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Friendship Paradox and Hashtag Embedding in the Instagram Social Network

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ICT Innovations 2019. Big Data Processing and Mining (ICT Innovations 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1110))

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Abstract

Instagram is a social networking platform which gained popularity even faster than most of the other modern online social networks. It is relatively newer and less explored than other social networks, such as Facebook and Twitter. Therefore, we have conducted a research based on a sample data set extracted through the Instagram weekend hashtag project, in order to unveil some of its characteristics. First, we reveal the various forms of friendship paradox present in Instagram, which are often observed in social networks. Then, we conduct a detailed hashtag analysis and provide a method for hashtag representation and recommendation using natural language processing.

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References

  1. https://github.com/nasadigital/diplomska-instagram

  2. Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. Trans. Assoc. Comput. Linguist. 5, 135–146 (2017)

    Article  Google Scholar 

  3. Cohen, R., Havlin, S., Ben-Avraham, D.: Efficient immunization strategies for computer networks and populations. Phys. Rev. Lett. 91(24), 247901 (2003)

    Article  Google Scholar 

  4. Dhingra, B., Zhou, Z., Fitzpatrick, D., Muehl, M., Cohen, W.W.: Tweet2Vec: character-based distributed representations for social media. arXiv preprint arXiv:1605.03481 (2016)

  5. Feld, S.L.: Why your friends have more friends than you do. Am. J. Sociol. 96(6), 1464–1477 (1991)

    Article  Google Scholar 

  6. Ferrara, E., Interdonato, R., Tagarelli, A.: Online popularity and topical interests through the lens of Instagram. In: Proceedings of the 25th ACM Conference on Hypertext and Social Media, pp. 24–34. ACM (2014)

    Google Scholar 

  7. Hampton, K.N., Goulet, L.S., Marlow, C., Rainie, L.: Why most Facebook users get more than they give. Pew Internet Am. Life Proj. 3, 1–40 (2012)

    Google Scholar 

  8. Hodas, N.O., Kooti, F., Lerman, K.: Friendship paradox redux: your friends are more interesting than you. In: Seventh International AAAI Conference on Weblogs and Social Media (2013)

    Google Scholar 

  9. Hu, Y., Manikonda, L., Kambhampati, S.: What we Instagram: a first analysis of Instagram photo content and user types. In: Eighth International AAAI Conference on Weblogs and Social Media (2014)

    Google Scholar 

  10. Jang, J.Y., Han, K., Lee, D.: No reciprocity in liking photos: analyzing like activities in Instagram. In: Proceedings of the 26th ACM Conference on Hypertext & Social Media, pp. 273–282. ACM (2015)

    Google Scholar 

  11. Kumar, R., Novak, J., Tomkins, A.: Structure and evolution of online social networks. In: Yu, P., Han, J., Faloutsos, C. (eds.) Link Mining: Models, Algorithms, and Applications, pp. 337–357. Springer, New York (2010). https://doi.org/10.1007/978-1-4419-6515-8_13

    Chapter  Google Scholar 

  12. Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: International Conference on Machine Learning, pp. 957–966 (2015)

    Google Scholar 

  13. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)

  14. Mislove, A., Marcon, M., Gummadi, K.P., Druschel, P., Bhattacharjee, B.: Measurement and analysis of online social networks. In: Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, pp. 29–42. ACM (2007)

    Google Scholar 

  15. Penni, J.: The future of online social networks (OSN): a measurement analysis using social media tools and application. Telematics Inform. 34(5), 498–517 (2017)

    Article  Google Scholar 

  16. Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)

    Google Scholar 

  17. Rajaraman, A., Ullman, J.D.: Mining of Massive Datasets. Cambridge University Press, New York (2011)

    Book  Google Scholar 

  18. Tagarelli, A., Interdonato, R.: Time-aware analysis and ranking of lurkers in social networks. Soc. Netw. Anal. Min. 5(1), 46 (2015)

    Article  Google Scholar 

  19. Veit, A., Nickel, M., Belongie, S., van der Maaten, L.: Separating self-expression and visual content in hashtag supervision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5919–5927 (2018)

    Google Scholar 

  20. Weston, J., Chopra, S., Adams, K.: #Tagspace: semantic embeddings from hashtags. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1822–1827 (2014)

    Google Scholar 

  21. Zhang, L., Zhao, J., Xu, K.: Who creates trends in online social media: the crowd or opinion leaders? J. Comput. Mediated Commun. 21(1), 1–16 (2015)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje, Skopje, North Macedonia

    David Serafimov, Miroslav Mirchev & Igor Mishkovski

Authors
  1. David Serafimov
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  2. Miroslav Mirchev
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  3. Igor Mishkovski
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Corresponding author

Correspondence to Miroslav Mirchev .

Editor information

Editors and Affiliations

  1. Saints Cyril and Methodius University of Skopje, Skopje, North Macedonia

    Sonja Gievska

  2. Saints Cyril and Methodius University of Skopje, Skopje, North Macedonia

    Gjorgji Madjarov

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Serafimov, D., Mirchev, M., Mishkovski, I. (2019). Friendship Paradox and Hashtag Embedding in the Instagram Social Network. In: Gievska, S., Madjarov, G. (eds) ICT Innovations 2019. Big Data Processing and Mining. ICT Innovations 2019. Communications in Computer and Information Science, vol 1110. Springer, Cham. https://doi.org/10.1007/978-3-030-33110-8_11

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  • DOI: https://doi.org/10.1007/978-3-030-33110-8_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33109-2

  • Online ISBN: 978-3-030-33110-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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