Abstract
It is important to occasionally remember that the World Wide Web (WWW) is the largest information network the world has ever seen. Just about every sphere of human activity has been altered in some way, due to the web. Our understanding of the web has been evolving over the past few decades ever since it was born. In its early days, the web was seen just as an unstructured hypertext document collection. However, over time, we have come to model the web as a global, participatory, socio-cognitive space. One of the consequences of modeling the web as a space rather than as a tool, is the emergence of the concept of Web observatories. These are application programs that are meant to observe and curate data about online phenomena. This paper details the design of a Web observatory called Cogno, that is meant to observe online social cognition. Social cognition refers to the way social discourses lead to the formation of collective worldviews. As part of the design of Cogno, we also propose a computational model for characterizing social cognition. Social media is modeled as a “marketplace of opinions” where different opinions come together to form “narratives” that not only drive the discourse, but may also bring some form of returns to the opinion holders. The problem of characterizing social cognition is defined as breaking down a social discourse into its constituent narratives, and for each narrative, its key opinions, and the key people driving the narrative.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adamic, L.A., Lento, T.M., Adar, E., Ng, P.C.: Information evolution in social networks. In: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, WSDM 2016, pp. 473–482. ACM, New York (2016). https://doi.org/10.1145/2835776.2835827
Bakshy, E., Hofman, J.M., Mason, W.A., Watts, D.J.: Everyone’s an influencer: quantifying influence on twitter. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, WSDM 2011, pp. 65–74. ACM, New York (2011). https://doi.org/10.1145/1935826.1935845
Bhanushali, A., Subbanarasimha, R.P., Srinivasa, S.: Identifying opinion drivers on social media. OTM 2017. LNCS, vol. 10574, pp. 242–253. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69459-7_17
Borge-Holthoefer, J., Perra, N., Gonçalves, B., González-Bailón, S., Arenas, A., Moreno, Y., Vespignani, A.: The dynamics of information-driven coordination phenomena: a transfer entropy analysis. Sci. Adv. 2(4), e1501158 (2016). https://doi.org/10.1126/sciadv.1501158. http://advances.sciencemag.org/content/2/4/e1501158
Ceri, S., Bozzon, A., Brambilla, M., Della Valle, E., Fraternali, P., Quarteroni, S.: Web information retrieval. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39314-3
Cha, M., Haddadi, H., Benevenuto, F., Gummadi, K.P.: Measuring user influence in twitter: the million follower fallacy. In Proceedings of International AAAI Conference on Weblogs and Social, ICWSM 2010 (2010)
Craswell, N., Hawking, D.: Web information retrieval. Information Retrieval: Searching in the 21st Century, pp. 85–101 (2009)
Desikan, P., Srivastava, J., Kumar, V., Tan, P.N.: Hyperlink analysis-techniques & applications. Army High Performance Computing Center Technical Report (2002)
Ferrara, E., JafariAsbagh, M., Varol, O., Qazvinian, V., Menczer, F., Flammini, A.: Clustering memes in social media (2013). CoRR abs/1310.2665. http://arxiv.org/abs/1310.2665
Ferrara, E., Varol, O., Menczer, F., Flammini, A.: Detection of promoted social media campaigns. In: Tenth International AAAI Conference on Web and Social Media, pp. 563–566 (2016)
Muñoz García, O., García-Silva, A., Corcho, O., de la Higuera Hernández, M., Navarro, C.: Identifying topics in social media posts using DBpedia. In: Jean-Dominique, M., Hrasnica, H., Genoux, F. (eds.) Proceedings of the NEM Summit, NEM Initiative, Eurescom - The European Institute for Research and Strategic Studies in Telecommunications - GmbH, Heidelberg, Germany, pp. 81–86, September 2011
Ghosh, S., Sharma, N., Benevenuto, F., Ganguly, N., Gummadi, K.: Cognos: Crowdsourcing search for topic experts in microblogs. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2012, pp. 575–590. ACM, New York (2012). https://doi.org/10.1145/2348283.2348361
Hall, W., et al.: The southampton university web observatory. In: 1st International workshop on Building Web Observatories, ACM Web Science 2013, 1–3 May 2013, April 2013. https://eprints.soton.ac.uk/352287/
Hendler, J., Berners-Lee, T.: From the semantic web to social machines: a research challenge for ai on the world wide web. Artif. Intell. 174(2), 156–161 (2010)
Henzinger, M.R.: Hyperlink analysis for the web. IEEE Internet Comput. 1, 45–50 (2001)
Lewandowski, D.: Web information retrieval. Inf. Wissenschaft Praxis 56(1), 5–12 (2005)
Li, H., Mukherjee, A., Liu, B., Kornfield, R., Emery, S.: Detecting campaign promoters on twitter using markov random fields. In: Proceedings of the 2014 IEEE International Conference on Data Mining, ICDM 2014, pp. 290–299. IEEE Computer Society, Washington, DC (2014). https://doi.org/10.1109/ICDM.2014.59
Madaan, A., Tiropanis, T., Srinivasa, S., Hall, W.: Observlets: empowering analytical observations on web observatory. In: Proceedings of the 25th International Conference Companion on World Wide Web, WWW 2016 Companion, International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, pp. 775–780 (2016). https://doi.org/10.1145/2872518.2890593
McKelvey, K., Menczer, F.: Design and prototyping of a social media observatory. In: Proceedings of the 22nd International Conference on World Wide Web. WWW ’13 Companion, pp. 1351–1358. ACM, New York (2013). https://doi.org/10.1145/2487788.2488174
Mihaila, G.A.: WebSQL: an SQL-like query language for the World Wide Web. University of Toronto (1996)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space (2013). CoRR abs/1301.3781. http://dblp.uni-trier.de/db/journals/corr/corr1301.html#abs-1301-3781
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Burges, C.J.C., Bottou, L., Welling, M., Ghahramani, Z., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 26, pp. 3111–3119. Curran Associates, Inc., New York (2013)
Pal, A., Counts, S.: Identifying topical authorities in microblogs. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, WSDM 2011, pp. 45–54. ACM, New York (2011). https://doi.org/10.1145/1935826.1935843
Park, H.W.: Hyperlink network analysis: a new method for the study of social structure on the web. Connections 25(1), 49–61 (2003)
Qiang, J., Chen, P., Wang, T., Wu, X.: Topic modeling over short texts by incorporating word embeddings (2016). CoRR abs/1609.08496. http://arxiv.org/abs/1609.08496
Simpson, R., Page, K.R., De Roure, D.: Zooniverse: Observing the world’s largest citizen science platform. In: Proceedings of the 23rd International Conference on World Wide Web, WWW 2014 Companion, pp. 1049–1054. ACM, New York (2014). https://doi.org/10.1145/2567948.2579215
Sivaraman, N.K., Srinivasa, S.: Abstractions, expressions and online collectives. In: Proceedings of the ACM Web Science Conference, WebSci 2015, pp. 58:1–58:2. ACM, New York (2015). https://doi.org/10.1145/2786451.2786499
Smart, P.R., Shadbolt, N.R.: Social machines. In: Encyclopedia of Information Science and Technology, 3rd Edn., pp. 6855–6862. IGI Global, Hershey (2015)
Steinskog, A., Therkelsen, J., Gambäck, B.: Twitter topic modeling by tweet aggregation. In: NODALIDA (2017)
Tinati, R., Wang, X., Tiropanis, T., Hall, W.: Building a real-time web observatory. IEEE Internet Comput. 19(6), 36–45 (2015)
Tiropanis, T., Hall, T., Shadbolt, W., De Roure, D., Contractor, N., Hendler, J.: The web science observatory. IEEE Intell. Syst. 28(2), 100–104 (2013). https://doi.org/10.1109/MIS.2013.50
Tiropanis, T., Hall, W., Hendler, J., de Larrinaga, C.: The web observatory: a middle layer for broad data. Big Data 2(3), 129–133 (2014). https://doi.org/10.1089/big.2014.0035
Wagner, C., Liao, V., Pirolli, P., Nelson, L., Strohmaier, M.: It’s not in their tweets: modeling topical expertise of twitter users. In: Proceedings of the 2012 ASE/IEEE International Conference on Social Computing and 2012 ASE/IEEE International Conference on Privacy, Security, Risk and Trust, SOCIALCOM-PASSAT 2012, pp. 91–100. IEEE Computer Society, Washington, DC (2012). https://doi.org/10.1109/SocialCom-PASSAT.2012.30
Wang, X., et al.: Building a web observatory for south Australian government: Supporting an age friendly population. Web Science Conference, June 2015
Xiong, F., Liu, Y.: Opinion formation on social media: an empirical approach. Chaos Interdiscip. J. Nonlinear Sci. 24(1), 013130 (2014). https://doi.org/10.1063/1.4866011
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Srinivasa, S., Subbanarasimha, R.P. (2018). Design of the Cogno Web Observatory for Characterizing Online Social Cognition. In: Mondal, A., Gupta, H., Srivastava, J., Reddy, P., Somayajulu, D. (eds) Big Data Analytics. BDA 2018. Lecture Notes in Computer Science(), vol 11297. Springer, Cham. https://doi.org/10.1007/978-3-030-04780-1_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-04780-1_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04779-5
Online ISBN: 978-3-030-04780-1
eBook Packages: Computer ScienceComputer Science (R0)