.: Emotional dynamics in the age of misinformation. PLoS ONE 10(9), e138740 (2015)
Acknowledgments
This work was supported in part by the European Commission FP7 projects MULTIPLEX (no. 317532) and SIMPOL (no. 610704), the H2020 FET project DOLFINS (no. 640772), and by the Slovenian ARRS programme Knowledge Technologies (no. P2-103).
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Sluban, B., Grčar, M., Mozetič, I. (2016). Temporal Multi-layer Network Construction from Major News Events. In: Cherifi, H., Gonçalves, B., Menezes, R., Sinatra, R. (eds) Complex Networks VII. Studies in Computational Intelligence, vol 644. Springer, Cham. https://doi.org/10.1007/978-3-319-30569-1_3
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DOI: https://doi.org/10.1007/978-3-319-30569-1_3
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